SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED INTELLIGENT BUILDING ACCESS ENDPOINT SECURITY MONITORING AND MANAGEMENT

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

Method and Device for Situation Awareness

A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

Roomfinder platform

A method may include receiving, from a client device, a reservation time and image data relating to a desired room; receiving, from a network storage device, facility data for a plurality of rooms; identifying, using an image recognition model, the desired room based on the image data and the facility data; determining an availability of the desired room based on the reservation time and the facility data; generating a first reservation option to reserve the desired room and/or a second reservation option to reserve an alternate room; transmitting, to the client device, the first reservation option and/or the second reservation option; receiving, from the client device, a user selection of the first reservation option and/or the second reservation option; and transmitting, to the network storage device, an instruction to reserve the desired room and/or the alternate room.

СПОСОБ ВНЕСЕНИЯ СРЕДСТВА ДЛЯ РАСТЕНИЙ

Изобретение относится к области сельского хозяйства, в частности к контролю признаков нормального роста растений. Способ внесения удобрения включает выявление рядов растений на участке поля посредством оптического съемочного блока с применением цветовой компоненты и/или инфракрасного съемочного блока с применением инфракрасной компоненты; идентификацию всех рядов растений на снятом участке поля; распознавание рядов растений с внесенным удобрением с применением следующих наборов данных: цветовая компонента выявленных растений, предпочтительно зеленая цветовая компонента, и/или инфракрасная компонента выявленных растений, расстояние между растениями, расстояние между рядами растений, стадия роста растений, географические координаты посева растений, сравнение полученного изображение с тестовой выборкой и внесение удобрения. В качестве тестовой выборки используют ряды растений, на которые не производится внесение удобрений, а внесение удобрения осуществляют на основании разницы между информацией ...

Systems and methods for automated detection of changes in extent of structures using imagery

Processors configured to detect objects and methods of detecting objects

An electronic device, to detect an object, may continuously obtain a driving image and convert the driving image into a top-view image, generate a change value including a horizontal change value based on a steering angle and a vertical change value based on a vehicle speed, compensate the top-view image based on a plurality of lanes to generate first data and compensate the first data based on the change value to generate second data, generate third data based on performing a subtraction operation on a first frame and a second frame of the second data, and a group the third data and extract a grouped object.

Polynomial convolutional neural network with late fan-out

The invention proposes a method of training a convolutional neural network in which, at each convolutional layer, weights for one seed convolutional filter per layer are updated during each training iteration. All other convolutional filters are polynomial transformations of the seed filter, or, alternatively, all response maps are polynomial transformations of the response map generated by the seed filter.

METHOD AND SYSTEM FOR IMPROVING TARGET DETECTION PERFORMANCE THROUGH DYNAMIC LEARNING

The present disclosure provides a method of improving target detection performance through dynamic learning by a target detection application executed by at least one processor of a terminal, including: acquiring (S101) first target object tracking information including a plurality of first feature points for a target object and a plurality of first descriptors for each of the plurality of first feature points; acquiring (S103) a first captured video obtained by capturing the target object; extracting (S105) target object detection information including a plurality of second feature points and a plurality of second descriptors for each of the plurality of second feature points from the acquired first captured video; detecting (S107) the target object by comparing the extracted target object detection information with the first target object tracking information; acquiring (S109) second target object tracking information obtained by updating the first target object tracking information based ...

Устройство для обнаружения признаков асоциального поведения

Изобретение относится к устройствам контроля мест массового скопления людей. Технический результат заключается в повышении надежности обнаружения признаков асоциального поведения в местах скопления людей. Устройство для обнаружения признаков асоциального поведения содержит последовательно соединенные блок регистрации видеоинформации, блок обнаружения объекта и блок определения пространственных координат объекта, выходом подключенный к первому входу блока комплексного анализа, блок обнаружения субъекта, вход которого соединен со вторым выходом блока регистрации видеоинформации, один из выходов через последовательно соединенные блок определения пространственных координат субъекта и блок построения «скелета» субъекта соединен со вторым входом блока комплексного анализа, а другие выходы блока обнаружения субъекта соединены соответственно со входами блоков регистрации направления взгляда субъекта, расчета соотношения «зрачок/радужка» и анализа мимики, выходами соединенных соответственно с третьим ...

Efficient storage and searching of vector datasets

In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a feature vector index, wherein the feature vector index comprises a sparse-array data structure representing a feature space for a set of labeled feature vectors, wherein the set of labeled feature vectors are assigned to a plurality of classes. The processor is to: receive a query corresponding to a target feature vector; access, via the storage device, a first portion of the feature vector index, wherein the first portion of the feature vector index comprises a subset of labeled feature vectors that correspond to a same portion of the feature space as the target feature vector; determine the corresponding class of the target feature vector based on the subset of labeled feature vectors; and provide a response to the query based on the corresponding class.

DUAL DEEP LEARNING ARCHITECTURE FOR MACHINE-LEARNING SYSTEMS

Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Refining element associations for form structure extraction

In implementations of refining element associations for form structure extraction, a computing device implements a structure system to receive estimate data describing estimated associations of elements included in a form and a digital image depicting the form. An image patch is extracted from the digital image, and the image patch depicts a pair of elements of the elements included in the form. The structure system encodes an indication of whether the pair of elements have an association of the estimated associations. An indication is generated that the pair of elements have a particular association based at least partially on the encoded indication, bounding boxes of the pair of elements, and text depicted in the image patch.

Apparatus and method for generating a recording

Determining object mobility parameters using an object sequence

The described system can use semantic images, lidar images, and/or 3D bounding boxes to determine mobility parameters for objects in the semantic image. In some cases, the system can generate virtual points for an object in a semantic image and associate the virtual points with lidar points to form denser point clouds for the object with the denser point clouds being used to estimate the mobility parameters for the object. In certain cases, the system can use semantic images, lidar images, and/or 3D bounding boxes to determine an object sequence for an object where the object sequence can indicate a location of the particular object at different times or the system can use the object sequence to estimate the mobility parameters for the object.

Discrete three-dimensional processor

A discrete three-dimensional (3-D) processor comprises first and second dice. The first die comprises 3-D memory (3D-M) arrays, whereas the second die comprises logic circuits and at least an off-die peripheral-circuit component of the 3D-M array(s). Typical off-die peripheral-circuit component could be an address decoder, a sense amplifier, a programming circuit, a read-voltage generator, a write-voltage generator, a data buffer, or a portion thereof.

Generating embeddings in a multimodal embedding space for cross-lingual digital image retrieval

The present disclosure relates to methods, systems, and non-transitory computer-readable media for retrieving digital images in response to queries. For example, in one or more embodiments, the disclosed systems receive a query comprising text and generates a cross-lingual-multimodal embedding for the text within a multimodal embedding space. The disclosed systems further identifies an image embedding for a digital image that corresponds to (e.g., is relevant to) the text from the query based on an embedding distance between the image embedding and the cross-lingual-multimodal embedding for the text within the multimodal embedding space. Accordingly, the disclosed systems retrieve the digital image associated with the image embedding for display on a client device, such as the client device that submitted the query.

GRAPH CONVOLUTIONAL NETWORKS WITH MOTIF-BASED ATTENTION

Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.

Methods and systems for detection in an industrial Internet of Things data collection environment with intelligent data management for industrial processes including sensors

An apparatus, methods and systems for data collection in an industrial environment are disclosed. A monitoring system can include a data collector coupled to a plurality of sensors to collect data, a data storage structured to store a plurality of data collection management plans, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data and select one of the plurality of data collection management plans, wherein the selected one of the plurality of data collection management plans is selected is at least in part based on a data analysis of received data from the plurality of sensors.

Discrete three-dimensional processor

A discrete three-dimensional (3-D) processor comprises stacked first and second dice. The first die comprises 3-D memory (3D-M) arrays, whereas the second die comprises logic circuits and at least an off-die peripheral-circuit component of the 3D-M array(s). In one preferred embodiment, the first and second dice are vertically stacked. In another preferred embodiment, the first and second dice are face-to-face bonded.

Image recognition method and device

The disclosure provides an image recognition method and an image recognition device. The method includes: acquiring an image and capturing a plurality of feature points in the image; obtaining a capsule network, where the capsule network sequentially includes a convolution layer, a primary capsule layer, a routing capsule layer, and an output layer; inputting the image and the feature points into the convolution layer to generate a plurality of feature vectors; inputting the feature vectors and the feature points into the primary capsule layer to generate a plurality of activity vectors; and generating a recognition result corresponding to the image by the routing capsule layer and the output layer based on the activity vectors.

SYSTEM AND METHOD FOR CLIENT-SIDE PHYSIOLOGICAL CONDITION ESTIMATIONS BASED ON A VIDEO OF AN INDIVIDUAL

The present disclosure pertains to a system for providing client-side physiological condition estimations during a live video session. In some embodiments, the system includes a first client computer system that is caused to: (i) store a neural network on one or more computer-readable storage media of the first client computer system, (ii) obtain a live video stream of an individual via a camera of the first client computer system during a video streaming session between the first client computer system and a second client computer system, (iii) provide, during the video streaming session, video data of the live video stream as input to the neural network to obtain physiological condition information from the neural network, and (iv) provide, during the video streaming session, the physiological condition information for presentation at the second client computer system.

Visually tracked spacial audio

Apparatuses, systems, and techniques to determine head poses of users and provide related audio to said users. A head pose is determined 104 based, at least in part, on camera frame information 102, and an audio signal is generated 110 based upon the determined head pose. Preferably, a translational position of the head pose and the distance in relation to a display screen are determined based on the camera frame information. Preferably there is only a single camera. Preferably the audio is a synthesized binaural signal. Embodiments featuring facial recognition, neural networks, head related transfer functions (HRTFs), computer games, and video conferencing are also disclosed.

Image processing apparatus, control method thereof, and storage medium

An image processing apparatus includes an input unit configured to input image data, a learning unit configured to perform machine learning processing using information contained in the image data input by the input unit, an estimation unit configured to output an estimation result based on the information contained in the image data using a learning model generated by learning of the learning unit, and a determination unit configured to determine whether the image data input by the input unit contains sensitive information, wherein in a case where the determination unit determines that the image data input by the input unit contains the sensitive information, the learning unit does not perform machine learning on at least the sensitive information contained in the image data.

Resource scheduling using machine learning

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

SYSTEMS AND METHODS FOR COMMUNICATION-EFFICIENT MODEL AGGREGATION IN FEDERATED NETWORKS FOR CONNECTED VEHICLE APPLICATIONS

A server for communication-efficient model aggregation in federated networks for connected vehicle applications is provided. The server includes a controller programmed to: obtain contributions of a plurality of vehicles in a federated learning framework; determine weights for local gradients received from the plurality of vehicles based on the contributions; adjust the weights based on a comparison of potential functions for the plurality of vehicles; and aggregate the local gradients based on the adjusted weights to obtain a global model.

System and methods for classifying magnetic resonance imaging (MRI) image characteristics

A method comprising receiving medical imaging data associated with a magnetic resonance imaging (MRI) scan, determining pulse sequence characteristics for the imaging data using a machine learning (ML) model, classifying the characteristics, and updating a database containing the imaging data to include the classified characteristics. Feature extraction may be applied to the imaging data prior to determining the characteristics. The imaging data and characteristics may be displayed to the user via an interface (GUI). A hanging protocol may be determined based on the characteristics and the imaging data displayed according to the hanging protocol. The ML model may be a convolutional neural network (CNN). The characteristics may include one of: whether fat suppression is present, an echo type of the MRI scan, an indication of whether fluid is bright, or whether a contrast is present. A DICOM tag may be generated for the imaging data based on the classification.

A temporal bottleneck attention architecture for video action recognition

This invention classifies actions performed within a video clip, by receiving a video clip for analysis, where the video clip comprises a time sequence of video frames, applies a bottleneck attention mechanism to the frames in the clip to generate a reduced sequence of key-frames, applies a 2 dimensional (2D) convolutional neural network to the sequence of keyframes to obtain a 3D embedding tensor for each keyframe, applies a multi-headed attention mechanism to the 3D embedding tensors to generate a final action context, and apples a classification mechanism to the final action context to obtain a probability for each action class that indicates the likelihood that an action specified by the action class occurred in the video clip.

Cross-domain structural mapping in machine learning processing

A method of using a computing device executing to interrelate two or more corpuses (610, 650) of dissimilar data that includes receiving input data from each of the corpuses of dissimilar data, comprising: computing a pass for each of the input data into two or more encoder-decoder models; obtaining a prediction of an identity mapping for each of different domains of knowledge from each of the encoder-decoder models; computing a distribution distance metric as an output from each of a low-dimensional embedding vector representation from each of the encoder-decoder models; computing a function based on each of the predictions from each of the encoder-decoder models and the distribution distance metrics; and updating the encoder-decoder models. The method may further comprise computing a corresponding reconstruction loss for each of the encoder-decoder models using the respective prediction and the input data from each of the corpuses of data; and extracting a low-dimensional embedding vector ...

Evaluation data structure for evaluating a specific motion pattern and dashboard for setting up the evaluation data structure

An editor application configured for setting up at least one evaluation data structure, each evaluation data structure being configured for evaluating a corresponding specific motion pattern in a sequence of image data structures Each evaluation data structure includes a machine learning (ML) model artifact of an exercise specific ML model configured for evaluating the particular physical exercise. Further, the ML model is trained based on a plurality of sequences of image data structures showing different variants of the specific motion pattern for the particular physical exercise, and for each image data structure, a set of key data elements is provided, a key data element indicating a respective position of a landmark in the image data structure, said training being further based on class labels provided for each image data structure. In addition, the ML model is configured to determine, based on input data comprising key data elements provided for at least one image data structure, ...

METHOD FOR STOCK KEEPING IN A STORE WITH FIXED CAMERAS

One variation of a method for stock keeping in a store includes: accessing an image captured by a fixed camera within the store; retrieving a field of view of the fixed camera; estimating a segment of an inventory structure in the store depicted in the image based on a projection of the field of view onto a planogram of the store; identifying a set of slots within the inventory structure segment; retrieving a product model representing a set of visual characteristics of a product type assigned to a slot, in the set of slots, by the planogram; extracting a constellation of features from the image; if the constellation of features approximates the set of visual characteristics in the product model, detecting presence of a product unit of the product type occupying the inventory structure segment; and representing presence of the product unit, occupying the inventory structure segment, in a realogram.

A temporal bottleneck attention architecture for video action recognition

This invention classifies actions performed within a video clip, by receiving a video clip for analysis, where the video clip comprises a time sequence of video frames, applies a bottleneck attention mechanism to the frames in the clip to generate a reduced sequence of key-frames, applies a 2 dimensional (2D) convolutional neural network to the sequence of keyframes to obtain a 3D embedding tensor for each keyframe, applies a multi-headed attention mechanism to the 3D embedding tensors to generate a final action context, and apples a classification mechanism to the final action context to obtain a probability for each action class that indicates the likelihood that an action specified by the action class occurred in the video clip.

Systems and methods for human mesh recovery

Human mesh model recovery may utilize prior knowledge of the hierarchical structural correlation between different parts of a human body. Such structural correlation may be between a root kinematic chain of the human body and a head or limb kinematic chain of the human body. Shape and/or pose parameters relating to the human mesh model may be determined by first determining the parameters associated with the root kinematic chain and then using those parameters to predict the parameters associated with the head or limb kinematic chain. Such a task can be accomplished using a system comprising one or more processors and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to implement one or more neural networks trained to perform functions related to the task.

Anchor window size and position selection in time series representation learning

A method of using a computing device to determine a window size in variate time series data that includes receiving, by a computing device, variate time series data associated with a machine learning model. The computing device sets a moving window size and a standard deviation for the variate time series data. The computing device further calculates a moving window average for the variate time series data. The computing device additionally calculates a standard deviation across all variate time series data. The computing device sorts the standard deviations calculated in descending order. The computing device further iterates indices for the standard deviations until the indices have been visited by at least one anchor. The computing device iteratively expands each anchor to cover neighbors' anchors which have been visited by previous anchors. The computing device determines a window size based upon the expanded anchors.

Artificial intelligence (AI) models to improve image processing related to item deliveries

Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives image data showing a portion of a delivery location. The computer system determines an artificial intelligence (AI) model associated with the delivery location. The computer system inputs the image data to the AI model. The computer system receives an indication of whether the portion corresponds to a correct drop-off location and causes a presentation about the indication to be provided at a device.

Apparatus and method for reconstructing three-dimensional image

The present invention relates to a three-dimensional reconstructing method of a 2D medical image. A three-dimensional reconstructing device includes: a communicator for receiving sequential 2D images with an arbitrary slice gap; a sliced image generator for generating at least one sliced image positioned between the 2D images based on a feature point of the adjacent 2D images; and a controller for reconstructing the 2D image into a 3D image by use of the generated sliced image and providing the 3D image.

Image and video instance association for an e-commerce applications

A process for linking related images and videos is disclosed. The process can include receiving listing information including the images and the video, processing the images using an image processor to determine one or more image descriptors, processing the video using a video processor to determine video descriptors, comparing the image descriptors to the video descriptors, calculating a similarity value for each of the image descriptors in relation to each of the video descriptors, and linking the videos and the images based upon the calculated similarity value. The method can also include causing the display of a user interface including a video linking user interface element operable to cause playback of a relevant portion of the video linked with the displayed image and an image linking user interface element operable to cause display of a relevant image linked with a portion of the video playback.

Methods and systems of diagnosing machine components using analog sensor data and neural network

Systems and methods for data collection in an industrial environment are disclosed. A system can include a plurality of analog sensors, wherein each of the plurality of analog sensors is operationally coupled to a respective data collection point of a machine component, and generates a respective stream of detection values. A data acquisition and analysis circuit can receive the respective stream of detection values and analyze the respective stream of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition based on an analysis of the respective stream of detection values, wherein the expert system analysis circuit utilizes a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

Image Analysis to Mine Document Information

The present disclosure describes image analysis techniques that identify the source of a document. Once the source of the document is determined, the image analysis may locate one or more anchor fields in the document. The anchor fields may identify one or more additional fields that contain time-sensitive data and/or information. The image analysis performed herein may identify the time-sensitive data and/or information and process the data and/or information to schedule due dates and reminders.

Model constructing method of neural network model, model constructing system, and non-transitory computer readable storage medium

A model constructing method for a neural network model applicable for image recognition processing is disclosed. The model constructing method includes the following operation: updating, by a processor, a plurality of connection variables between a plurality of layers of the neural network model, according to a plurality of inputs and a plurality of outputs of the neural network model. The plurality of outputs represent a plurality of image recognition results. The plurality of connection variables represent a plurality of connection intensities between each two of the plurality of layers.

Identifying information and associated individuals

A hearing aid system for individual identification of a hearing aid system may include a wearable camera, a microphone, and at least one processor. The processor may be programmed to receive a plurality of images captured by the wearable camera; receive audio signals representative of sounds captured by the microphone; and identify a first audio signal, from among the received audio signals, representative of a voice of a first individual. The processor may transcribe and store, in a memory, text corresponding to speech associated with the voice of the first individual and determine whether the first individual is a recognized individual. If the first individual is a recognized individual, the processor may associate an identifier of the first recognized individual with the stored text corresponding to the speech associated with the voice of the first individual.

영상 3D 콘텐츠 데이터의 OBJECT SEGMENTATION 방법

... 본 발명은 일 실시예에 따른 영상 3D 콘텐츠 데이터의 OBJECT SEGMENTATION 방법에 관한 것으로, VR 영상에 CNN 기술을 통해 단안 영상에 대한 사물 정보 차이를 추정;VR 영상의좌우안 이미지를 이용하여 disparity 추출하기 위한 인식 및 분류;를 포함한다.

Discrete three-dimensional processor

A discrete three-dimensional (3-D) processor a plurality of storage-processing units (SPU's), each of which comprises a non-memory circuit and more than one 3-D memory (3D-M) array. The preferred 3-D processor further comprises communicatively coupled first and second dice. The first die comprises the 3D-M arrays and the in-die peripheral-circuit components thereof; whereas, the second die comprises the non-memory circuits and off-die peripheral-circuit components of the 3D-M arrays.

System and method for client-side physiological condition estimations based on a video of an individual

The present disclosure pertains to a system for providing client-side physiological condition estimations during a live video session. In some embodiments, the system includes a first client computer system that is caused to: (i) store a neural network on one or more computer-readable storage media of the first client computer system, (ii) obtain a live video stream of an individual via a camera of the first client computer system during a video streaming session between the first client computer system and a second client computer system, (iii) provide, during the video streaming session, video data of the live video stream as input to the neural network to obtain physiological condition information from the neural network, and (iv) provide, during the video streaming session, the physiological condition information for presentation at the second client computer system.

One-to-many randomizing interference microscope

A computational microscope and a method for its operation are disclosed. In some embodiments, the microscope maps points on a sample to point in an intensity pattern on a one-to-many basis. The microscope utilizes illumination angle coding, polarization coding, amplitude coding, and phase coding to capture more information than prior art computational microscopes. Although the resulting intensity patterns are not human-interpretable images of the sample, they contain more information about the sample, by virtue of the aforementioned coding techniques, than is captured by prior-art microscopes. Machine-learning algorithms, such as neural networks, are used to analyze the intensity patterns and extract useful information, such as cellular events or cell behavior.

CROWDSOURCING INCENTIVE BASED ON SHELF LOCATION

A method for identifying perishable products in a retail store and for automatically generating offers relating to the identified products is provided. The method may include receiving a set of images depicting perishable products of at least one product type displayed on at least one shelving unit in a retail store. The method may also include analyzing the set of images to determine information about a displayed inventory of the perishable products. The method may further include accessing a database storing information about the at least one product type. The method may also include using the information about the displayed inventory and the stored information about the at least one product type to determine at least one offer associated with the at least one product type. The method may further include providing the at least one offer to at least one customer of the retail store.

Systems and methods for machine learning enhanced intelligent building access endpoint security monitoring and management

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

Systems and methods for processing a table of information in a document

A device may receive document image data that includes an image of a document to be digitized. The device may detect, from the document image data, a table of information that is depicted in the image. The device may determine a data extraction score associated with a table image, wherein the data extraction score is associated with using a data conversion technique to convert the table image to digitized table data. The device may perform, based on the data extraction score not satisfying a threshold, a morphological operation on the table image to generate an enhanced table image that corresponds to an enhanced table of information associated with the table of information. The device may process, using the data conversion technique, the enhanced table image to extract the information from the enhanced table. The device may perform an action associated with the extracted information.

Image compression and decompression via reconstruction of lower resolution image data

The technology is directed towards image compression and decompression, including for 3D images. A sender device obtains lower resolution sub-images of an original image, captured from different angles. The sender device uses different neural network candidate level data and groups of candidate sub-images to determine selected neural network level data and sub-images that when combined, reconstruct the original image that satisfies a function of quality loss data, for sending to a receiver device. The receiver device receives the neural network level data, the group of sub-images and the reconstruction data, and reconstructs a three-dimensional representation of the original image using the neural network level and the group of sub-images arranged based on the reconstruction data.

Safety mode toggling by autonomous robots in a facility context

A system and a method are disclosed that cause a robot to traverse along a route based on a minimum distance to be maintained between the autonomous mobile robot and an obstacle corresponding to a first mode. The robot determines that the route cannot be continued without a distance between the robot and a detected obstacle becoming less than the minimum distance, and responsively determines whether the route can be continued without the distance between the robot and the detected obstacle becoming less than a second minimum distance less than the initial minimum distance, the second minimum distance corresponding to a second mode. Responsive to determining that the route can be continued without the distance between the autonomous mobile robot and the detected obstacle becoming less than the second minimum distance, the robot is configured to operate in second mode and continues traversal of the route.

Apparatus and method for identifying and picking object using artificial intelligence algorithm

According to various embodiments of the present invention, an electronic device comprises: a memory including instructions and a training database, which includes data, on at least one object, acquired on the basis of an artificial intelligence algorithm; at least one sensor; and a processor connected to the at least one sensor and the memory, wherein the processor can be configured to execute the instructions in order to acquire data on a designated area including the at least one object by using the at least one sensor, identify location information and positioning information on the at least one object on the basis of the training database, and transmit a control signal for picking the at least one object to a picking tool related to the electronic device on the basis of the identified location information and positioning information.

Determining object mobility parameters using an object sequence

A system can use semantic images, lidar images, and/or 3D bounding boxes to determine mobility parameters for objects in the semantic image. In some cases, the system can generate virtual points for an object in a semantic image and associate the virtual points with lidar points to form denser point clouds for the object. The denser point clouds can be used to estimate the mobility parameters for the object. In certain cases, the system can use semantic images, lidar images, and/or 3D bounding boxes to determine an object sequence for an object. The object sequence can indicate a location of the particular object at different times. The system can use the object sequence to estimate the mobility parameters for the object.

Device and method for detecting clinically important objects in medical images with distance-based decision stratification

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

Discrete Three-Dimensional Processor

A discrete three-dimensional (3-D) processor comprises a plurality of storage-processing units (SPU's), each of the SPU's comprising a non-memory circuit, at least a memory array and at least an off-die peripheral-circuit component thereof. The 3-D processor further comprises first and second dice. The first die comprises the memory arrays, whereas the second die comprises the non-memory circuit and the off-die peripheral-circuit component.

System for access control of image data using semantic data

An image is processed to determine semantic data about what is depicted in that image. The semantic data provides information about location in the image and semantic category of an object depicted within the image. The semantic categories may include “background”, “face”, “text”, “clothing”, “exercise”, and so forth. The semantic data may then be used to facilitate distribution of image data while limiting the information shared. For example, the semantic data may indicate the placement and position of a user exercising in first video data. Using the semantic data, second video data may be produced that obscures or replaces parts of the image that do not depict the user exercising. The second video data may then be used for providing feedback to the user, while preventing disclosure of other people, background, and so forth that are depicted in the first video data.

Image processing method and device, classifier training method, and readable storage medium

An image processing method, an image processing device, a training method and a computer-readable storage medium. The image processing method includes: extracting a characteristic vector in an image to be recognized; based on the characteristic vector of the image to be recognized, acquiring a predicted score value of the image to be recognized; and based on the predicted score value, determining a category of an image information of the image to be recognized; wherein the image to be recognized is a face image, and the image information is a facial expression.

FLOORPLAN GENERATION BASED ON ROOM SCANNING

Various implementations disclosed herein include devices, systems, and methods that generate floorplans and measurements using a three-dimensional (3D) representation of a physical environment generated based on sensor data.

Systems and methods for matching color and appearance of target coatings

A system and method include receiving target image data associated with a target coating. A feature extraction analysis process is applied to the target image data to determine a target image feature. The feature extraction analysis process includes dividing the target image into sub-images which contains a plurality of target pixels. A machine learning model identifies one or more types of flakes present in the target coating using target pixel features.

Recovering material properties with active illumination and camera on a robot manipulator

A method includes identifying a target surface in an environment of a robotic device. The method further includes controlling a moveable component of the robotic device to move along a motion path relative to the target surface, wherein the moveable component comprises a light source and a camera. The method additionally includes receiving a plurality of images from the camera when the moveable component is at a plurality of poses along the motion path and when the light source is illuminating the target surface. The method also includes determining bidirectional reflectance distribution function (BRDF) image data, wherein the BRDF image data comprises the plurality of images converted to angular space with respect to the target surface. The method further includes determining, based on the BRDF image data and by applying at least one pre-trained machine learning model, a material property of the target surface.

Automated detection of code regressions from time-series data

In non-limiting examples of the present disclosure, systems, methods and devices for detecting and classifying service issues associated with a cloud-based service are presented. Operational event data for a plurality of operations associated with the cloud-based application service may be monitored. A statistical-based unsupervised machine learning model may be applied to the operational event data. A subset of the operational event data may be tagged as potentially being associated with a code regression, wherein the subset comprises a time series of operational event data. A neural network may be applied to the time series of operational event data, and the time series of operational event data may be flagged for follow-up if the neural network classifies the time series as relating to a positive code regression category.

Floorplan generation based on room scanning

Various implementations disclosed herein include devices, systems, and methods that generate floorplans and measurements using a three-dimensional (3D) representation of a physical environment generated based on sensor data.

Determining Biomarkers from Histopathology Slide Images

A system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue includes a processor and an electronic network; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: process segmented tile images determine a predicted biomarker presence; and transmit the predicted presence. A non-transitory computer-readable medium includes a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: process segmented tile images; determine a predicted biomarker presence; and transmit the predicted presence. A computer-implemented method includes processing segmented tile images; determining a predicted biomarker presence; and transmitting the predicted presence.

System and method for executing access transactions of documents related to drug discovery

Disclosed is a system for executing access transactions of documents, for example, pertaining to drug discovery. A document and its metainformation are obtained, and value features are extracted from the document based on identification of concepts associated with the document. An importance score of the document is determined based on the value features and the metainformation. A summarized view of the document is constructed based on the value features, the metainformation, the concepts and the importance score. A unique identifier is generated for the document and associated with the summarized view and the concepts of the document. A search query is processed, and the summarized view of the document is retrieved and displayed based on the query. A request for accessing the document is validated, and document access is allowed when the request is validated successfully. The document access transaction may, for example, be facilitated using a blockchain platform.

System for information extraction from form-like documents

The present disclosure is directed to extracting text from form-like documents. In particular, a computing system can obtain an image of a document that contains a plurality of portions of text. The computing system can extract one or more candidate text portions for each field type included in a target schema. The computing system can generate a respective input feature vector for each candidate for the field type. The computing system can generate a respective candidate embedding for the candidate text portion. The computing system can determine a respective score for each candidate text portion for the field type based at least in part on the respective candidate embedding for the candidate text portion. The computing system can assign one or more of the candidate text portions to the field type based on the respective scores.

Polynomial convolutional neural network with early fan-out

The invention proposes a method of training a convolutional neural network in which, at each convolution layer, weights for one seed convolutional filter per layer are updated during each training iteration. All other convolutional filters are polynomial transformations of the seed filter, or, alternatively, all response maps are polynomial transformations of the response map generated by the seed filter.

Question correction method, device, electronic equipment and storage medium for oral calculation questions

The present disclosure provides a question correction method and device for oral calculation questions. The feature vector of the question to be searched is obtained according to the content of token in the stem of each question to be searched, and then the feature vector of each question to be searched is used to search for the target test paper that matches the test paper to be searched in the question bank. For the question to be searched in the form of oral calculation question, a second search is performed in the target test paper based on the feature vector of the question, and the search criterion is the minimum shortest editing distance. If the question type of the matched target question is also an oral calculation question, it is determined that the question to be searched is the oral calculation question to be corrected, then a preset oral calculation engine is used to calculate the oral calculation question to be corrected and the calculation result is output as the answer to ...

Cognitive determination of message suitability

Cognitive determination of whether a message is suitable for sending over a data communications network can include extracting tokens from the message prior to transmitting the message. One or more intended recipients of the message can be determined from the tokens. A machine learning classification model corresponding to the one or more recipients of the message can be selected. The machine learning classification model can be constructed based on tokens extracted from prior messages, which are combined to create a plurality of documents for training the machine learning classification model. The one or more tokens extracted from the message can be classified using the machine learning classification model. An alert message can be generated in response to determining based on the classifying that the message is unsuited for sending.

Intersection region detection and classification for autonomous machine applications

In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.

Zero-footprint image capture by mobile device

A computer-implemented method for image capture by a mobile device, comprising: receiving, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device; identifying a specific frame of the video stream; generating a plurality of hypotheses defining image borders within the specific frame; selecting, by a neural network, a particular hypothesis among the plurality of hypotheses; producing a candidate image by applying the particular hypothesis to the specific frame; determining a value of a quality metric of the candidate image; determining that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream; wherein the image capture application is a zero-footprint application.

Digital histopathology and microdissection

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class ...

PROGRAMMABLE CUSTOMIZATION OF A USER INTERFACE OF AN ELECTRONIC EQUIPMENT

Provided is an electronic equipment offering a plurality of controllable functionalities and that receives information designating a functionality selected amongst this plurality of controllable functionalities. The electronic equipment also receives a plurality of recordings of a same group of at least one user input for this selected functionality. The electronic equipment also applies a trained neural network model on this received plurality of recordings to recognize an input pattern. The electronic equipment also associates this recognized input pattern with this selected functionality, and stores this association in a memory. The electronic equipment also controls this selected functionality based on this stored association.

Systems and methods for providing personalized saliency models

Methods, systems, and computer readable media for providing personalized saliency models, e.g., for use in mixed reality environments, are disclosed herein, comprising: obtaining, from a server, a first saliency model for the characterization of captured images, wherein the first saliency model represents a global saliency model; capturing a first plurality of images by a first device; obtaining information indicative of a reaction of a first user of the first device to the capture of one or more images of the first plurality images; updating the first saliency model based, at least in part, on the obtained information to form a personalized, second saliency model; and transmitting at least a portion of the second saliency model to the server for inclusion into the global saliency model. In some embodiments, a user's personalized (i.e., updated) saliency model may be used to modify one or more characteristics of at least one subsequently captured image.

SGCNN: structural graph convolutional neural network

A computer-implemented method for learning structural relationships between nodes of a graph includes generating a knowledge graph comprising nodes representing a system and applying a graph-based convolutional neural network (GCNN) to the knowledge graph to generate feature vectors describing structural relationships between the nodes. The GCNN comprises: (i) a graph feature compression layer configured to learn subgraphs representing embeddings of the nodes of the knowledge graph into a vector space, (ii) a neighbor nodes aggregation layer configured to derive neighbor node feature vectors for each subgraph and aggregate the neighbor node feature vectors with their corresponding subgraphs to yield aggregated subgraphs, and (iii) a subgraph convolution layer configured to generate the feature vectors based on the aggregated subgraphs. Functional groups of components included in the system may then be identified based on the plurality of feature vectors.

Determining biomarkers from histopathology slide images

A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Non-intrusive image identification

Non-intrusive image identification includes acquiring an image and connectivity data at a network-connected computing node via a data communications network. The image is captured by an image-capturing device remotely located from the computing node. The connectivity data is associated with one or more devices located within a predetermined vicinity of the image-capturing device. Connectivity data can be correlated with electronically stored networking data associated with the one or more devices to determine an identity of one or more device users. The correlating includes electronically searching one or more network-connected sources of networking data and, based on the connectivity data, matching the one or more devices to the one or more device users. Image-tagging data corresponding to one or more selected individual device users is generated, and the image is tagged with the image-tagging data for identifying one or more individuals appearing in the image.

Time-series data uncertainty reduction

Time-series data uncertainty reduction can include generating an initial accumulated signal based on an event classifier prediction score. The event classifier prediction score can be generated by an event predicator based on time-series data and can correspond to a probability that a target event occurs. Signal leakage can be imposed on the initial accumulated signal. Additionally, an alert can be generated in response to determining that the initial accumulated signal is greater than an alert threshold.

Controlling a robot based on free-form natural language input

Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.

DIGITAL HISTOPATHOLOGY AND MICRODISSECTION

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class ...

Method for generating labeling data that describe an image content of images depicting at least one scene, corresponding processing device, vehicle and data storage medium

A method for generating labeling data is disclosed that describes an image content of images depicting at least one scene, wherein in a processing device image data are received from an imaging and a segmentation unit that detects at least one object in the image data. A graphical processing unit generates a respective graphical object marker that marks the at least one detected object and a display control unit displays an overlay of the at least one scene and the at least one object marker. An input reception unit receives a respective user input for each object marker, wherein the respective user input provides the image content of the image region marked by the object marker.

Time-series data uncertainty reduction

Time-series data uncertainty reduction can include generating an initial accumulated signal based on an event classifier prediction score. The event classifier prediction score can be generated by an event predicator based on time-series data and can correspond to a probability that a target event occurs. Signal leakage can be imposed on the initial accumulated signal. Additionally, an alert can be generated in response to determining that the initial accumulated signal is greater than an alert threshold.

Detection of sensitive database information

A computer system performs a scan to identify data items in a database that correspond to one or more of a plurality of specified output classes. The scan includes determining metadata for a portion of the database, and generating a data profile for a set of data items stored in the portion of the database. The generating includes performing a character-based analysis of the set of data items. The scan further includes identifying whether the set of data items corresponds to one of the plurality of specified output classes by utilizing a multi-class neural network classifier trained to perform the identifying using a plurality of features, including features extracted from the metadata and the data profile. The computing system identifies, based on outputs of the multi-class neural network classifier, a particular one of the plurality of specified output classes that corresponds to the set of data items.

Semantic segmentation for stroke classification in inking application

A data processing system for performing a semantic analysis of digital ink stroke data implements obtaining the digital ink stroke data representing handwritten text, drawings, or both; analyzing the digital ink stroke data to extract path signature feature information from the digital ink stroke data; analyzing the path signature feature information using a convolutional neural network (CNN) trained to perform a pixel-level sematic analysis of the digital ink stroke data and to output a pixel segmentation map with semantic prediction information for each pixel of digital ink stroke data; analyzing the pixel segmentation map to generate stroke-level semantic information using a pixel-to-stroke conversion model; and processing the digital ink stroke data based on the stroke-level semantic information.

Systems and methods for removing background noise in an industrial pump environment

Methods and systems for monitoring a plurality of components of a pump in an industrial environment include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors operationally coupled to the pump and communicatively coupled to the data acquisition circuit; a data processing circuit structured to utilize at least one of the plurality of detection values to perform at least one noise processing operation on at least a portion of the plurality of detection values; a signal evaluation circuit structured to determine a pump performance parameter in response to the noise processed plurality portion of the plurality of detection values; and a response circuit structured to perform at least one operation in response to the pump performance parameter.

Adaptive use of video models for holistic video understanding

Systems and techniques are provided for performing holistic video understanding. For example a process can include obtaining a first video and determining, using a machine learning model decision engine, a first machine learning model from a set of machine learning models to use for processing at least a portion of the first video. The first machine learning model can be determined based on one or more characteristics of at least the portion of the first video. The process can include processing at least the portion of the first video using the first machine learning model.

Operating mode determining method and operating mode determining device

Disclosed are an operating mode determining method and an operating mode determining device for determining an operating mode of a clothing treatment apparatus using image information by executing an artificial intelligence (AI) algorithm and/or machine learning algorithm in a 5G environment connected for the Internet of Things. According to an embodiment of the present disclosure, the operating mode determining method includes obtaining first image information obtained by photographing clothing through a camera of the clothing treatment apparatus, obtaining second image information obtained by photographing an entrance and exit of a user wearing the clothing through a camera for monitoring an entrance and exit of a room, and determining an operating mode of the clothing treatment apparatus according to an analysis result of the first image information and the second image information.

Electronic device and method for controlling same

Disclosed is an electronic device including a learning model having been learned according to an artificial intelligence algorithm. An electronic device according to the present disclosure may comprise: an input unit; and a processor which, when a two-dimensional image including at least one object is received through the input unit, acquires first depth information relating to at least one object by applying the two-dimensional image to a first learning model, acquires second depth information relating to the at least one object by applying the first depth information and actually measured depth data of the at least one object to a second learning model, and acquires three-dimensional information relating to the two-dimensional image on the basis of the second depth information, wherein the first depth information is implemented to include depth data according to a type of the at least one object.

Methods and systems for detection in an industrial internet of things data collection environment

Monitoring, systems and methods for data collection in an industrial environment are disclosed. A system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data, and sense a change in operation and determine an aggregate rate of data being collected from the plurality of input channels. If the aggregate rate exceeds a throughput parameter the data analysis circuit alters the data collection to reduce the amount of data collected or, based on the sensed change, modify a collector route.

Smart summarization, indexing, and post-processing for recorded document presentation

Systems and methods for providing summarization, indexing, and post-processing of a recorded document presentation are provided. The system accesses a structured document and recordings associated with a recorded presentation given using the structured document. The system analyzes, using machine-trained models, the structured document, audio and video recordings, and recording of operations performed during the presentation. The analyzing comprises generating a transcript of the audio recording, determining context of components of the structured document, and deriving context from the video recordings and recording of operations. Based on the analyzing, the system segments the recorded presentation into a plurality of segments and generates an index of the plurality of segments that is used for post-processing.

Visual attractiveness scoring system

Systems and methods are provided for receiving a plurality of images corresponding to a listing in an online marketplace, generating a scene type for each image of the plurality of images, and grouping each image into a scene type group of a set of predefined scene types. Each group of images are input into a respective machine learning model specific to the scene type of the group of images to generate a visual score for each image in each group of images, and an attractiveness score is generated for the listing in the online marketplace based on the visual scores for each image in each group of images.

DOMAIN AWARE MEDICAL IMAGE CLASSIFIER INTERPRETATION BY COUNTERFACTUAL IMPACT ANALYSIS

Measure GUI response time

An approach is disclosed that determines an amount of time before a webpage is ready to use by a user by performing various actions. The approach captures a recording of the webpage from an invocation of the webpage for a period of time sufficient to load completely load the webpage with the capturing resulting in sequenced image frames. An AI system provides a loading point in the sequenced image frames based on an analysis of the frames input to the trained AI system. Image diversity and saturation measurements are calculated on consecutive image frames from the sequenced image frames resulting in an image change analysis. Native webpage events and times are detected from webpage characteristics gathered from the captured digital recording. The amount of time is then calculated based on the loading point from the AI system, the image change analysis; and the webpage events and their corresponding times.

System, methods and apparatus for modifying a data collection trajectory for centrifuges

Systems, methods and apparatus for modifying a data collection trajectory for centrifuges are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of centrifuge types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a centrifuge performance parameter. A response circuit may initiate an action in response to the centrifuge performance parameter.

Techniques for detecting and classifying relevant changes

In various example embodiments, relevant changes between 3D models of a scene are detected and classified by transforming the 3D models into point clouds and applying a deep learning model to the point clouds. The model may employ a Siamese arrangement of sparse lattice networks each including a number of modified BCLs. The sparse lattice networks may each take a point cloud as input and extract features in 3D space to provide a primary output with features in 3D space and an intermediate output with features in lattice space. The intermediate output from both sparse lattice networks may be compared using a lattice convolution layer. The results may be projected into the 3D space of the point clouds using a slice process and concatenated to the primary io outputs of the sparse lattice networks. Each concatenated output may be subject to a convolutional network to detect and classify relevant changes.

Systems and methods for enabling user selection of components for data collection in an industrial environment

Systems and methods for data collection in an industrial environment are disclosed. An expert graphical user interface showing representations of components of an industrial machine to which sensors are attach is disclosed. The user interface may enable a user to select at least one of the components resulting in a search of a database of industrial machine failure modes for modes that correspond to the selected component. The corresponding failure mode may be presented to the user. The selection of the component may cause a controller to reference and implement a data collection template for configuring the system to automatically collect data from sensors associated with the selected component to detect at least one of the corresponding failure modes.

Methods and systems for augmented reality room identification based on room-object profile data

An illustrative room identification system performs augmented reality room identification based on free-hand image capture and room-object profile data. The room identification system obtains a set of reference room-object profiles for a set of rooms associated with an experience presented by an augmented reality presentation device. The room identification system generates a live room-object profile based on an image captured by the presentation device during the augmented reality experience, the image depicting a particular room of the set of rooms. Based on the live room-object profile and the set of reference room-object profiles, the room identification system determines that the presentation device is located in the particular room of the set of rooms and provides, to the presentation device, an indication of the determination that the presentation device is located in the particular room. Corresponding methods and systems are also disclosed.

Determining distribution and/or sorting information for the automated distribution and/or sorting of a consignment

A method performed by at least one apparatus is disclosed in which image data is obtained that represents an image of a surface of a consignment captured by an image sensor. At least partially on the basis of the image data, metadata associated with the image data is determined. The metadata represent a plurality of image features of the image represented by the image data. Each image feature of the image features represented by the metadata is an image component of the image represented by the image data. Distribution and/or sorting information is determined for the automated distribution and/or sorting of the consignment at least partially on the basis of the image features represented by the metadata.

Object identification method and device and non-transitory computer-readable storage medium

The disclosure relates to an object identification method and device as well as a non-transitory computer-readable storage medium. The object identification method includes: acquiring a first object image, and generating a first identification result group according to the first object image, wherein the first identification result group includes one or more first identification results arranged in order of confidence from high to low; acquiring a second object image, and generating a second identification result group based on the second object image, wherein the second identification result group includes one or more second identification results arranged in order of confidence from high to low; and determining whether the first object image and the second object image correspond to the same object to be identified according to the first identification result group and the second identification result group.

Gesture-based call center agent state change control

Users of a networked communication device may omit updating their availability state, and that of an associated communication device, if the state change is sudden or believed to be of sufficiently short duration. By quickly changing their state, a routing component may more accurately know whether or not a communication routed to the communication device will or will not be answered. By utilizing a camera and gesture processing logic, a user may naturally and quickly update their state without the need to navigate menus or select options with manual, tactile interactions with the communication device.

IMAGE PRE-PROCESSING DEVICE CONFIGURED TO PRE-PROCESS IMAGE DATA BEFORE OBJECT LOCALIZATION AND/OR CLASSIFICATION

The invention concerns an image pre-processing device comprising: - a features detector (12); - a virtual static image generator (14); - a determining module (16); - a scoring module (18); - a region of interest selection module (30); and further comprises: - a features local change detector (20); - a virtual dynamic image generator (22); - an integral image generator (24); - a dynamic region determination module (26); - a filtering module (28) configured to provide to said scoring module only the region proposals of a current frame comprising a number of local changes higher than a predetermined threshold, said region of interest selection module further comprising : - an inter-frame region of interest filter (34); - an update module (38) configured to update a list of regions of interest associated with the whole image sequence.

SYSTEMS AND METHODS FOR AUTOMATED DOCUMENT IMAGE ORIENTATION CORRECTION

Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Systems and methods for machine learning enhanced intelligent building access endpoint security monitoring and management

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

Data collation apparatus

A data collation apparatus has: a collation unit which calculates a similarity between input data measured by a measurement apparatus and each of registered data; and a calculation unit which adjusts, if a similarity of first registered data of which similarity with the input data is highest is lower than a first threshold, and a difference between the similarities of the first registered data and second registered data of which similarity with the input data is second highest is smaller than a predetermined value, or if a degree of separation to indicate a distance between the similarity of the first registered data and a similarity distribution of a group of registered data items of which similarities with the input data are second highest or lower is smaller than a predetermined value, the parameters of the measurement apparatus, and instructs the measurement apparatus to remeasure the measurement subject.

Stent localization in 3d cardiac images

Systems and methods are described for automatically identifying coronary stents within a 3D cardiac image. Based on feature analysis on a cardiac image, coronary stents can be detected by filtering the cardiac images for stent candidates, applying a score based on factors related to coronary stents and applying a threshold. Once coronary stents are identified, in-stent restenosis and stent fractures can be further detected.

Object recognition system with database pruning and querying

A database for object recognition is generated by performing at least one of intra-object pruning and inter-object pruning, as well as keypoint clustering and selection. Intra-object pruning removes similar and redundant keypoints within an object and different views of the same object, and may be used to generate and associate a significance value, such as a weight, with respect to remaining keypoint descriptors. Inter-object pruning retains the most informative set of descriptors across different objects, by characterizing the discriminability of the keypoint descriptors for all of the objects and removing keypoint descriptors with a discriminability that is less than a threshold. Additionally, a mobile platform may download a geographically relevant portion of the database and perform object recognition by extracting features from the query image and using determined confidence levels for each query feature during outlier removal.

Method and system for assessing data classification quality

Production data classified from a data source, such as a plurality of handprinted forms, is compared to provisional truth data independently classified from the same data source for constructing master truth data. The production data is compared to the master truth data for evaluating the quality with which the production data was classified.

Apparatus and method for recognizing identifier of vehicle

The present invention detects a candidate ROI group associated with character strings/figure strings on the basis of a result acquired through prior learning of various types of license plates, verifies the interested region candidate group detected by using at least one condition of five predetermined conditions, and determines an MBR region in the selected ROI region from the verified interested region candidate group by considering a ratio between the height and width of the ROI region to recognize the license plate for the automobile. According to the present invention, it is possible to automatically detect the location of the license plate regardless of various types of license plate specifications defined for each of countries.

Real-Time Face Tracking in a Digital Image Acquisition Device

A database includes an identifier and associated parameters for each of a number of faces to be recognized. A new acquired image from an image stream is received potentially including one or more face regions. Face detection is applied to at least a portion of the acquired image to provide a set of candidate face regions each having a given size and a respective location. Using the database, face recognition is selectively applied to at least one of the candidate face regions to provide an identifier for a face recognized in a candidate face region. A portion of the image is stored including the recognized face in association with at least one image of the image stream.

Real-Time Face Tracking in a Digital Image Acquisition Device

An image processing apparatus for tracking faces in an image stream iteratively receives an acquired image from the image stream including one or more face regions. The acquired image is sub-sampled at a specified resolution to provide a sub-sampled image. An integral image is then calculated for a least a portion of the sub-sampled image. Fixed size face detection is applied to at least a portion of the integral image to provide a set of candidate face regions. Responsive to the set of candidate face regions produced and any previously detected candidate face regions, the resolution is adjusted for sub-sampling a subsequent acquired image.

Method and System for Sensing Objects in a Scene Using Transducers Arrays and in Coherent Wideband Ultrasound Pulses

Pulses are transmitted into a scene by an array of the transducers, wherein each pulse has wideband ultrasound frequency, wherein the pulses are transmitted simultaneously, and wherein a pattern of the wideband ultrasound frequencies in each pulse is unique with respect to the patterns of each other pulse. The pulses as received when the pulse are reflected by the scene and objects in the scene. Each received pulse is sampled and decomposed using a Fourier transform to produce frequency coefficients, which are stacked to produce a linear system modeling a reflectivity of the scene and the objects. Then, a recovery method is applied to the linear system to recover the reflectivity of the scene and the objects.

Method and apparatus for compressed sensing

Method and apparatus for compressed sensing yields acceptable quality reconstructions of an object from reduced numbers of measurements. A component x of a signal or image is represented as a vector having m entries. Measurements y, comprising a vector with n entries, where n is less than m, are made. An approximate reconstruction of the m-vector x is made from y. Special measurement matrices allow measurements y=Ax+z, where y is the measured m-vector, x the desired n-vector and z an m-vector representing noise. “A” is an n by m matrix, i.e. an array with fewer rows than columns. “A” enables delivery of an approximate reconstruction, x # ′ of x. An embodiment discloses approximate reconstruction of x from the reduced-dimensionality measurement y. Given y, and the matrix A, approximate reconstruction x # of x is possible. This embodiment is driven by the goal of promoting the approximate sparsity of x # .

Method and system for determining whether a drug will be effective on a patient with a disease

A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Digital image analysis utilizing multiple human labels

Systems and methods for implementing a multi-label image recognition framework for classifying digital images are provided. The provided multi-label image recognition framework utilizes an iterative, multiple analysis path approach to model training and image classification tasks. A first iteration of the multi-label image recognition framework generates confidence maps for each label, which are shared by the multiple analysis paths to update the confidence maps in subsequent iterations. The provided multi-label image recognition framework permits model training and image classification tasks to be performed more accurately than conventional single-label image recognition frameworks.

Data clustering

A clustering procedure for grouping a set of images is selected from amongst plural clustering procedures. A predetermined categorization of objects such as images is input, and image features are extracted from each image in the set of images. A comparison measure is determined, by which to compare respective features of the set of images. Respective features between the images in the set of images are compared, based on the comparison measure, and a group of measures representing the differences between features of respective images is output. The plural clustering procedures are applied to the set of images to cluster the images based in part on the calculated group of measures. A clustering quality score is generated for each clustering procedure, based on the clusters created by the clustering procedure and the predetermined categorization of images. The clustering procedure with a high clustering quality score is selected.

Collapsed gibbs sampler for sparse topic models and discrete matrix factorization

In an inference system for organizing a corpus of objects, feature representations are generated comprising distributions over a set of features corresponding to the objects. A topic model defining a set of topics is inferred by performing latent Dirichlet allocation (LDA) with an Indian Buffet Process (IBP) compound Dirichlet prior probability distribution. The inference is performed using a collapsed Gibbs sampling algorithm by iteratively sampling (1) topic allocation variables of the LDA and (2) binary activation variables of the IBP compound Dirichlet prior In some embodiments the inference is configured such that each inferred topic model is a clean topic model with topics defined as distributions over sub-sets of the set of features selected by the prior. In some embodiments the inference is configured such that the inferred topic model associates a focused sub-set of the set of topics to each object of the training corpus.

Systems and methods to improve feature generation in object recognition

Present embodiments contemplate systems, apparatus, and methods to improve feature generation for object recognition. Particularly, present embodiments contemplate excluding and/or modifying portions of images corresponding to dispersed pixel distributions. By excluding and/or modifying these regions within the feature generation process, fewer unfavorable features are generated and computation resources may be more efficiently employed.

Automatic recognition of images

Presented is a method of automatically performing an action, based on graphical input. The method comprises: receiving, for a user, an input image; comparing the input image with the contents of a user-customized database comprising a plurality of records, each record representing a predefined class of image, wherein the user has previously associated records in the database with respective specified actions; attempting to recognize the image, based on the similarity of the input image to one of the predefined classes of image represented in the user-customised database; and if the image is recognized, performing the action previously associated by the user with the class. Also presented is apparatus for recognizing an image and a method of constructing a user-customized database.

Validation analysis of human target

Technology for testing a target recognition, analysis, and tracking system is provided. A searchable repository of recorded and synthesized depth clips and associated ground truth tracking data is provided. Data in the repository is used by one or more processing devices each including at least one instance of a target recognition, analysis, and tracking pipeline to analyze performance of the tracking pipeline. An analysis engine provides at least a subset of the searchable set responsive to a request to test the pipeline and receives tracking data output from the pipeline on the at least subset of the searchable set. A report generator outputs an analysis of the tracking data relative to the ground truth in the at least subset to provide an output of the error relative to the ground truth.

Object detection in crowded scenes

Methods and systems are provided for object detection. A method includes automatically collecting a set of training data images from a plurality of images. The method further includes generating occluded images. The method also includes storing in a memory the generated occluded images as part of the set of training data images, and training an object detector using the set of training data images stored in the memory. The method additionally includes detecting an object using the object detector, the object detector detecting the object based on the set of training data images stored in the memory.

Classification of microwave scattering data

Example embodiments presented herein relate to solutions for analyzing and/or classifying microwave scattering data. The analyzing and/or classifying may be utilized for estimating an internal condition in an enclosed volume.

Method and apparatus for machine error detection by combining multiple sensor inputs

One of the most significant safety concerns in the automation of extracorporeal blood treatments such as dialysis is the risk of blood leakage. Extracorporeal blood treatment systems draw blood at such a high rate that a loss of integrity in the blood circuit can be serious. There are a number of mechanisms for detecting and preventing leaks, but none is perfect. This tends to limit the use of such equipment in unsupervised settings, such as the home will be limited. Some leak detection schemes can be made sensitive enough to detect the barest of leaks, but when this is done, they result in too many false positives. The invention combines information from multiple inputs to enhance sensitivity in leak detection and reduce the problem of false positives.

Method and apparatus for evaluating human pose recognition technology

A method and apparatus for evaluating a human pose recognition technology, where this method comprises: a loading step of loading a pose recognition module with the human pose recognition technology to be evaluated; a pose recognizing step of recognizing a human pose for each test image in a test image set using the pose recognition module to obtain pose data for each test image; a pose classifying step of classifying the pose data for each test image in terms of a predetermined pose category set to obtain a pose category value for each test image; a pose classification evaluating step of calculating a pose classification accuracy rate by comparing the pose category value for each test image with a pose category true value; and a pose recognition comprehensive evaluating step of calculating a comprehensive evaluation score for the human pose recognition technology according to the pose classification accuracy rate.

Method for Implementing a High-Level Image Representation for Image Analysis

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high-level visual tasks, such low-level image representations are potentially not enough. The present invention provides a high-level image representation where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on this representation, superior performances on high-level visual recognition tasks are achieved with relatively classifiers such as logistic regression and linear SVM classifiers.

Automated prostate tissue referencing for cancer detection and diagnosis

This application provides to a method for identifying one or more prostate tissue samples in a database that are closest to a test prostate sample, which can be used to aid pathologists when examining prostate tissues to attain reliable and consistent diagnoses of prostate cancer. Also provided are databases and computer algorithms that can be used with such methods.

Method for automatically seeding previously-classified images among images of objects of interest from a specimen

A computer-assisted method of classifying cytological samples, includes using a processor to analyze images of cytological samples and identify cytological objects of interest within the sample images, wherein the processor (i) displays images of identified cytological objects of interest from the sample images to a reviewer, (ii) accesses a database of images of previously classified cytological objects, and (iii) displays to the reviewer, interspersed with the displayed images of the identified objects of interest from the sample images, one or more images obtained from the database of images of previously-classified objects.

Analyzing the expression of biomarkers in cells with clusters

A data set of cell profile data is stored. The cell profile data includes multiplexed biometric image data describing the expression of a plurality of biomarkers. Cell profile data is generated from tissue samples drawn from a cohort of patients having an assessment related to the commonality. Multiple sets of clusters of similar cells are generated from the data set; the proportion of cells in each cluster is examined for an association with a diagnosis, a prognosis, or a response; and a predictive set of clusters is selected based on model performance. One predictive set of clusters is selected based on a comparison of the performance of at least one model of the plurality of sets of clusters. Display techniques that aid in understanding the characteristics of a cluster are disclosed.

Image-based popularity prediction

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

System and method for auto-commissioning an intelligent video system

An auto-commissioning system provides automatic parameter selection for an intelligent video system based on target video provided by the intelligent video system. The auto-commissioning system extracts visual feature descriptors from the target video and provides the one or more visual feature descriptors associated with the received target video to an parameter database that is comprised of a plurality of entries, each entry including a set of one or more stored visual feature descriptors and associated parameters tailored for the set of stored visual feature descriptors. A search of the parameter database locates one or more best matches between the extracted visual feature descriptors and the stored visual feature descriptors. The parameters associated with the best matches are returned as part of the search and used to commission the intelligent video system.

Skin Color Detection And Adjustment In An Image

A preferred skin tone region is determined within a luminance-chrominance color space for each of a plurality of luminance values. Additional preferred skin tone regions are determined by interpolation in view of the initially determined preferred skin tone regions. An ellipsoid skin-color model is generated in the luminance-chrominance color space based on the preferred skin tone regions. The ellipsoid skin-color is used to detect a skin color pixel in an image and adjust one or more color values for the skin color pixel.

Detection of objects in digital images

A method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A given color channel of the image is extracted. At least one blob that stands out from a background of the given color channel is identified. One or more features are extracted from the blob. The one or more features are provided to a plurality of pre-learned object models each including a set of pre-defined features associated with a pre-defined blob type. The one or more features are compared to the set of pre-defined features. The blob is determined to be of a type that substantially matches a pre-defined blob type associated with one of the pre-learned object models. At least a location of an object is visually indicated within the image that corresponds to the blob.

Using relevance feedback in face recognition

Images are searched to locate faces that are the same as a query face. Images that include a face that is the same as the query face may be presented to a user as search result images. Images also may be sorted by the faces included in the images and presented to the user as sorted search result images. The user may provide explicit or implicit feedback regarding the search result images. Additional feedback may be inferred regarding the search result images based on the user-provided feedback, and the results may be updated based on the user-provided and inferred feedback.

Methods and apparatuses for convolutive blind source separation

Methods and apparatuses for convolutive blind source separation are described. Each of a plurality of input signals is transformed into frequency domain. Values of coefficients of unmixing filter corresponding to frequency bins are calculated by performing a gradient descent process on a cost function at least dependent on the coefficients of the unmixing filters. In each iteration of the gradient descent process, gradient terms for calculating the values of the same coefficient of the unmixing filters are adjusted to improve smoothness of gradient terms across the frequency bins. With respect to each of the frequency bins, source signals are estimated by filtering the transformed input signals through the respective unmixing filter configured with the calculated values of the coefficients. The estimated source signals on the respective frequency bins are transformed into time domain. The cost function is adapted to evaluate decorrelation between the estimated source signals.

Method and apparatus of compiling image database for three-dimensional object recognition

A method of compiling an image database for a three-dimensional object recognition including the steps of: when a plurality of images each showing an object from different viewpoint are inputted, extracting local features from each of the images, and expressing the local features using feature vectors; forming sets of the feature vectors, each set representing a same part of the object from a series of the viewpoints, and generating subspaces, each subspace representing a characteristic of each set; and storing each subspace to the image database with an identifier of the object to perform a recognition process that is realized by the steps of: when at least one image of an object is given as a query, extracting query feature vectors; determining the subspace most similar to each query feature vector; and executing a counting process to the identifiers to retrieve an object most similar to the query.

Computer tool with sparse representation

Computer tool characterised in that it includes: a working memory to receive input data, an auxiliary memory to store several approximation dictionaries, a reducer arranged for the iterative calculation of the approximation data of the input data, where the successive iterations are of the “matching pursuit” type, a selector arranged to choose an approximation dictionary in the auxiliary memory based on a selection rule based on at least some of the preceding iterations, in which the reducer calls the selector in each iteration, and uses the dictionary thus selected in the adaptive matching.

Re-ranking item recommendations based on image feature data

An apparatus and method to adjust item recommendations are disclosed herein. A first image attribute of a query image is compared to a second image attribute of each of a plurality of inventory images of a plurality of inventory items to identify the inventory items similar to the query image. Item recommendations comprising the identified inventory items in a first listing order are provided for display at a remote device. A second listing order of the identified inventory items is determined based on a user preference for a particular one of the identified inventory items. At least the second listing order is provided to the remote device for re-display of the item recommendations in accordance with the second listing order.

Region segmented image data creating system and feature extracting system for histopathological images

A region segmented image data creating system for histopathological images is provided. The region segmented image data creating system is capable of creating region segmented image data required to generating a region segmented image. A first bi-level image data creating section 12 creates first bi-level image data, in which nucleus regions can be discriminated from other regions, from histopathological image data. A second bi-level image data creating section 14 creates second bi-level image data, in which a background regions can be discriminated from other regions, from the histopathological image data. A three-level image data creating section 15 clarifies cytoplasm regions by computing a negative logical addition of the first bi-level image data and the second bi-level image data, and to create three-level image data as the region segmented image data.

Image processing learning device, image processing learning method, and image processing learning program

Disclosed is a technology with which face direction estimation processing and face detection processing can be learned simultaneously and with high precision without incurring significant costs. The image processing learning device comprises: a face direction identification unit that identifies whether a face direction is already known; a position conversion unit that converts information regarding the face direction to a position on a manifold, when already known; a position estimation unit that estimates the position on the manifold, when unknown; a face identification unit that identifies whether an object is already known to be a face/not a face; a first update quantity calculation unit that calculates the update quantity according to whether the object is a face/not a face from the distance between the position on the manifold and the position in space, when already known; a second update quantity calculation unit that calculates the update quantity so as to be closer when the distance between the position on the manifold and the position in space is close, and further when far, when unknown; and a parameter update unit that updates parameters. The image processing learning device comprises: a face direction identification unit that identifies whether a face direction is already known; a position conversion unit that converts information regarding the face direction to a position on a manifold, when already known; a position estimation unit that estimates the position on the manifold, when unknown; a face identification unit that identifies whether an object is already known to be a face/not a face; a first update quantity calculation unit that calculates the update quantity according to whether the object is a face/not a face from the distance between the position on the manifold and the position in space, when already known; a second update quantity calculation unit that calculates the update quantity so as to be closer when the distance between the position ...

Method of building classifiers for real-time classification of neurological states

A method of building binary classifiers for classification of brain electrical activity data into one or more neurological classes is described. The method comprises the steps of extracting quantitative features from the brain electrical activity data, and reducing the pool of extracted features into a computationally manageable and statistically relevant set of features which can then be used for designing one or more classifiers.

Generating and updating event-based playback experiences

Exemplary methods, apparatus, and systems are disclosed for capturing, organizing, sharing, and/or displaying media. For example, using embodiments of the disclosed technology, a unified playback and browsing experience for a collection of media can be created automatically. For instance, heuristics and metadata can be used to assemble and add narratives to the media data. Furthermore, this representation of media can recompose itself dynamically as more media is added to the collection. While a collection may use a single user's content, sometimes media that is desirable to include in the collection is captured by friends and/or others at the same event. In certain embodiments, media content related to the event can be automatically collected and shared among selected groups. Further, in some embodiments, new media can be automatically incorporated into a media collection associated with the event, and the playback experience dynamically updated.

Spectral image dimensionality reduction system and method

Methods for reducing dimensionality of hyperspectral image data having a number of spatial pixels, each associated with a number of spectral dimensions, include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the sets of coefficients, and either a maximum error value or a maximum data size. The methods also include calculating, using a processor, a first set of errors for each pixel associated with the set of basis vectors, and one or more additional sets of errors for each pixel associated with one or more subsets of the set of basis vectors. Utilizing such errors calculations, an optimum size of the set of basis vectors may be ascertained, allowing for either a minimum amount of error within the maximum data size, or a minimum data size within the maximum error value.

Method and system for generating intrinsic images using a single reflectance technique

An automated, computerized method is provided for processing an image. The method includes the steps of providing an image file depicting an image, in a computer memory, identifying a dominant region of single reflectance in the image and segregating the image into intrinsic images as a function of the dominant region of single reflectance.

Face recognition using multilayered discriminant analysis

Face recognition using multilayered discriminant analysis includes systems and methods applying an initial linear discriminate analysis to a database of face images in a more-or less conventional manner. Initial fuzzy logic then is applied to the results of the initial linear discriminate analysis to produce a subset of the database of face images. Thereafter, a subsequent linear discriminate analysis is applied to the subset of the database of face images and subsequent fuzzy logic is applied to the results of the subsequent linear discriminate analysis to produce a further subset of the subset of the database of face images. The application of the subsequent linear discriminate analysis and application of the subsequent fuzzy logic may be repeated until the further subset contains only one, or zero, face images.

Optical Pattern Recognition Technique

Disclosed is a distortion invariant system, method and computer readable medium for detecting the presence of one or more predefined targets in an input image. The input image and a synthetic discriminant function (SDF) reference image are correlated in a shift phase-encoded fringe-adjusted joint transform correlation (SPFJTC) correlator yielding a correlation output. A peak-to-clutter ratio (PCR) is determined for the correlation output and compared to a threshold value. A predefined target is present in the input image when the PCR is greater than or equal to the threshold value.

Image Classification

The present disclosure introduces a method and an apparatus for classifying images. Classification image features of an image for classification are extracted. Based on a similarity relationship between each classification image feature and one or more visual words in a pre-generated visual dictionary, each classification image feature is quantified by multiple visual words in the visual dictionary and a similarity coefficient between each classification image feature and each of the visual words is determined. Based on the similarity coefficient of each visual word that corresponds to different classification image features, a weight of each visual word is determined to establish a classification visual word histogram of the image for classification. The classification visual word histogram is input into an image classifier that is trained by sample visual word histograms arising from multiple sample images. An output result is used to determine a classification of the image for classification.

Smartphone-Based Methods and Systems

Arrangements involving portable devices (e.g., smartphones and tablet computers) are disclosed. One arrangement enables a content creator to select software with which that creator's content should be rendered—assuring continuity between artistic intention and delivery. Another utilizes a device camera to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some technologies concern improvements to the user interfaces associated with such devices. Others involve use of these devices in connection with shopping, text entry, sign language interpretation, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern use of linked data in portable devices—some of which exploit GPU capabilities. Still other technologies concern computational photography. A great variety of other features and arrangements are also detailed.

Image concealing via efficient feature selection

A novel technique for unsupervised feature selection is disclosed. The disclosed methods include automatically selecting a subset of a feature of an image. Additionally, the selection of the subset of features may be incorporated with a congealing algorithm, such as a least-square-based congealing algorithm. By selecting a subset of the feature representation of an image, redundant and/or irrelevant features may be reduced or removed, and the efficiency and accuracy of least-square-based congealing may be improved.

Method for closed-loop controlling a laser processing operation and laser material processing head using the same

The present invention relates to a method for closed-loop controlling a processing operation of a workpiece, comprising the steps of: (a) recording a pixel image at an initial time point of an interaction zone by means of a camera, wherein the workpiece is processed using an actuator having an initial actuator value; (b) converting the pixel image into a pixel vector; (c) representing the pixel vector by a sum of predetermined pixel mappings each multiplied by a corresponding feature value; (d) classifying the set of feature values on the basis of learned feature values into at least two classes of a group of classes comprising a first class of a too high actuator value, a second class of a sufficient actuator value and a third class of a too low actuator value at the initial time point; (e) performing a control step for adapting the actuator value by minimizing the error e t between a quality indicator y e and a desired value; and (f) repeating the steps (a) to (e) for further time points to perform a closed-loop controlled processing operation.

Image segmentation based on approximation of segmentation similarity

A system and a method for image segmentation use segmentation maps of one or more similar images as a basis for the segmentation. The method includes generating an image signature for an input image to be segmented and identifying at least one similar image from a set of images, based on the image signature of the input image and image signatures of images in the set of images. The similarity may be computed after first projecting the image signatures into a feature space where similarity is more likely to agree with segmentation map similarity. The input image is segmented, based on the segmentation map of one or more of the at least one identified similar images.

Interactive and automated tissue image analysis with global training database and variable-abstraction processing in cytological specimen classification and laser capture microdissection applications

A system and method for performing tissue image analysis and region of interest identification for further processing applications such as laser capture microdissection is provided. The invention provides three-stage processing with flexible state transition that allows image recognition to be performed at an appropriate level of abstraction. The three stages include processing at one or more than one of the pixel, subimage and object levels of processing. Also, the invention provides both an interactive mode and a high-throughput batch mode which employs training files generated automatically.

Method for brightness level calculation of the digital x-ray image for medical applications

The invention relates to methods for evaluation a brightness level of the digital x-ray image for medical applications by means of the image histogram using a neural network. The calculations comprise of: image acquisition, image histogram calculation, transformation the frequencies of the histogram into input arguments of the neural network and calculation the brightness level as linear transform of the output value of the neural network. Training of the neural network is performed over a learning set calculated over the given image database. The transformed frequencies of histograms of these images are used as a set of input arguments of the neural network. The brightness levels calculated for each image over the region of interest and scaled to the range of output values of the neuron network are used as a set of target values.

Information processing apparatus information processing method, and storage medium

Disclosed is an information processing apparatus that selects appropriate features in polynomial time from the viewpoint of both the relevance and the redundancy of features to be selected. This information processing apparatus includes a relevance evaluator that evaluates relevance of each feature included in a set of features, a redundancy evaluator that evaluates redundancy between the features included in the set of features, and a selected feature determiner that determines selected features that optimize a submodular objective function defined using the relevance calculated by the relevance evaluator and the redundancy calculated by the redundancy evaluator.

Image processor, image processing method, control program and recording medium

In processing of detecting a target included in a registered image from a certain image, a processing load or a processing time is reduced. A registered image classifier is generated in order to detect the target included in the registered image by replacing some weak classifiers included in a dedicated classifier having the highest score of a detection result for the registered image with the weak classifiers included in a general-purpose classifier.

Multispectral Detection of Personal Attributes for Video Surveillance

Techniques, systems, and articles of manufacture for multispectral detection of attributes for video surveillance. A method includes generating one or more training sets of one or more multispectral images, generating a group of one or more multispectral box features, using the one or more training sets to select one or more of the one or more multispectral box features to generate a multispectral attribute detector, and using the multispectral attribute detector to identify a location of an attribute in video surveillance, wherein using the multispectral attribute detector comprises, for one or more locations on each spectral band level of the multispectral image, applying the multispectral attribute detector and producing an output indicating attribute detection or an output indicating no attribute detection, and wherein the attribute corresponds to the multispectral attribute detector.

System and method for generating bilinear spatiotemporal basis models

Techniques are disclosed for generating a bilinear spatiotemporal basis model. A method includes the steps of predefining a trajectory basis for the bilinear spatiotemporal basis model, receiving three-dimensional spatiotemporal data for a training sequence, estimating a shape basis for the bilinear spatiotemporal basis model using the three-dimensional spatiotemporal data, and computing coefficients for the bilinear spatiotemporal basis model using the trajectory basis and the shape basis.

Image Capture and Identification System and Process

A digital image of the object is captured and the object is recognized from plurality of objects in a database. An information address corresponding to the object is then used to access information and initiate communication pertinent to the object.

Image Capture and Identification System and Process

A digital image of the object is captured and the object is recognized from plurality of objects in a database. An information address corresponding to the object is then used to access information and initiate communication pertinent to the object.

Adaptive search window control for visual search

Image-processing apparatus and methods to adaptively control a size and/or location of a visual search window used for feature matching in a machine-vision system are described. A search window controller may receive motion vector data and image recognition rate data, and compute a search window size and/or search window location based on the received data. The computed search window size may be a portion of an image frame. The motion vector data and image recognition rate data may be computed from one or more images in a video image sequence. By adaptively controlling search window size and location, an appreciable reduction in data processing burden for feature matching may be achieved.

Process and device for securing documents

A process for securing a document including a step of printing a distribution of dots on said document, said printing, as a result of unanticipated unknowns in printing, causing an unpredictable variation, dot by dot, of at least one geometric characteristic of the printed dots and prior to said print step, a step generating said distribution of dots so that at least half the dots of said distribution are not laterally juxtaposed to four other dots of said dot distribution, and at least one dimension of at least one part of the dots of said distribution of dots is of the same order of magnitude as the average for the absolute value of said unpredictable variation. In embodiments, during the step generating the dot distribution, more than half the dots do not touch any other dot of said distribution. In embodiments, during the step of generating the dot distribution, dots are produced of which at least one geometric characteristic is variable, the geometric amplitude of the generated variation having the order of magnitude of the average dimension of at least one part of the dots.

Systems and methods for performing quality review scoring of biomarkers and image analysis methods for biological tissue

Exemplary embodiments include methods, systems, and devices for enabling users to provide quality scores for indicating the quality of image analysis methods performed on images of biological tissue. An exemplary user interface displays results of an image analysis method performed on an image of biological tissue in an overlaid manner on an image of biological tissue. The exemplary user interface enable a user to provide, directly on the user interface, one or more quality scores to indicate the user's assessment of the quality of the image analysis performed on the image. Exemplary embodiments store the quality scores provided by the user in association with the image analysis method and the image of biological tissue.

Automatic refernce selection for registration of medical imaging time series

A reference selection method includes receiving a plurality of volumes imaging an object of interest ( 211 ), determining a plurality of features of the plurality of volumes ( 212 ), receiving a set of weak learners for determining a threshold and polarity separating positive and negative features of the plurality of features ( 213 ), and learning a selection function based on the features and combining the weak learners, wherein the selection function selects a reference image from the plurality of volumes ( 214 ).

Tactical Object Finder

A detection system includes processing circuitry configured to receive overhead image data divided into a plurality of image chips and receive metadata associated with the image data. The metadata includes ground sample distance information associated with the image data and provides an indication of ground area represented by each pixel within the image chips. The processing circuitry is further configured to screen the image chips for candidate detections based on a multi-stage screening process and determine whether to classify candidate detections as target detections. The process includes an intensity based screening stage, an object extraction stage that employs binary shape features to extract objects from detect positions identified based on an output of the intensity based screening stage, and a candidate detection identification stage employing template based and structural feature criteria to identify candidate detections from an output of the object extraction stage.

Methods and apparatus for performing transformation techniques for data clustering and/or classification

Some aspects include transforming data for which at least one constraint has been specified on a portion of the data, the at least one constraint relating to a similarity and/or dissimilarity of at least some of the portion of the data. Techniques comprise determining a first transformation that approximates the at least one constraint using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the data, applying at least the first transformation to the data to obtain transformed data, and fitting a plurality of clusters to the transformed data to obtain a plurality of established clusters. Some aspects include classifying input data by transforming the input data using at least the first transformation and comparing the transformed input data to the established clusters.

Method and apparatus for assessing feasibility of probes and biomarkers

The quantitative evaluation of biomarker-probe activity is disclosed. In certain embodiments, the biomarker-probe activity may be quantified and analyzed using biodistributions generated using a model. In some embodiments, such biodistributions may be used to generate simulated images from which quantitative thresholds may be derived. In some embodiments, the quantitative thresholds may be used to analyze the biodistributions.

Method for Representing Images Using Quantized Embeddings of Scale-Invariant Image Features

Scale-invariant features are extracted from an image. The features are projected to a lower dimensional random projection matrix by multiplying the features by a matrix of random entries. The matrix of random projections is quantized to produce a matrix of quantization indices, which form a query vector for searching a database of images to retrieve metadata related to the image.

Detecting facial similarity based on human perception of facial similarity

Similar faces may be determined within images based on human perception of facial similarity. The user may provide an image including a query face to which the user wishes to find faces that are similar. Similar faces may be determined based on similarity information. Similarity information may be generated from information related to a human perception of facial similarity. Images that include faces determined to be similar, based on the similarity information, may be provided to the user as search result images. The user then may provide feedback to indicate the user's perception of similarity between the query face and the search result images.

Systems and methods for creating a visual vocabulary

Systems and methods for generating a visual vocabulary build a plurality of visual words via unsupervised learning on set of features of a given type; decompose one or more visual words to a collection of lower-dimensional buckets; generate labeled image representations based on the collection of lower dimensional buckets and labeled images, wherein labels associated with an image are associated with a respective representation of the image; and iteratively select a sub-collection of buckets from the collection of lower-dimensional buckets based on the labeled image representations, wherein bucket selection during any iteration after an initial iteration is based at least in part on feedback from previously selected buckets.

Region refocusing for data-driven object localization

A system and method are provided for segmenting an image. The method includes computing an image signature for an input image. One or more similar images are identified from a first set of images, based on the image signature of the input image and image signatures of images in the first set of images. The similar image or images are used to define a cropped region of the input image and a second image signature is computed, this time for the cropped region. One or more similar images are identified from a second set of images, based on the cropped image signature and the image signatures of images in the second set of images. The input image is segmented based on a segmentation map of at least one of the similar images identified in the second set of images.

Method for calibrating vehicular vision system

A method for calibrating a vehicular vision system includes providing a camera at a vehicle, with the camera having a field of view. Images are captured with the camera and a set of resultant images are acquired for a classification. Information is extracted related to image features in the set of resultant images, and an appropriate subset of coefficients is determined. For each classification, a classification vector of at least one appropriate weight is stored that corresponds to the determined subset of coefficients.

Image fusion using sparse overcomplete feature dictionaries

Approaches for deciding what individuals in a population of visual system “neurons” are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Acceleration of Multidimensional Scaling by Vector Extrapolation Techniques

A method for multidimensional scaling (MDS) of a data set comprising a plurality of data elements is provided, wherein each data element is identified by its coordinates, the method comprising the steps of: (i) applying an iterative optimization technique, such as SMACOF, a predetermined amount of times on a coordinates vector, said coordinates vector representing the coordinates of a plurality of said data elements, and obtaining a modified coordinates vector; (ii) applying a vector extrapolation technique, such as Minimal Polynomial Extrapolation (MPE) or reduced Rank Extrapolation (RRE) on said modified coordinates vector obtaining a further modified coordinates vector; and (iii) repeating steps (i) and (ii) until one or more predefined conditions are met.

Fast computation of kernel descriptors

An approach to computation of kernel descriptors is accelerated using precomputed tables. In one aspect, a fast algorithm for kernel descriptor computation that takes O(1) operations per pixel in each patch, based on pre-computed kernel values. This speeds up the kernel descriptor features under consideration, to levels that are comparable with D-SIFT and color SIFT, and two orders of magnitude faster than STIP and HoG3D. In some examples, kernel descriptors are applied to extract gradient, flow and texture based features for video analysis. In tests of the approach on a large database of internet videos used in the TRECVID MED 2011 evaluations, the flow based kernel descriptors are up to two orders of magnitude faster than STIP and HoG3D, and also produce significant performance improvements. Further, using features from multiple color planes produces small but consistent gains.

Agent trajectory prediction using anchor trajectories

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using anchor trajectories.

Method and device for creating a machine learning system

A method for creating a machine learning system which is designed for segmentation and object detection in images. The method includes: providing a directed graph; selecting a path through the graph, at least one additional node being selected from this subset, a path through the graph from the input node along the edges via the additional node up to the output node being selected; creating a machine learning system as a function of the selected path; and training the machine learning system created.

Electronic apparatus training individual model of user and method of operating the same

A method and an electronic apparatus for training a personal model of a user are provided. The method includes obtaining first information including personal data of the user represented as a first constituent element of the personal model; obtaining second information including group data of a plurality of users in a group to which the user belongs, represented as a second constituent element of the personal model; determining a first weight value and a second weight value to be respectively applied to the first information and the second information based on reliability of the first information; and training the personal model based on the first information and the second information to which the first weight value and the second weight value are respectively applied.

Orchestration techniques for adaptive transaction processing

Systems and techniques are described for orchestrating iterative updates to machine learning models (e.g., transaction models) deployed to multiple end-user devices. In some implementations, output data generated by a first transaction model deployed at a first end-user device is obtained. The first transaction model is trained to apply a set of evidence factors to identify potentially anomalous activity associated with a first target entity. An adjustment for a second transaction model deployed at a second end-user device is determined. The second transaction model is trained to apply the set of evidence factors to identify potentially anomalous activity associated with a second target entity determined to be similar to the first target entity. A model update for the second transaction model is generated. The model update specifies a change to the second transaction model. The model update is provided for output to the second end-user device.

Method and apparatus for extracting pattern from image

An apparatus includes a noise removal block for removing image noise mixed with an input image, a Modified Census Transform (MCT) block for transforming the input image from which the image noise has been removed into a current MCT coefficient, a candidate pattern extraction block for calculating a Sum of Hamming Distance (SHD) value between a current pattern window of the transformed current MCT coefficient and a reference pattern window of a previously registered template image by performing SHD calculation on the two pattern windows and extracting a candidate pattern, and a pattern detection block for scaling down the input image in a predetermined ratio when all pixels of the current scale are processed and detecting a candidate pattern having a minimum SHD value, of stored candidate patterns, as a representative pattern.

Image Capture and Identification System and Process

A digital image of the object is captured and the object is recognized from plurality of objects in a database. An information address corresponding to the object is then used to access information and initiate communication pertinent to the object.

Method and apparatus for enhancing semantic features of sar image oriented small set of samples

The present disclosure relates to a method for enhancing sematic features of SAR image oriented small set of samples, comprising: acquiring a sample set of an SAR target image, and performing transfer learning and training on the sample set to obtain a initialized deep neural network of an SAR target image, the sample set comprising an SAR target image and an SAR target virtual image; performing network optimization on the deep neural network by an activation function, and extracting features of the SAR target image by the optimized deep neural network to obtain a feature map; and mapping, by an auto-encoder, the feature map between a feature space and a semantic space to obtain a deep visual feature with an enhanced semantic feature.

Binary tensor factorization

In factorization of binary matrices or tensors, training algorithms usually scale linearly with the number of training examples. For very unbalanced learning problems, the number of non-zero training examples can be much smaller than the number of zeros in the full dataset. For some problems where the squared norm can be efficiently computed, the training time complexity can be reduced. A method herein receives a binary tensor defined by matrices comprising elements in a database. A processing device determines an upper bound for non-quadratic losses associated with factorization of the binary tensor. The upper bound is based on a variation parameter. The processing device performs factorization of the binary tensor by alternately minimizing the upper bound with respect to the variation parameter and minimizing the upper bound with respect to the elements of the matrices using a gradient descent method.

System and Method for Control Constrained Operation of Machine with Partially Unmodeled Dynamics Using Lipschitz Constant

A control system for controlling a machine with partially modeled dynamics to perform a task estimates a Lipschitz constant bounding the unmodeled dynamics of the machine, initializes a constraint-admissible control policy using the Lipschitz constant for controlling the machine to perform a task, such that the constraint-admissible control policy satisfies stability constraint, safety and admissibility constraint including one or combination of a state constraint and an input constraint, and has a finite cost on the performance of the task, and jointly controls the machine and update the control policy to control an operation of the machine to perform the task according the control policy starting with the initialized constraint-admissible control policy and to update the control policy using data collected while performing the task. In such a manner, the updated control policy is constraint-admissible.

Using high definition maps for generating synthetic sensor data for autonomous vehicles

According to an aspect of an embodiment, operations may comprise accessing high definition (HD) map data of a region, presenting, via a user interface, information describing the HD map data, receiving instructions, via the user interface, for modifying the HD map data by adding one or more synthetic objects to locations in the HD map data, modifying the HD map data based on the received instructions, and generating a synthetic track in the modified HD map data comprising, for each of one or more vehicle poses, generated synthetic sensor data based on the one or more synthetic objects in the modified HD map data.

Method for making music recommendations and related computing device, and medium thereof

This application discloses a method for making music recommendations. The method for making music recommendations is performed by a server device. The method includes obtaining a material for which background music is to be added; determining at least one visual semantic tag of the material, the at least one visual semantic tag describing at least one characteristic of the material; identifying a matched music matching the at least one visual semantic tag from a candidate music library; sorting the matched music according to user assessing information of a user corresponding to the material; screening the matched music based on a sorting result and according to a preset music screening condition; and recommending matched music obtained through the screening as candidate music of the material.

Apparatus and method for motor imagery classification using eeg

The present disclosure relates to an apparatus and method for motor imagery classification using electroencephalography (EEG), and more particularly, to an apparatus and method for motor imagery classification that extracts features in different domains included in EEG signals generated during motor imagery in real time and classifies a user's intentions using the features.

System and method of segmenting free space based on electromagnetic waves

A method and system for segmenting free space area, in real-time, based on data from an electromagnetic device may include: an image acquisition device adapted to capture an image of a scene; an electromagnetic device adapted to produce data corresponding to the scene; a non-transitory memory device, wherein modules of instruction code may be stored; and a processor, associated with the memory device, and configured to execute the modules of instruction code. Upon execution of said modules of instruction code, the processor may be configured to train a module to perceive elements in a scene.

Automatic recognition of entities related to cloud incidents

Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.

Account prediction using machine learning

Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

Object Detection Model Training Method, Apparatus, and Device

In an object detection model training method, a classifier that has been trained in a first phase is duplicated to at least two copies, and in a training in a second phase, each classifier obtained through duplication is configured to detect to-be-detected objects with different sizes, and train an object detection model based on a detection result.

Method and system for website detection

The present disclosure provides a method and system for website detection in the field of computer technology. According to some embodiments, a cloud platform receives a website detection request carrying a target URL and forwards the website detection request to a target edge device corresponding to the target URL; the target edge device acquires a page screenshot corresponding to the target URL, and analyzes the page screenshot based on a preset character recognition algorithm and/or a picture analysis model to generate an analysis result; and the target edge device feeds back the analysis result to a transmitting terminal of the website detection request. The method and system according to embodiments of the present disclosure effectively reduce cost and increase efficiency of website detection. Consumption of network traffic bandwidth may also be reduced and delay of website detection may be shortened.

Face model matrix training method and apparatus, and storage medium

Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

Intelligent multi-scale medical image landmark detection

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Non-transitory computer-readable recording medium storing image analysis program and image analysis method

Provided are a non-transitory computer-readable recording medium storing an image analysis program and an image analysis method which can properly extract a cell nucleus area corresponding to a diagnostician from an image for pathological diagnosis with reducing a burden on diagnosticians. A device for analyzing images for pathological diagnosis, acquires a pathological diagnosis image prepared by shooting a tissue specimen, and extracts possibles for a cell nucleus area from the pathological diagnosis image, by using a first parameter for determining a criterion for cell nucleus determination for each diagnostician. The device further extracts one or more cell nucleus areas from the possibles for a cell nucleus area, by using the second parameter for determining a criterion for cell nucleus determination for each diagnosis purpose, and causes a display unit to display the pathological diagnosis image with the one or more cell nucleus areas made recognizable.

Method, apparatus, device and system for processing commodity identification and storage medium

The present application provides a method, an apparatus, a device and a system for processing commodity identification and a storage medium, where the method includes: receiving image information transmitted by a camera apparatus and a distance signal transmitted by a distance sensor corresponding to the camera apparatus; determining a start frame and an end frame for a pickup behavior of a user according to the image information and the distance signal; and determining, according to the start frame and the end frame for the pickup behavior of the user, information of a commodity taken by the user. By performing a commodity identification on the start frame and the end frame for the pickup behavior of the user, and determining the information of the commodity taken by the user, commodity identification efficiency is effectively improved.

Feature generation pipeline for machine learning

Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.

Inspection system, discrimination system, and learning data generation device

A characteristic included in data is determined with relatively high precision. An inspection system according to one aspect of the present invention acquires a plurality of learning data sets respectively including a combination of image data and correct answer data, and sets a difficulty level of determination for each of the learning data sets in accordance with a degree to which a result which is obtained by determining the acceptability of a product in the image data of each of the learning data sets by a first discriminator conforms to a correct answer indicated by the correct answer data. Besides, the inspection system constructs a second discriminator that determines the acceptability of the product by executing stepwise machine learning in which the learning data sets are utilized in ascending order of the set difficulty level.

Machine learning based driver assistance

A method for machine learning based driver assistance is provided. The method may include detecting, in one or more images of a driver operating an automobile, one or more facial landmarks. The detection of the one or more facial landmarks may include applying, to the one or more images, a first machine learning model. A gaze dynamics of the driver may be determined based at least on the one or more facial landmarks. The gaze dynamics of the driver may include a change in a gaze zone of the driver from a first gaze zone to a second gaze zone. A state of the driver may be determined based at least on the gaze dynamics of the driver. An operation of the automobile may be controlled based at least on the state of the driver. Related systems and articles of manufacture, including computer program products, are also provided.

Systems and methods for quantum processing of data

Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.

Multiscale feature representations for object recognition and detection

Embodiments of the present invention are directed to a computer-implemented method for multiscale representation of input data. A non-limiting example of the computer-implemented method includes a processor receiving an original input. The processor downsamples the original input into a downscaled input. The processor runs a first convolutional neural network (“CNN”) on the downscaled input. The processor runs a second CNN on the original input, where the second CNN has fewer layers than the first CNN. The processor merges the output of the first CNN with the output of the second CNN and provides a result following the merging of the outputs.

Methods and Systems for the Measurement of Relative Trustworthiness for Technology Enhanced With AI Learning Algorithms

The present disclosure provides a system and method for use in evaluating the trustworthiness of an artificial intelligence (AI) process. The trustworthiness evaluation may include both automated and manual evaluation of the AI process. Further, the system is provided with functionality for automatically evaluating and modifying the criteria used in evaluating the AI process.

Method and apparatus for predicting flight delay, device and storage medium

The disclosure provides a method and an apparatus for predicting flight delay, a device and a storage medium. The method includes: acquiring flight historical data, where the flight historical data includes take-off amount and delay amount of flights during each of a plurality of time periods; determining prior knowledge of each of the plurality of time periods according to the take-off amount and the delay amount of the flights during each of the plurality of time periods; constructing a SVM prediction model according to the prior knowledge and a standard SVM model; and predicting a flight delay situation according to the SVM prediction model. The prediction of the flight delay situation for each of the plurality of time periods is realized.

Systems and methods of feature correspondence analysis

A method and system, the method including receiving semantic descriptions of features of an asset extracted from a first set of images; receiving a model of the asset, the model constructed based on a second set of a plurality images of the asset; receiving, based on an optical flow-based motion estimation, an indication of a motion for the features in the first set of images; determining a set of candidate regions of interest for the asset; determining a region of interest in the first set of images; iteratively determining a matching of features in the set of candidate regions of interest and the determined region of interest in the first set of images to generate a record of matches in features between two images in the first set of images; and displaying a visualization of the matches in features between two images in the first set of images.

Generalizable medical image analysis using segmentation and classification neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.

Detecting a defect within a bodily sample

Apparatus and methods are described for analyzing a bodily sample. One or more microscope images of the bodily sample are acquired. Using at least one computer processor at least one sample-informative feature that is indicative of a characteristic of the bodily sample is extracted from the images. Based upon the sample-informative feature, the computer processor determines that there is a defect associated with the bodily sample, and determines a source of the defect. Other applications are also described.