Spinal fracture detection in x-ray images

Methods and systems for detecting a vertebral fracture within an x-ray. One method includes receiving a chest x-ray image and identifying a plurality of vertebrae represented in the chest x-ray image. The method further includes extracting a plurality of image patches from the chest x-ray image, each image patch of the plurality of image patches including a portion of the chest x-ray image representing one of the plurality of vertebrae identified in the chest x-ray image. The method further includes sequencing the plurality of image patches into an ordered sequence of image patches, and assigning, with a deep learning model applied to the ordered sequence of image patches, a classification to each of the plurality of image patches indicating whether the image patch represents a fractured vertebra or an unfractured vertebra.

Multiple emitters to treat agricultural objects from multiple payload sources

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural ...

SYSTEMS AND METHODS FOR CELL CLASSIFICATION

The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

Meta few-shot class incremental learning

This disclosure provides for methods and system for meta few-shot class incremental learning. According to an aspect a method is provided. The method includes obtaining at least one weight attention map of a first network and updating weights of a second network using the at least one weight attention map, where the second network is a modulatory network. The method further includes generating at least one feature attention map of the second network based on the at least one weight attention map of the first network and a set of input images of at least one class. The method further includes generating at least one feature map of the first network based on the set of input images of the at least one class, and updating the at least one feature map of the first network based on the feature attention map of the second network.

METHODS, SYSTEMS AND COMPUTER PROGRAM PRODUCTS FOR CLASSIFYING IMAGE DATA FOR FUTURE MINING AND TRAINING

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

Methods and Systems for X-Ray Imaging and Labeling

An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

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.

Autonomous vehicle navigation

Autonomous vehicle navigation is disclosed. An example lawn mower includes a vision sensor configured to capture an image of an area being approached. The lawn mower includes a processor in communication with the vision sensor and configured to divide the image into a grid of separate image sections. Each of the image sections corresponds to a respective portion of the area being approached. For each of the image sections in the grid the processor is further configured to calculate a percentage of the image section categorized as corresponding with unmowable material and assign a result value based on a comparison between the calculated percentage and one or more movement thresholds. The lawn mower includes a drive system in communication with the processor and configured to control movement of the lawn mower based on an arrangement of the result values in the grid of the image sections.

Virtual content creation method

A virtual content creation method according to an embodiment of the present invention includes, by a server, receiving a model content including at least one of a text, an SMS, a voice-recorded MP3 file, a picture, and a video of a model; by the server, extracting a model feature including at least one of a text feature, a voice feature, an image feature, and a video feature from the model content; and when a user wants to communicate with the model, by the server, being operated based on deep learning or artificial intelligence to allow the user to input a user content to the server, determine a user state by detecting an emotional state of the user from the user content, and transform the model content into the virtual content using the model feature or the user state.

Systems, methods, and devices for classifying and identifying waste in real-time

A device is disclosed which includes a processor, a screen, and either or both of a camera and a microphone. The device may receive input related to a waste item, identify the waste item, classify a disposal type for the waste item, and provide a suggestion for disposing of the waste item.

VISION SENSORS, IMAGE PROCESSING DEVICES INCLUDING THE VISION SENSORS, AND OPERATING METHODS OF THE VISION SENSORS

A vision sensor includes a pixel array comprising pixels arranged in a matrix, an event detection circuit, an event rate controller, and an interface circuit. Each pixel is configured to generate an electrical signal in response to detecting a change in incident light intensity. The event detection circuit detects whether a change in incident light intensity has occurred at any pixels, based on processing electrical signals received from one or more pixels, and generates one or more event signals corresponding to one or more pixels at which a change in intensity of incident light is determined to have occurred. The event rate controller selects a selection of one or more event signals corresponding to a region of interest on the pixel array as one or more output event signals. The interface circuit communicates with an external processor to transmit the one or more output event signals to the external processor.

PERCEPTION AND FITTING FOR A STAIR TRACKER

PERCEPTION AND FITTING FOR A STAIR TRACKER

A method for perception and fitting for a stair tracker includes receiving sensor data for a robot adjacent to a staircase. For each stair of the staircase, the method includes detecting, at a first time step, an edge of a respective stair of the staircase based on the sensor data. The method also includes determining whether the detected edge is a most likely step edge candidate by comparing the detected edge from the first time step to an alternative detected edge at a second time step, the second time step occurring after the first time step. When the detected edge is the most likely step edge candidate, the method includes defining, by the data processing hardware, a height of the respective stair based on sensor data height about the detected edge. The method also includes generating a staircase model including stairs with respective edges at the respective defined heights.

MULTIPLE EMITTERS TO TREAT AGRICULTURAL OBJECTS FROM MULTIPLE PAYLOAD SOURCES

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural ...

Methods and systems for x-ray imaging and labeling

An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

VISION BASED TARGETING OF AGRICULTURAL OBJECTS

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may receive captured sensor data of an agricultural environment by one or more image capture devices, generate a first image from the captured sensor data, detect a first real-world agricultural object from the first image, determine a first pose of the first real-world agricultural object, identify the detected first real-world agricultural object as a new agricultural object or a preidentified agricultural object, and determine a treatment policy associated with the first real-world agricultural object.

Vision sensors, image processing devices including the vision sensors, and operating methods of the vision sensors

A vision sensor includes a pixel array comprising pixels arranged in a matrix, an event detection circuit, an event rate controller, and an interface circuit. Each pixel is configured to generate an electrical signal in response to detecting a change in incident light intensity. The event detection circuit detects whether a change in incident light intensity has occurred at any pixels, based on processing electrical signals received from one or more pixels, and generates one or more event signals corresponding to one or more pixels at which a change in intensity of incident light is determined to have occurred. The event rate controller selects a selection of one or more event signals corresponding to a region of interest on the pixel array as one or more output event signals. The interface circuit communicates with an external processor to transmit the one or more output event signals to the external processor.

SYSTEMS AND METHODS FOR SURVEYING AN AGRICULTURAL ENVIRONMENT ON A MOVING PLATFORM

Various embodiments relate generally to computer vision and automation to autonomously survey an agricultural environment. In some examples, a system can receive sensor data of a geographic boundary from one or more sensors onboard of the agricultural observation system, detect a plurality of external dynamic objects, a plurality of external static objects, one or more agricultural objects, or a combination thereof from the sensor data, determine a position of a component of the agricultural observation system relative to the plurality of external dynamic objects, the plurality of external static objects, the one or more agricultural objects, or a combination thereof, and generate a local map, in real time, via one or more processing units.

Systems and methods for identifying cell clusters within images of stained biological samples

The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

Methods, systems and computer program products for classifying image data for future mining and training

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

Autonomous agricultural treatment system using map based targeting of agricultural objects

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

Precision treatment of agricultural objects on a moving platform

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural ...

Vision sensors, image processing devices including the vision sensors, and operating methods of the vision sensors

A vision sensor includes a pixel array comprising pixels arranged in a matrix, an event detection circuit, an event rate controller, and an interface circuit. Each pixel is configured to generate an electrical signal in response to detecting a change in incident light intensity. The event detection circuit detects whether a change in incident light intensity has occurred at any pixels, based on processing electrical signals received from one or more pixels, and generates one or more event signals corresponding to one or more pixels at which a change in intensity of incident light is determined to have occurred. The event rate controller selects a selection of one or more event signals corresponding to a region of interest on the pixel array as one or more output event signals. The interface circuit communicates with an external processor to transmit the one or more output event signals to the external processor.

Digital image analysis using multi-step analysis

Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks.

Method For Calculating Weight Ratio By Quality Grade In Grain Appearance Quality Grade Discrimination Device

A method is provided for calculating a weight ratio by quality grade using a grain appearance quality grade discrimination device. The method involves the steps of imaging a plurality of grains; discriminating the quality grade of the grains on the basis of data of the imaged grains; tallying, by quality grade, the number of pixels in said data of the imaged grains with regards to the grains whose quality grade has been discriminated; multiplying the number of pixels tallied by quality grade by a weight conversion coefficient per pixel predetermined by quality grade, and thereby converting said number of pixels into a weight by quality grade; and calculating the weight ratio by quality grade of the grains on the basis of the weight by quality grade.

Method for determining a preferred node in a classification and regression tree for use in a predictive analysis

Techniques are described for determining what node of a classification and regression tree (CART) should be used by a predictive analysis application. A first approach is to use a standard deviation of the data at a given the level of the CART to determine whether data in the next, lower node is more consistent than the data in the current node. A second approach is to measure a correlation between data points in a given node and the time at which each point was sampled (or other correlation metric) to identify a preferred node.

Method, system and apparatus for determining a subject and a distractor in an image

A method of identifying a subject and a distractor in a target image is disclosed. The method receives a reference image comprising image content corresponding to image content of the target image. A first saliency map, which defines a distribution of visual attraction values identifying salient regions within the target image, and a second saliency map, which defines a distribution of visual attraction values identifying salient regions within the reference image, are determined. The method compares image content in salient regions of the first saliency map and the second saliency map. The subject is identified by a salient region of the target image sharing image content with a salient region of the reference image. The distractor is identified based on at least one remaining salient region of the target image.

System and method for root cause analysis of mobile network performance problems

A method and system for identifying causes of performance metric changes in a network by selecting, from a pool of network event counters, a plurality of candidate counters relevant to a performance metric; grouping the candidate counters into clusters of similar counters; selecting, from each cluster, one or more representative counters; and fitting the selected representative counters to a model of the performance metric to determine thereby a set of representative counters most relevant to the performance metric.

Validation template for valuable media of multiple classes

A technique for use in automatic validation of a media item involves accessing a template that comprises multiple one-class classifiers, each corresponding to one of multiple classes to which the media item might belong, and then applying each of the one-class classifiers to an image of the media item to generate a result set for each of the multiple classes. The result set for each media class is then analyzed to assess whether the media item belongs to that class.

Neural network with lane aggregation for lane selection prediction of moving objects during autonomous driving

In one embodiment, an autonomous driving system of an ADV perceives a driving environment surrounding the ADV based on sensor data obtained from various sensors, including detecting one or more lanes and at least a moving obstacle or moving object. For each of the lanes identified, an NN lane feature encoder is applied to the lane information of the lane to extract a set of lane features. For a given moving obstacle, an NN obstacle feature encoder is applied to the obstacle information of the obstacle to extract a set of obstacle features. Thereafter, a lane selection predictive model is applied to the lane features of each lane and the obstacle features of the moving obstacle to predict which of the lanes the moving obstacle intends to select.

Method and appartaus for data efficient semantic segmentation

A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

Classification method and system for high-throughput transparent articles

A method for detecting and classifying defects in high-throughput transparent articles such as syringes, vials, cartridges, ampules, and bottles is provided. The method includes the steps of providing a stream of the articles; capturing a first digital image of each of the articles in the stream; inspecting the first digital image for objects; determining parameters of the objects; performing a first classification step to classify the objects into a first defect class and a second defect class; performing a second classification step to classify the objects into a plurality of defect types using at least two second classification models; comparing at least one object parameter of a classified object with a predetermined defect type dependent threshold; classifying the article as defective or non-defective based on the comparing step; and separating defective articles from non-defective articles.

Apparatus and method for interactively viewing and clustering data segments from long data recordings

A method for operating a data processing system and computer readable medium causing a data processing system to execute that method are disclosed. The method includes causing the data processing system to receive a plurality of first EDSs classified into a plurality of first clusters and a first RDS for each of the plurality of first clusters and displaying, a first display for each of the first clusters and a RDS for each of the first clusters. The data processing system receives information from a user specifying one or more of the first clusters to be further clustered to arrive at a specified number of second clusters into which the specified one or more first clusters are to be classified, and performing a second clustering on the selected clusters. The method also includes displaying a second display that includes a plurality of second EDSs, classified into the second clusters.

Characterization of a ball game racket string pattern

The present invention relates to a method for characterizing a string pattern of a ball game racket frame as well as to the representation of a string pattern image of a strung ball game racket frame.

System of joint brain tumor and cortex reconstruction

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

Content classification method and classification model generation method

A classification model which classifies contents is provided. Learning contents and contents are included. The learning contents are provided with a first feature and a learning label, and the contents are provided with a second feature. The content classification method includes a step of generating a plurality of first classification models by machine learning using the plurality of learning contents, a step of generating a second classification model with the use of the plurality of first classification models, and a step of providing judgment data for the plurality of contents with the use of the second classification model and performing display on a GUI. The judgment data includes a classification label or a score. The GUI designates a particular numerical range of the score and displays a corresponding content in a list form. Note that features provided for the contents are management parameters (metadata).

Smart waste receptacle

Embodiments relate generally to waste receptacles and more particularly to smart waste receptacles capable of identifying waste. A waste receptacle may identify waste using machine vision and perform a task in response to identifying the waste. In an embodiment, the smart waste receptacle may instruct a user to direct the waste to one of a plurality of waste bins based on a category of the waste. In an embodiment, the smart waste receptacle may place an order to replenish the stock of a product based on the identification of waste. In an embodiment, the smart waste receptacle is created by attaching a smart device to a standard waste receptacle.

Image based orthodontic treatment refinement

A method for dental treatment may include receiving photos capturing a state of a patient's dentition of a patient at a specific time, retrieving a first treatment plan to treat the patient's dentition, identifying an intended arrangement of the first treatment plan at the specific time, evaluating the photo parameters of the photos to generate alignment data to align the intended arrangement of the first treatment plan and the photos, generating an alignment mesh using the alignment data, the alignment mesh comprising a three-dimensional (3D) mesh representation of the patient's dentition at the specific time, performing an evaluation of the first treatment plan for modifications using the alignment mesh, and identifying proposed modifications to the first treatment plan based on the evaluation.

Method and System for Cluster-Based Video Monitoring and Event Categorization

A computing system obtains a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream. In response to receiving the respective motion vector for each of the series of motion event candidates, the computing system determines a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors, and in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigns the first motion event candidate to at least a first event category associated with the first existing cluster.

Image processing apparatus, training apparatus, image processing method, training method, and storage medium

There is provided with an image processing apparatus, for example, for image recognition. An extraction unit extracts a feature amount from a target image. An estimation unit estimates distribution of regions having attributes different from each other in the target image based on the feature amount.

Methods and systems for fire detection

In accordance with various aspects of the present disclosure, methods and systems for fire detection are disclosed. In some embodiments, a method for fire detection includes: acquiring data related to a monitored area, wherein the data comprises image data related to the monitored area; determining, based on the acquired data, whether a first mode or a second mode is to be executed for fire detection, wherein the first mode comprises a first smoke detection, and the second mode comprises a first flame detection; and executing, by a hardware processor, at least one of the first mode or the second mode based on a result of the determination.

Video summarization using semantic information

An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Information processing device, information processing method, computer program product, and vehicle control system

According to an embodiment, an information processing device includes one or more processors. The processors are configured to: acquire a plurality of pieces of detection information including detection results at two-dimensional positions different from each other acquired by detection of an object by one or more detection devices through a transmission body, the plurality of pieces of detection information including distortion due to the transmission body that exists between the detection devices and the object; detect a feature point from each of the plurality of pieces of detection information; and estimate, by minimizing an error between a three-dimensional position corresponding to the feature point and a detection position of the feature point corrected based on a distortion map expressing distortion at each of the two-dimensional positions, the distortion map, the three-dimensional position, and the detection position.

Reinforced machine learning tool for anomaly detection

In some embodiments, there may be provided a system. The system may be configured to receive web server data indicating a current state of a web server; determine, by a machine learning model and based on the web server data, whether the web server is in a first failure state; generate a first failure state indication for the web server in response to the determination, by the machine learning model, that the web server is in the first failure state; determine, by a forecaster and based on the web server data, whether the web server is in a second failure state; and generate a second failure state indication for the web server in response to the determination, by the forecaster, that the web server is in the second failure state.

Unobtrusive emotion recognition system

One variation of a system for unobtrusively recognizing emotions of users includes: a galvanic skin response sensor; a heart rate sensor; a skin temperature sensor; a motion sensor; and a controller configured to sample a sequence of biosignal values of a user from the galvanic skin response sensor, the heart rate sensor, the skin temperature sensor, and the motion sensor, to implement statistical tests, self-organising maps, and clustering techniques to identify of significant changes in characteristics of the sequence of biosignal values, and to predict an emotional status of the user based on the significant changes in characteristics of the sequence of biosignal values.

Method and device for training tree model

A method and device for training a tree model based on a dataset are disclosed. The dataset comprises m pieces of sample data and m sample labels, each sample data comprises n features, the features and feature values in the dataset are ciphertexts, the method comprises: generating, for the dataset, candidate splits based on ciphertexts; partitioning, for each candidate split, the dataset into a left and right subset based on ciphertexts; calculating a partition coefficient of each candidate split based on the left and right subset obtained through partition for each candidate split; determining a feature in a target candidate split as an optimal feature, determining a threshold in the target candidate split as an optimal splitting value, the optimal feature and the optimal splitting value are ciphertexts; assigning the dataset to two child nodes of the current node based on the optimal feature and the optimal splitting value.

Generating auxiliary information for a media presentation

Techniques for generating auxiliary information for a media presentation are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, first feedback information for a group of users regarding mental states of the users during a media presentation. The computer-implemented method can further comprise determining, by the device, first parts of the media presentation considered confusing to at least some of the users and second parts of the media presentation considered interesting to at least some of the users based on the first feedback information, and generating, by the device, an index data structure comprising first information identifying the first parts and classifying the first parts as confusing and second information identifying the second parts and classifying the second parts as interesting.

Anomaly detection

According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.

Systems and methods for generating a two-dimensional planogram based on intermediate data structures

A system for generating a two-dimensional planogram. The system generates, for a facility in a cluster of facilities, a planogram for sets of items using an intermediate file.

System and method for parameter compression of capsule networks using deep features

This disclosure relates generally to system and method for parameter compression of capsule networks using deep features. The conventional capsule networks have distinct capability of retaining spatial correlations between extracted features but that comes at a cost of intensive computational, cost, memory usage and bandwidth requirement. The embodiments herein disclose a system and method for employing a lightweight deep features based capsule network that is capable of compressing the parameters. In an embodiment, the system includes a deep feature based capsule network such that the capsule layer is preceded by feature blocks. Said feature blocks comprises convolutional operation with a kernel size 3, followed by convolutional operation with kernel of size 1, and a Batch Normalization layer, and hence are able to extract deep features. These deep features when input to the capsule layer facilitates in compressing the parameters of the capsule layer, while ensuring significant increase in recognition accuracy.

Recognition method and recognition system for unambiguously recognizing an object

The presented invention relates to a computer-implemented recognition method ( 100 ) for unambiguously recognizing an object. The recognition method ( 100 ) comprises a first determining step ( 101 ) for determining, by means of a first optical sensor ( 201 ) at a first point in time, reference information by capturing a number of symbols applied to a reference object, a training step ( 103 ) for training a machine learner on the basis of the reference information and a provided ground truth which assigns respective reference information to a first class or a further class, a second determining step ( 105 ) for determining, by means of a second optical sensor ( 205 ) at a second point in time, sample information by capturing a number of symbols applied to a sample object, an assigning step ( 107 ) for the assigning of the sample information to the first class or the further class by the machine learner, and an outputting step ( 109 ) for outputting a validation signal in case the machine learner assigns the sample information to the first class. Furthermore, the presented invention relates to a recognition system ( 200 ).

Detecting and responding to an anomaly in an event log

A method identifies and prioritizes anomalies in received monitoring logs from an endpoint log source. One or more processors identify anomalies in the monitoring logs by applying a plurality of disparate types of anomaly detection algorithms to the monitoring logs, and then determine a likelihood that the identified anomalies are anomalous based on outputs of the plurality of disparate types of anomaly detection algorithms. The processor(s) then prioritize the monitoring logs based on the likelihood that the identified anomalies are actually anomalous, and send prioritized monitoring logs that exceed a priority level to a security information and event management system (SIEM).

Image-recognition apparatus, image-recognition method, and non-transitory computer-readable storage medium thereof

An image-recognition method is provided. The method includes the following steps: receiving structured data, wherein the structured data includes training-set data and testing-set data, and the structured data includes a plurality of groups, and each group includes one or more types, and each type includes a plurality of check-point images; training an artificial-intelligence (AI) model using the training-set data; inputting the testing-set data into the AI model to obtain a model evaluation of the AI model; and determining one or more first types with a lower overall recognition rate or a lower confidence level in the structured data, and deletes or corrects the check-point images in the one or more first types to update the structured data.

Generating compressed representations of sorted arrays of identifiers

A method includes obtaining an array of sorted identifiers to be stored in a designated portion of a memory of a given computing system, determining a segment size for splitting elements of the array into a plurality of segments, splitting the array into the plurality of segments based at least in part on the determined segment size, and compressing the plurality of segments to create a plurality of compressed segments. The method also includes generating a balanced binary search tree comprising a plurality of nodes each identifying a range of elements of the array corresponding to a given one of the segments and comprising a pointer to a given compressed segment corresponding to the given segment. The method further includes maintaining the balanced binary search tree and the compressed segments in the designated portion of the memory, and processing queries to the array utilizing the balanced binary search tree.

Hierarchical classifiers

Examples relate to providing hierarchical classifiers. In some examples, a superclass classifier of a hierarchy of classifiers is trained with a first type of prediction threshold, where the superclass classifier classifies data into one of a number of subclasses. At this stage, a subclass classifier is trained with a second type of prediction threshold, where the subclass classifier classifies the data into one of a number of classes. The first type of prediction threshold of the superclass classifier and the second type of prediction threshold of the subclass classifier are alternatively applied to classify data segments.

Reliability determination of workload migration activities

Techniques for determining reliability of a workload migration activity are disclosed. In one embodiment, sub-tasks associated with the workload migration activity may be determined. Further, statistical data associated with an execution of the sub-tasks corresponding to different instances of the workload migration activity may be retrieved. Furthermore, a reliability model may be trained through machine learning using the statistical data to determine reliability of the workload migration activity. Then, the reliability of a new workload migration activity may be determined using the trained reliability model.

Information processing apparatus and information processing method

[Object] To provide an information processing apparatus and an information processing method. [Solution] The information processing apparatus including: a display size decision unit configured to decide a display size of data that is based on learning of a label corresponding to a class, on the basis of a distance between a position of the data arranged on the basis of a likelihood vector obtained by recognition of the data, and a position of a class to which the data belongs; and a communication control unit configured to cause the display size to be transmitted.

Information processing apparatus and method of controlling the same

An apparatus that creates an electronic album laying-out images in units of pages from a plurality of image files, comprising a management unit which manages, by classifying into a plurality of pre-set categories, templates that define a location region of an image to be located in a page, a setting unit which sets one of the plurality of categories for each page, a decision unit which analyzes image files that are targets to include in an electronic album, and decides images that are targets to lay out in each page, and a layout unit which lays out each decided image as a target to lay out to a page of interest, by using a template classified into a category set to a page of interest by the setting unit from the templates managed by the management unit.

Automated methods and systems that facilitate root cause analysis of distributed-application operational problems and failures

The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems. Call-trace-classification rules that are useful in multiple diagnoses are maintained as diagnosis tools for future diagnoses.

Method and apparatus of processing information, method and apparatus of recommending information, electronic device, and storage medium

The present disclosure provides a method of processing information, an apparatus of processing information, a method of recommending information, an electronic device, and a storage medium. The method includes: obtaining a tree structure parameter of a tree structure, wherein the tree structure is configured to index an object set used for recommendation; obtaining a classifier parameter of a classifier, wherein the classifier is configured to sequentially predict, from a top layer of the tree structure to a bottom layer of the tree structure, a preference node set whose probability of being preferred by a user is ranked higher in each layer, and a preference node set of each layer subsequent to the top layer of the tree structure is determined based on a preference node set of a previous layer of the each layer; and constructing a recalling model based on the tree structure parameter and the classifier parameter.

Binary image stack cookie protection

A computer includes a memory and a processor programmed to execute instructions stored in the memory. The instructions include identifying a function in a binary file, assigning one of a plurality of classifications to the function, and determining that the function requires stack cookie protection based at least in part on the classification assigned to the function.

Machine learning data extraction algorithms

Embodiments of the present disclosure pertain to extracting data corresponding to particular data types using machine learning algorithms. In one embodiment, a method includes receiving an image in a backend system, sending the image to an optical character recognition (OCR) component, and in accordance therewith, receiving a plurality of characters recognized in the image. The character set is matched against known values to generate candidate character strings. The character set is processed by one or more machine learning algorithms to produce features. For each candidate character string, the features are then processed by a random forest model to determine a final character string.

Selecting from computing nodes for correlating events

In response to determining that an event matches a condition of a rule, a given one of a plurality of computing nodes is selected to send the event, based on one or both of an attribute of the event and an identifier of the rule. Information of the event is sent to the given computing node to perform correlation of the event with another event.

Image Processing

A method and apparatus for processing images, the method comprising: using a radar ( 4 ), generating a first image of an area of terrain ( 8 ); using a sensor ( 5 ), generating a second image of the area of terrain ( 8 ); performing an image segmentation process on the first image to identify a point in the first image as corresponding to a ground surface of the area of terrain ( 8 ); and projecting the identified point in the first image from the first image into the second image to identify a point in the second image as corresponding to the ground surface of the area of terrain ( 8 ). The method may further comprise: for the identified point in the second image, defining a sub-image of the second image containing that point; and performing a feature extraction process on the sub-image to identify points in the sub-image that correspond to the ground surface.

Device for managing environment of breeding farm

Provided according to an embodiment of the present invention is a device for managing the environment of a breeding farm, the device comprising: an imaging unit for capturing a thermal image of the inside of a breeding farm; an object detection unit for detecting an object from the captured image; an area division unit for dividing the captured image into multiple areas; a filtering unit for extracting, from the divided areas, a second area except a first area where the object is detected; and a control unit for comparing the temperature of the second area with a reference temperature to detect an abnormal situation.

Computer vision transaction monitoring

A machine-learning algorithm is trained on images with a set of diverse items to produce as output feature vectors in a feature-vector space derived for the set. New item images for new items are passed to the algorithm and new feature vectors are projected into the vector space. A classifier for each new item is trained on the new feature vectors to determine whether the new item is new item or is not that new item. During a transaction, an item code scanned for an item and an item image are obtained. The item image is passed to the algorithm, a feature vector is obtained, a corresponding classifier for the item code is retrieved, the feature vector is passed to the classifier, and a determination is provided as to whether the item image and item code matches a specific item that should be associated with the item code.

PU Classifier For Detection of Travel Mode Associated with Computing Devices

Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics are calculated from the sets of values corresponding to the metrics, and the set of derived values are compared to a binary classification model to determine whether the received digital data represents movement of a first type. The binary classification model was trained using a first set of traces representing the first type of movement and a second set of traces. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Method for predicting fluctuation of circuit path delay on basis of machine learning

A method for predicting the fluctuation of circuit path delay on the basis of machine learning, comprising the following steps: S1: selecting suitable sample characteristics by means of analyzing the relationship between circuit characteristics and path delay; S2: generating a random path by means of enumerating values of randomized parameters, acquiring the maximum path delay by means of performing Monte Carlo simulation on the random path, selecting a reliable path by means of the 3σ standard, and using the sample characteristics and path delay of the reliable path as a sample set (D); S3: establishing a path delay prediction model, and adjusting parameters of the model; S4: verifying the precision and stability of the path delay prediction model; S5: obtaining the path delay. The method for predicting the fluctuation of circuit path delay on the basis of machine learning has the advantages of high precision and low running time, thereby having remarkable advantages in the accuracy and efficiency of timing analysis.

Adaptive fault diagnostic system for motor vehicles

A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.

Learning device and learning method

A learning device is configured to perform learning of a decision tree. The learning device includes a branch score calculator, and a scaling unit. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree based on a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The scaling unit is configured to perform scaling on a value related to the cumulative sum used for calculating the branch score by the branch score calculator to fall within a numerical range with which the branch score is capable of being calculated.

Instrument image reliability systems and methods

Described herein are systems and methods related to image management. A system may include an instrument that includes an elongate body and an imaging device. The elongate body can be configured to be inserted into a luminal network. The imaging device may be positioned at a distal tip of the elongate body. The system may receive from the imaging device one or more images captured when the elongate body is within the luminal network. For each of the images, the system may determine one or more metrics that are indicative of a reliability of an image for localization of the distal tip of the elongate body within the luminal network. The system may determine a reliability threshold value for each of the one or more metrics. The system can utilize the one or more images based on whether the one or more metrics meet corresponding reliability threshold values.

Selecting exterior images of a structure based on capture positions of indoor images associated with the structure

Systems, computer-implemented methods, apparatus and/or computer program products are provided that facilitate improving the accuracy of global positioning system (GPS) coordinates of indoor photos. The disclosed subject matter further provides systems, computer-implemented methods, apparatus and/or computer program products that facilitate generating exterior photos of structures based on GPS coordinates of indoor photos.

Method for recognizing fog concentration of hazy image

A method for recognizing a fog concentration of a hazy image includes inputting a target hazy image into a pre-trained directed acyclic graph (DAG) support vector machine to acquire a fog concentration of the target hazy image. The fog concentration of the target hazy image is represented based on a prebuilt multi-feature model, and the feature vector in the multi-feature model includes at least one of a color feature, a dark channel feature, an information quantity feature and a contrast feature.

Method, apparatus and device for detecting lane lines

A method for detecting a lane line is provided. The method includes: determining candidate lane line regions in a lane image by filtering the lane image; extracting center points for each candidate lane line region; obtaining a fitting curve of each candidate lane line region by performing curve fitting on the center points of each candidate lane line region; classifying the candidate lane line regions as rectilinear and curvilinear candidate lane line regions according to a slope angle of the fitting curve of each candidate lane line region; dividing the candidate lane line regions into groups based on the classifying result; and determining two groups, which meet a first predetermined condition, as target groups, and recognizing candidate lane line regions in the target groups as lane line regions. Besides, an apparatus and a device for detecting a lane line are further provided.

Segmentation based image transform

The present invention relates to a method for changing the appearance of an original image comprising N>1 classes of image elements, the method comprising the step of: for each pixel and for at least one subset of the original image: calculating N probability values, each probability value defining a probability for the pixel of belonging to a corresponding one of the N classes of image elements, transforming a color of the pixel by using predetermined color transforms for each of the N classes of image elements and the N probability values for the pixel.

Manifold-anomaly detection with axis parallel explanations

Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Applied data quality metrics for physiological measurements

A model of data quality is derived for physiological monitoring with a wearable device by comparing data from the wearable device to concurrent data acquisition from a ground truth device such as a chest strap or electrocardiography (EKG) heart rate monitor. With this comparative data, a machine learning model or the like may be derived to prospectively evaluate data quality based on the data acquisition context, as determined, for example, by other sensor data and signals from the wearable device.

Electronic threat assessment system

A system for assessing threats includes a method of detecting a physical location of the system, establish a connection to at least one database, scanning a proximate area, prioritizing a plurality of identified objects, comparing data for at least one of the identified object to data from the at least one database, and outputting at least one notification.

Rail track asset survey system

The present application involves a railroad track asset surveying system comprising an image capture sensor, a location determining system, and an image processor. The image capture sensor is mounted to a railroad vehicle. The location determining system holds images captured by the image capture sensor. The image processor includes an asset classifier and an asset status analyser. The asset classifier detects an asset in one or more captured images and classifies the detected asset by assigning an asset type to the detected asset from a predetermined list of asset types according to one or more features in the captured image. The asset status analyser identifies an asset status characteristic and compares the identified status characteristic to a predetermined asset characteristic so as to evaluate a deviation therefrom.

Generating labels for synthetic images using one or more neural networks

Apparatuses, systems, and techniques to determine pixel-level labels of a synthetic image. In at least one embodiment, the synthetic image is generated by one or more generative networks and the pixel-level labels are generated using a combination of data output by a plurality of layers of the generative networks.

Method and system for training machine learning models on sensor nodes

Disclosed are apparatus and methods for automatically training a sensor node to detect anomalies in an environment. At the sensor node, an indication is received to initiate training by the sensor node to detect anomalies in the environment based on sensor data generated by a sensor that resides on such sensor node and is operable to detect sensor signals from the environment. After training is initiated, the sensor node automatically trains a model that resides on the sensor to detect anomalies in the environment, and such training is based on the sensor data. After the model is trained, the model to detect anomalies in the environment is executed by the sensor node.

Differential atlas for cancer assessment

Methods and apparatus associated with producing a quantification of differences associated with biochemical recurrence (BcR) in a region of tissue demonstrating prostate cancer (PCa) are described. One example apparatus includes a set of logics, and a data store that stores a set of magnetic resonance (MR) images acquired from a population of subjects. The set of logics includes an image acquisition logic that acquires a diagnostic image of a region of tissue in a patient demonstrating PCa, a morphology logic that extracts a shape feature, a volume feature, or an intensity feature from the diagnostic image or from a member of the set of MR images, a differential atlas construction logic that constructs a statistical shape differential atlas from the set of MR images, and a quantification logic that produces a quantification of differences based on the shape feature, the volume feature, or the intensity feature, and the differential atlas.

Determining a document type of a digital document

Disclosed are systems and method for determining document type of a digital document. An example method comprises: executing a first MLA classifier in order to determine a document type for a digital document, wherein the first MLA classifier is associated with a first hierarchical order of execution, and wherein the first MLA classifier is trained on a first trained dataset containing a first document type and a second document type, wherein the first document type is confidently predictable by the first MLA classifier and the second document type is not confidently predictable by the first MLA classifier; and responsive to determining that the first MLA classifier produced the second document type for the digital document, executing a second MLA classifier in order to determine the document type for the digital document, wherein the second MLA classifier is associated with a second hierarchical order of execution following the first hierarchical order of execution, and wherein the second MLA classifier is trained on a second trained dataset containing no documents of the first document type.

Automated facial recognition detection

A method and system for improving an automated facial recognition software system is provided. The method includes automatically detecting a face of a user via an IOT device. An image of the face is retrieved and image portions are extracted from the image and represented as a vector. The user is classified via determined facial feature attributes with respect to a plurality of user type weights stored in a cache and an initial user type of the user is determined. The vector and data indicating the initial user type are transmitted to a server and a process for inferring with respect to the initial user type, the vector, and images in a specified database associated with the initial user type, a final user type of the user s performed. An identity of the user is determined based on the inferring and the identity is transmitted to the IOT device

Apparatus and process for detecting network security attacks on iot devices

Some embodiments include a network attack detection process, including, for each of a plurality of IoT devices of a communications network: receiving corresponding network traffic data representing network traffic characteristics of a plurality of network traffic flows of the device; processing the network traffic data to generate a plurality of corresponding features for each of the network traffic flows; and applying a corresponding set of one-class flow classifiers to the generated features to classify network traffic flows of the device and assess whether the network traffic characteristics of the network traffic flows are indicative of the device being under attack or having been compromised; wherein the flow classifiers are trained with training data representing normal network traffic behaviour of the device in an uncompromised state.

Method and apparatus for counting person

A counting method and apparatus are provided. The method and/or apparatus includes generating a regression tree by inputting information about a moving object contained in a plurality of images, in response to a new image being input, inputting information about a moving object contained in the new input image to the regression tree, and determining the number of people contained in the new image based on a result value of the regression tree.

Systems for Font Replacement in Print Workflows

In implementations of systems for font replacement in print workflows, a computing device implements a print system to receive print request data describing a document having a corpus of text rendered using a font that is not available to the print system. The print system extracts the corpus of text from the document and generates an indication of a context category of the corpus of text using a machine learning model training to classify context categories of text inputs using training data describing a different corpus of text. A replacement font is identified based on the indication of the context category from replacement font data describing a plurality of candidate replacement fonts. The printing system generates a raster image depicting the corpus of text rendered using the replacement font.

Method and system for creating aesthetic design at scale

The use of gestures are increasing frequently. Normally these gestures are disconnected with each other. Therefore, various methods have been used for creating the gestural language. The existing methods for creating gestural language is difficult to learn and lacks design aesthetics. A method and system for creating aesthetic design language using a plurality of gestures is provided. The system takes in to account of aesthetics of the generated form of gestures and the user's constraints of movement—degrees of freedom. The system is using a socio-techno system which aids the machine assisted creation of aesthetic language for gestural interactions. A grammar has also been defined for creating the gestural language based on the domain. In the final stage of the system, the grammar and the form symbols are chosen/selected/published to present the interaction language to the user.

Systems and methods for physical object analysis

Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method that includes obtaining physical object data for a physical object, determining a physical object type based on the obtained physical object data, and determining based on the obtained physical object data, using at least one processor-implemented learning engine, findings data comprising structural deviation data representative of deviation between the obtained physical object data and normal physical object data representative of normal structural conditions for the determined physical object type.

Method of training random forest model, electronic device and storage medium

A method of training a random forest model, an electronic device and a storage medium. The method of training the random forest model includes analyzing, by a system of controlling model training, whether model training conditions are met or not; if the model training conditions are met, determining whether reconstructive training needs to be carried out on the random forest model or not; if the reconstructive training needs to be carried out on the random forest model, carrying out the reconstructive training on the random forest model by using sample data; if the reconstructive training does not need to be carried out on the random forest model, carrying out corrective training on the random forest model by using the sample data.

Intelligent color label matching

The present disclosure involves, systems, software, and computer-implemented methods for determining a color name or label from a color code. One example method comprises receiving information about a color and identifying the luminance of the color. If the luminance of the color is below a first luminance threshold, the color is labeled as black, and if the luminance of the color is above a second luminance threshold, the color is labeled as white. If the color is determined to be neither black nor white (e.g., if the luminance is between the first luminance threshold and the second luminance threshold), then the saturation of the color is identified, and if the saturation of the color is below a saturation threshold, the color is labeled gray. If the color is determined not to be gray (e.g., if the saturation is above the saturation threshold), the hue of the color is identified, and the color is labeled according to its hue.

System for dispatching cognitive computing across multiple workers

The present design is directed to a system for dispatching cognitive computing across multiple workers, comprising a supervisory node configured to dispatch work to a plurality of compute servers forming interconnected knowledge nodes and a relevancy engine provided in at least one knowledge node. The relevancy engine comprises a rule package and a series of processors organized in a tree arrangement, the series of processors configured to perform functions according to the rule package.

Multi-Tier Network for Task-Oriented Deep Neural Network

A method for controlling an autonomous vehicle is disclosed. The method may include receiving image data. The image data may be logistically classified by a first neural network as pertaining to one situation of a plurality of situations. Based on this logistic classification, the image data may be assigned to a second neural network that is specifically trained to address the one situation. The second neural network may perform regression on the image data. Thereafter, the vehicle may be control with a command based on the regression.

Method for automatic behavioral phenotyping

A method of identifying and classifying social complex behaviors among a group of model organisms, comprising implanting at least one RFID transponder in each model organism in said group of model organisms; enclosing said group of model organisms in a monitored space divided into RFID monitored segments; RFID tracking a position of each model organism by reading said at least one RFID transponder in each model organism over a period of time; capturing a sequence of images of each model organism over said period of time; and calculating at least one spatiotemporal model of each model organism based on time synchronization of said RFID tracked position of said model organism with said sequence of images.

Stain normalization for automated whole-slide image classification

Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.

Data Conversion/Symptom Scoring

A computer system for generating a quantitative value relating to a negative health outcome, the computer system comprising: a conversion module configured to receive multiple sets of data items associated with patients and each comprising a descriptor and the time at which that event impacted the patient. The conversion module generates a training data structure which comprises for each patient an array of selected features, each selected feature associated with a numerical value representing a score indicative of the relevance of that feature to the prediction of the negative health outcome, and a label indicating if the patient exhibits the negative health outcome; and a machine learning model which is trained using the training data structure so as to be operable to generate a quantitative value relating to a negative health outcome for a patient with at least some of the features.

Interactive training of a machine learning model for tissue segmentation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to segment magnified images of tissue samples. The method includes obtaining a magnified image of a tissue sample; processing an input comprising: the image, features derived from the image, or both, in accordance with current values of model parameters of a machine learning model to generate an automatic segmentation of the image into a plurality of tissue classes; providing, to a user through a user interface, an indication of: (i) the image, and (ii) the automatic segmentation of the image; determining an edited segmentation of the image, comprising applying modifications specified by the user to the automatic segmentation of the image; and determining updated values of the model parameters of the machine learning model based the edited segmentation of the image.

Image recognition method and image recognition apparatus

An image recognition apparatus is provided which comprises a first extracting means for extracting, from every registration image previously registered, a set of registration partial images of a predetermined size, and a second extracting means for extracting, from an input new image, a set of new partial images of a predetermined size. The apparatus further comprises a discriminating means for discriminating an attribute of the new partial image based on a rule formed by dividing the set of the registration partial images extracted by the first extracting means, and a collecting means for deriving a final recognition result of the new image by collecting discrimination results by the discriminating means at the time when the new partial images as elements of the set of the new partial images are input.

Method and apparatus for detecting braking behavior of front vehicle of autonomous vehicle

Embodiments of the present disclosure disclose a method and apparatus for detecting a braking behavior of a front vehicle of an autonomous vehicle. A specific implementation of the method comprises: extracting a vehicle image from a vehicle area in an image acquired by the image acquisition device; converting a color space of the vehicle image to generate a first vehicle image; setting a pixel value of a pixel point in the first vehicle image meeting any condition in a preset condition group to a first preset value to generate a second vehicle image; analyzing the second vehicle image to determine a candidate vehicle light area group; and detecting, based on the candidate vehicle light area group, whether a vehicle indicated by the vehicle image is braking, and generating a detection result. The implementation improves the detection efficiency for the braking behavior of the front vehicle.

Method and apparatus for detecting driver's abnormalities based on machine learning using vehicle can bus signal

Provided is a method for detecting a driver's abnormalities based on a CAN (Controller Area Network) bus network communicating with an ECU (Electronic Control Unit) of a vehicle. The method may include: acquiring a CAN bus signal related to an operation of the vehicle from the CAN bus network; extracting a detection vector from the CAN bus signal using an auto encoder; and detecting a driver's abnormality based on the detection vector.

Computer-readable recoding medium, learning method, prediction method, learning apparatus, and prediction apparatus

A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including: generating, from pieces of training data each including explanatory variables and an objective variable, a hypothesis set in which a plurality of hypotheses meeting a specific condition, each of the plurality of hypotheses being a combination of the explanatory variables, each of the pieces of training data being classified into any of the plurality of hypotheses; and performing a machine learning process to calculate a weight of each of the plurality of hypotheses included in the hypothesis set on a basis of whether each of the plurality of hypotheses includes each of the pieces of training data.

State of emotion time series

Methods, computer program products, and systems are presented. The methods include, for instance: inputs of emotion time series data of a user and environmental factor data from one or more data collection device for a user assistance service. A baseline emotion time series is generated and an environmental factor that is likely to have affected changes in state of emotion on a subject is identified by regression analysis. An emotion time series model for the identified environmental factor is produced and prediction of a path to attain a target state of emotion at a certain time in the future is made. Recommendation to achieve the target state of emotion is produced based on the predicted path.

Memory system and operating method thereof

A memory system includes: a memory device including a plurality of memory blocks; a memory; a data classifier suitable for classifying check-pointing information stored in the memory as selective information and mandatory information; and a check-pointing component suitable for performing a control to periodically perform a check-pointing operation of programming the selective information and the mandatory information in a memory block, wherein the check-pointing component performs the check-pointing operation by performing a control to program the mandatory information after programming the selective information.

Information processing method and terminal device

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device; calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information; and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

Determining fraud risk indicators using different fraud risk models for different data phases

Different fraud risk models can be developed and applied for a consortium of e-commerce merchants. With this multi-phase modeling strategy, a consortium member can get its optimal model performance at different data phases from an early phase where the consortium member does not have any historical data, to a more mature phase where the consortium member has a short time period of matured data, to a fully mature phase where the consortium member has a long-time period of matured data. On the other hand, the matured consortium data is not affected by the immature data from new members. Thus, the model performance for long-time existing members is not affected by new members at immature phases.

Multiple exposure event determination

Systems, devices and methods provide, implement, and use vision-based methods of sequence inference for a device affixed to a vehicle.

User classification based upon images

One or more systems and/or methods for providing content to a user are provided. An image, associated with a user, may be evaluated utilizing an image classifier to identify an object within the image. The object may be utilized to identify a predicted class for the user. In an example, the predicted class may correspond to a life event (e.g., graduating college, having a baby, buying a house, etc.) and/or a life stage (e.g., adolescence, retirement, etc.). Locational information (e.g., a geotag) for the image may be evaluated to determine an image location (e.g., a location where the image was generated). Responsive to the image location corresponding to a home location of the user, the predicted class may be determined to be a class associated with the user. Content (e.g., promotional content) may be selected from a content repository based upon the class and subsequently provided to the user.

Algorithm-specific neural network architectures for automatic machine learning model selection

Techniques are provided for selection of machine learning algorithms based on performance predictions by trained algorithm-specific regressors. In an embodiment, a computer derives meta-feature values from an inference dataset by, for each meta-feature, deriving a respective meta-feature value from the inference dataset. For each trainable algorithm and each regression meta-model that is respectively associated with the algorithm, a respective score is calculated by invoking the meta-model based on at least one of: a respective subset of meta-feature values, and/or hyperparameter values of a respective subset of hyperparameters of the algorithm. The algorithm(s) are selected based on the respective scores. Based on the inference dataset, the selected algorithm(s) may be invoked to obtain a result. In an embodiment, the trained regressors are distinctly configured artificial neural networks. In an embodiment, the trained regressors are contained within algorithm-specific ensembles. Techniques are also provided for optimal training of regressors and/or ensembles.

System and method for automated fault diagnosis and prognosis for rotating equipment

Techniques, including systems and methods for monitoring a rotating equipment, are provided. A sensor that is in proximity of the rotating equipment senses vibrations of the rotating equipment. The sensor generates a digital signal corresponding to the vibrations of the rotating equipment and transmits the digital signal over a communication network. A server receives the digital signal and pre-processes the digital signal using ensemble empirical mean decomposition (EEMD) technique. The server processes the digital signal using wavelet neural network (WNN) to detect faults in the rotating equipment. Further, the server processes the digital signal using the wavelet neural network to predict remaining useful life (RUL) of the rotating equipment.

Methods and systems for resolving tactile user input selections

Methods and systems are provided for resolving a selected graphical element corresponding to a tactile user input associated with a graphical user interface display on a display device associated with a vehicle, such as an aircraft. The tactile user input is classified based on its temporal duration, and the classification of the tactile user input influences the identification of the selected graphical element or other response to the tactile user input.

Method and system with optimization of lens module assembly

A lens module assembly optimization method includes: in preparing a lens module including assembled N lenses respectively formed in cavities: receiving characteristic information of at least N lenses respectively formed in N cavity groups each including a respective plurality of cavities; and processing information for selecting N cavities from the N cavity groups, based on the characteristic information. A past cavity selection result, a fitness function configured based on data of the assembled N lenses or data of the prepared lens module according to the past cavity selection result, and a genetic algorithm are received or stored. The processing of the information includes updating chromosome entity information based on the fitness function and output chromosome information crossed or mutated based on the genetic algorithm from input chromosome information corresponding to the past cavity selection result, and processing the information based on the chromosome entity information and the characteristic information.

System and method for binned inter-quartile range analysis in anomaly detection of a data series

One embodiment provides a system for facilitating anomaly detection. During operation, the system determines, by a computing device, a set of testing data which includes a plurality of data points, wherein the set includes a data series for a first variable and one or more second variables, and wherein the one or more second variables are dependent on the first variable. The system divides the set of testing data into a number of groups based on a type of the data series. The system determines an inter-quartile range for a respective group. The system classifies a first testing data point in the respective group as an anomaly based on the inter-quartile range for the respective group, thereby enhancing data mining and outlier detection for the data series for multiple variables.

Authentication based on image classification

The present disclosure provides a computer-implemented method, computer system and computer program product for authentication. The computer-implemented method comprises sending an image to a device, where the image is adapted to be classified to a first category by an image classifier, and where the first category is different from an actual category of the image; receiving a response message from the device, where the response message indicates a second category of the image determined by the device; and determining the device to be an authorized device in response to the second category being consistent with the first category.

Image based object detection

Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

Predictive parcel damage identification, analysis, and mitigation

A first parcel digital image associated with a first interaction point is received. The first parcel digital image may be associated with a first parcel being transported to or from the first interaction point. At least a second parcel digital image associated with at least a second interaction point is further be received. The second parcel digital image may be associated with the first parcel being transported to or from the second interaction point. A first parcel damage analysis is automatically generated based at least in part on analyzing the first parcel digital image and the at least second parcel image. The damage analysis can include determining whether the first parcel is damaged above or below a threshold.

Method For Classifying Facility Fault Of Facility Monitoring System

An exemplary embodiment of the present disclosure discloses a method of classifying facility failure of a facility monitoring system, the method including: acquiring sensor data output from each sensor; acquiring output data by inputting input data including the sensor data of each sensor to a trained neural network model; calculating a comparison result value by comparing the input data and the output data; diagnosing failure based on specific parameters included in the comparison result value; and classifying failure corresponding to the input data based on a combination of the specific parameters.

Image processing device, object detection device, image processing method

An image processing device for evaluating a classifier which recognizes a target object within an input image and classifies the target object into one among a plurality of classes, includes: a classification unit which obtains a classification performance of the classifier by using the classifier and classifying the target object included in each of a plurality of verification images in which a class of the target object is known, and outputting one among the plurality of classes for each of the verification images; and an evaluation unit which outputs an evaluation result of the classifier based on the classification performance of the classifier obtained with the classification unit.

Defect Review Sampling and Normalization Based on Defect and Design Attributes

A decision tree and normalized reclassification are used to classify defects. Defect review sampling and normalization can be used for accurate Pareto ranking and defect source analysis. A defect review system, such as a broadband plasma tool, and a controller can be used to bin defects using the decision tree based on defect attributes and design attributes. Class codes are assigned to at least some of the defects in each bin. Normalized reclassification assigns a class code to any unclassified defects in a bin. Additional decision trees can be used if any bin has more than one class code after normalized reclassification.

Fast single-pass interest operator for text and object detection

The invention provides a method of using machine vision to recognize text and symbols, and more particularly traffic signs.

Emergency vehicle detection

Techniques for determining a direction of arrival of an emergency are discussed. A plurality of audio sensors of a vehicle can receive audio data associated with the vehicle. An audio sensor pair can be selected from the plurality of audio sensors to generate audio data representing sound in an environment of the vehicle. An angular spectrum associated with the audio sensor pair can be determined based on the audio data. A feature associated with the audio data can be determined based on the angular spectrum and/or the audio data itself. A direction of arrival (DoA) value associated with the sound may be determined based on the feature using a machine learned model. An emergency sound (e.g., a siren) can be detected in the audio data and a direction associated with the emergency relative to the vehicle can be determined based on the feature and the DoA value.

Visual sensor-based management of a return transaction

A method, computer program product, and system are disclosed for managing a return transaction within an environment having a plurality of purchasable item. The method acquires, using at least one visual sensor disposed within the environment, first image information including a first person and including a first item associated with the first person. The method identifies the first person using image analysis performed on the first image information. Further, the method determines, using image analysis performed on the first image information and based on one or more predetermined criteria, that the first item is eligible to be returned by the first person within the return transaction. Upon completion of one or more predefined return tasks specified for the first item within the return transaction, the method updates a first data structure representing an inventory of the environment to reflect the returned first item.

Systems and methods for product identification using image analysis and trained neural network

Disclosed are methods, systems, and non-transitory computer-readable medium for analysis of images including wearable items. For example, a method may include obtaining a first set of images, each of the first set of images depicting a product; obtaining a first set of labels associated with the first set of images; training an image segmentation neural network based on the first set of images and the first set of labels; obtaining a second set of images, each of the second set of images depicting a known product; obtaining a second set of labels associated with the second set of images; training an image classification neural network based on the second set of images and the second set of labels; receiving a query image depicting a product that is not yet identified; and performing image segmentation of the query image and identifying the product in the image by performing image analysis.

Adaptive robustness certification against adversarial examples

Adaptive verifiable training enables the creation of machine learning models robust with respect to multiple robustness criteria. In general, such training exploits inherent inter-class similarities within input data and enforces multiple robustness criteria based on this information. In particular, the approach exploits pairwise class similarity and improves the performance of a robust model by relaxing robustness constraints for similar classes and increasing robustness constraints for dissimilar classes. Between similar classes, looser robustness criteria (i.e., smaller ϵ) are enforced so as to minimize possible overlap when estimating the robustness region during verification. Between dissimilar classes, stricter robustness regions (i.e., larger ϵ) are enforced. If pairwise class relationships are not available initially, preferably they are generated by receiving a pre-trained classifier and then applying a clustering algorithm (e.g., agglomerative clustering) to generate them. Once pre-defined or computed pairwise relationships are available, several grouping methods are provided to create classifiers for multiple robustness criteria.