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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ

Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Поддерживает ввод нескольких поисковых фраз (по одной на строку). При поиске обеспечивает поддержку морфологии русского и английского языка
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Применить Всего найдено 5. Отображено 5.
06-08-2014 дата публикации

Large-scale facial expression recognition method based on multiscale LBP and sparse coding

Номер: CN103971095A
Автор: PENG XIANLIN, XIA ZHAOQIANG, FENG XIAOYI, PENG JINYE, WANG JUN, MAO XIAOFEI, CUI MINGHUI, HU XUTAO
Принадлежит:

The invention provides a large-scale facial expression recognition method based on a multiscale LBP and sparse coding. The method comprises the steps that a large-scale facial expression database is built firstly, a training database and a testing database are generated based on a random sampling technology, then facial expression features are expressed through multiscale LBP features, then a dictionary needed in a sparse coding method is generated, a new expression sample is solved to obtain an optimal sparse coefficient, and the sparse coefficients of different expressions are accumulated to recognize the expression samples. According to the method, a high-robustness feature expressing mode is obtained through the multiscale LBP features, the sparse problem in large-scale facial expression recognition is solved through sparse coding, and the effectiveness of the large-scale facial expression recognition method based on the multiscale LBP and sparse coding is verified.

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Номер записи: 1
09-05-2023 дата публикации

Grid constraint image splicing method and model construction method

Номер: CN116091316A
Автор: ZHANG YONGQIN, LI LIANGJIANG, PENG XIANLIN, LI RUOTONG, DURINGER
Принадлежит:

The invention relates to a grid constraint image splicing method and a model construction method, and the method comprises the steps: building a deep learning-based unsupervised neural network image splicing model which is used for dynamically extracting image features from an input image, and enabling the trained model to be used for ancient wall painting image splicing. A powerful technical support is provided for application and popularization of a deep learning technology in cultural heritage digital protection in the future, and the problems of result artifacts and detail missing in an existing image splicing technology are solved.

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Номер записи: 2
04-04-2023 дата публикации

Style migration method for converting landscape photo into Chinese landscape painting

Номер: CN115908602A
Автор: HU QIYAO, XIE PENGLIN, PENG XIANLIN, ZHAO WANQING, YUAN JIANHUI, PENG SHENGLIN, PENG JINYE, FAN JIANPING
Принадлежит:

The invention provides a style migration method for converting a landscape photo into a Chinese landscape painting, which adopts an image style migration framework based on CycleGAN, and adopts an RRDB module with a reflection filling layer for the conditions of artifacts and light spots existing in the migration process; a spectrum normalization U-Net discriminator is adopted to improve the capability of the discriminator, and the training dynamic state is stabilized; and high-frequency information such as edges and details is reserved by using an LMS-SSIM loss function, and the brightness and color of the generated image are optimized. A loss function is adopted to enable the model to restrain the image after style migration from the aspects of brightness, contrast and structure, so that the image generated in style conversion can better conform to the visual perception of people.

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Номер записи: 3
25-07-2023 дата публикации

Image restoration method, model and device based on attention modulation adversarial network

Номер: CN116485663A
Автор: LI RUOTONG, ZHANG YONGQIN, FAN JIANPING, PENG XIANLIN, DURINGER, MA XIAOLONG, LAN SHANZHEN, ZHONG HANWEN
Принадлежит:

The invention discloses an image restoration method, model and device based on an attention modulation adversarial network. A model construction method comprises the following steps: step 1, acquiring images of a plurality of tomb murals; 2, dividing the images with actual damage into a training set, a test set and a verification set; 3, taking an image without actual damage as a training set of a repair model; 4, the training set, the test set and the image damage area position are sent into the improved detection network for training; 5, sending the training set of the repair model and a randomly generated binary mask image into the improved modulation generative adversarial network for training; step 6, sending the verification set into a detection model and then converting the verification set into a binary image; and 7, sending the mural image and the binary image into the trained restoration network to obtain a restored image. According to the method, the characteristics of the tomb ...

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Номер записи: 4
06-08-2014 дата публикации

Area-of-interest detection method based on Kinect

Номер: CN103971116A
Автор: PENG XIANLIN, XIA ZHAOQIANG, FENG XIAOYI, PENG JINYE, WANG JUN, MAO XIAOFEI, CUI MINGHUI, HU XUTAO
Принадлежит:

The invention discloses an area-of-interest detection method based on Kinect. The area-of-interest detection method is used for solving the technical problem that an existing area-of-interest detection method based on an improved saliency map model is low in accuracy. According to the technical scheme, a Kinect3D camera lens is used for obtaining a two-dimensional RGB image and depth information; on the basis, the RGB image is used for extracting various visual features and establishing a multi-scale visual feature map; then the feature map and a depth map are fused to generate the saliency map, and a winner-take-all strategy is used for generating a two-value saliency map; finally, dilation is performed on the two-value saliency map to detect a final area of interest. The 3D image, generated by the Kinect camera lens, in a RGB-D format can be used for detecting the area of interest, wherein the area of interest is consistent with the result of perception through the human eyes. Under the ...

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Номер записи: 5