Настройки

Укажите год
-

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

Подробнее
-

Мониторинг СМИ

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

Подробнее

Форма поиска

Поддерживает ввод нескольких поисковых фраз (по одной на строку). При поиске обеспечивает поддержку морфологии русского и английского языка
Ведите корректный номера.
Ведите корректный номера.
Ведите корректный номера.
Ведите корректный номера.
Укажите год
Укажите год
Применить Всего найдено 7. Отображено 7.
19-11-2019 дата публикации

Medical image segmentation using an integrated edge guidance module and object segmentation network

Номер: US0010482603B1
Автор: Huazhu Fu, Jianbing Shen, Zhijie Zhang, Hang Dai, Ling Shao, FU HUAZHU, SHEN JIANBING, ZHANG ZHIJIE, DAI HANG, SHAO LING, Fu, Huazhu, Shen, Jianbing, Zhang, Zhijie, Dai, Hang, Shao, Ling
Принадлежит: ARTIFICIAL INTELLIGENCE, LTD., INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD, ARTIFICIAL INTELLIGENCE LTD, Inception Institute of Artificial Intelligence, Ltd.

This disclosure relates to improved techniques for performing image segmentation functions using neural network architectures. The neural network architecture integrates an edge guidance module and object segmentation network into a single framework for detecting target objects and performing segmentation functions. The neural network architecture can be trained to generate edge-attention representations that preserve the edge information included in images. The neural network architecture can be trained to generate multi-scale feature information that preserves and enhances object-level feature information included in images. The edge-attention representations and multi-scale feature information can be fused to generate segmentation results that identify target object boundaries with increased accuracy.

Подробнее
Номер записи: 1
22-10-2019 дата публикации

Object counting and instance segmentation using neural network architectures with image-level supervision

Номер: US0010453197B1
Автор: Hisham Cholakkal, Guolei Sun, Fahad Shahbaz Khan, Ling Shao, CHOLAKKAL HISHAM, SUN GUOLEI, KHAN FAHAD SHAHBAZ, SHAO LING, Cholakkal, Hisham, Sun, Guolei, Khan, Fahad Shahbaz, Shao, Ling
Принадлежит: Inception Institute of Artificial Intelligence, Ltd., INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD

This disclosure relates to improved techniques for performing computer vision functions including common object counting and instance segmentation. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can be trained using image-level supervision techniques that utilize a loss function to jointly train an image classification branch and a density branch of the neural network architecture. The neural network architecture constructs per-category density maps that can be used to generate analysis information comprising global object counts and locations of objects in images.

Подробнее
Номер записи: 2
05-01-2021 дата публикации

Multi-view image clustering techniques using binary compression

Номер: US0010885379B2
Автор: Zheng Zhang, Li Liu, Jie Qin, Fan Zhu, Fumin Shen, Yong Xu, Ling Shao, Heng Tao Shen, ZHANG ZHENG, LIU LI, QIN JIE, ZHU FAN, SHEN FUMIN, XU YONG, SHAO LING, SHEN HENG TAO, Zhang, Zheng, Liu, Li, Qin, Jie, Zhu, Fan, Shen, Fumin, Xu, Yong, Shao, Ling, Shen, Heng Tao
Принадлежит: INCEPTION INSTITUTE OF ARTIFICIAL INTELLIGENCE, LTD., INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD, Inception Institute of Artificial Intelligence, Ltd.

This disclosure relates to improved techniques for performing multi-view image clustering. The techniques described herein utilize machine learning functions to optimize the image clustering process. Multi-view features are extracted from a collection of images. A machine learning function is configured to jointly learn a fused binary representation that combines the multi-view features and one or more binary cluster structures that can be used to partition the images. A clustering function utilizes the fused binary representation and the one or more binary cluster structures to generate one or more image clusters based on the collection of images.

Подробнее
Номер записи: 3
15-06-2021 дата публикации

Systems and methods for transforming raw sensor data captured in low-light conditions to well-exposed images using neural network architectures

Номер: US0011037278B2
Автор: Syed Waqas Zamir, Salman Hameed Khan, Fahad Shahbaz Khan, Aditya Arora, Ling Shao, ZAMIR SYED WAQAS, KHAN SALMAN HAMEED, KHAN FAHAD SHAHBAZ, ARORA ADITYA, SHAO LING, Zamir, Syed Waqas, Khan, Salman Hameed, Khan, Fahad Shahbaz, Arora, Aditya, Shao, Ling
Принадлежит: INCEPTION INSTITUTE OF ARTIFICIAL INTELLIGENCE, LTD., INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD, Inception Institute of Artificial Intelligence, Ltd.

This disclosure relates to improved techniques for generating images from raw image sensor data captured in low-light conditions without the use of flash photography. The techniques described herein utilize a neural network architecture to transform the raw image sensor data into well-exposed images. The neural network architecture can be trained using a multi-criterion loss function that jointly models both pixel-level and feature-level properties of the images. The images output by the neural network architecture can be provided to a contrast correction module that enhances the contrast of the images.

Подробнее
Номер записи: 4
21-05-2019 дата публикации

3D scene synthesis techniques using neural network architectures

Номер: US0010297070B1
Автор: Fan Zhu, Li Liu, Jin Xie, Fumin Shen, Ling Shao, Yi Fang, ZHU FAN, LIU LI, XIE JIN, SHEN FUMIN, SHAO LING, FANG YI, Zhu, Fan, Liu, Li, Xie, Jin, Shen, Fumin, Shao, Ling, Fang, Yi
Принадлежит: Inception Institute of Artificial Intelligence, Ltd, INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD, Inception Institute of Artificial Intelligence, Ltd.

This disclosure relates to improved techniques for synthesizing three-dimensional (3D) scenes. The techniques can utilize a neural network architecture to analyze images for detecting objects, classifying scenes and objects, and determining degree of freedom information for objects in the images. These tasks can be performed by, at least in part, using inter-object and object-scene dependency information that captures the spatial correlations and dependencies among objects in the images, as well as the correlations and relationships of objects to scenes associated with the images. 3D scenes corresponding to the images can then be synthesized using the inferences provided by the neural network architecture.

Подробнее
Номер записи: 5
01-10-2019 дата публикации

Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques

Номер: US0010430946B1
Автор: Yi Zhou, Xiaodong He, Lei Huang, Li Liu, Fan Zhu, Shanshan Cui, Ling Shao, ZHOU YI, HE XIAODONG, HUANG LEI, LIU LI, ZHU FAN, CUI SHANSHAN, SHAO LING, Zhou, Yi, He, Xiaodong, Huang, Lei, Liu, Li, Zhu, Fan, Cui, Shanshan, Shao, Ling
Принадлежит: INCEPTION INSTITUTE OF ARTIFICIAL INTELLIGENCE, LTD., INCEPTION INST OF ARTIFICAL INTELLIGENCE LTD, INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD, Inception Institute of Artifical Intelligence, Ltd.

This disclosure relates to improved techniques for performing computer vision functions on medical images, including object segmentation functions for identifying medical objects in the medical images and grading functions for determining severity labels for medical conditions exhibited in the medical images. The techniques described herein utilize a neural network architecture to perform these and other functions. The neural network architecture can be trained, at least in part, using semi-supervised learning techniques that enable the neural network architecture to accurately perform the object segmentation and grading functions despite limited availability of pixel-level annotation information.

Подробнее
Номер записи: 6
17-03-2020 дата публикации

Motion deblurring using neural network architectures

Номер: US0010593021B1
Автор: Jianbing Shen, Ziyi Shen, Wenguan Wang, Xiankai Lu, Ling Shao, SHEN JIANBING, SHEN ZIYI, WANG WENGUAN, LU XIANKAI, SHAO LING, Shen, Jianbing, Shen, Ziyi, Wang, Wenguan, Lu, Xiankai, Shao, Ling
Принадлежит: INCEPTION INSTITUTE OF ARTIFICIAL INTELLIGENCE, LTD., INCEPTION INST OF ARTIFICIAL INTELLIGENCE LTD, Inception Institute of Artificial Intelligence, Ltd.

This disclosure relates to improved techniques for performing computer vision functions including motion deblurring functions. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can include a human-aware attention model that is able to distinguish between foreground human objects and background portions of degraded images affected by motion blur. The neural network architecture further includes an encoder-decoder network that separately performs motion deblurring functions on foreground and background portions of degraded images, and reconstructs enhanced images corresponding to the degraded images.

Подробнее
Номер записи: 7