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41.发明授权公开(公告)号：US11587343B2
公开(公告)日：2023-02-21
申请号：US17044275
申请日：2020-04-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Lijie Zhang , Guannan Chen , Hanwen Liu , Dan Zhu
IPC: G06V30/244 , G06F40/109 , G06V30/32 , G06V30/28
Abstract: A method and a system for converting a font of a Chinese character in an image, a computer device and a medium are disclosed. A specific implementation of the method includes: acquiring a stroke of a to-be-converted Chinese character in the image and spatial distribution information of the stroke; and generating a Chinese character in a target font that corresponds to the to-be-converted Chinese character in the image according to the stroke of the to-be-converted Chinese character, the spatial distribution information of the stroke and standard stroke information of the target font, to replace the to-be-converted Chinese character.
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42.发明授权公开(公告)号：US11446706B2
公开(公告)日：2022-09-20
申请号：US16076451
申请日：2018-01-23
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Qi Zeng , Lijie Zhang , Lihua Geng , Han Yan , Xitong Ma , Tianyue Zhao
Abstract: A trash sorting and recycling method, a trash sorting device and a trash sorting and recycling system are provided. The trash sorting and recycling method includes: acquiring a detection image of trash to be sorted; processing the detection image with a deep learning neural network to judge whether or not the trash to be sorted belongs to recyclable trash; if yes, sending a first control signal, to control to deliver the trash to be sorted into a recycling region; if no, sending a second control signal, to control to deliver the trash to be sorted into a non-recycling region.
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公开(公告)号：US11113816B2
公开(公告)日：2021-09-07
申请号：US16651946
申请日：2019-09-19
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Guannan Chen , Lijie Zhang
Abstract: The present disclosure provides an image segmentation apparatus, method and relevant computing device. The image segmentation apparatus comprises: a feature extractor configured to extract N image semantic features having different scales from an input image, where N is an integer not less than 3; and a feature processor comprising cascaded dense-refine networks and being configured to perform feature processing on the N image semantic features to obtain a binarized mask image for the input image. A dense-refine network is configured to generate a low-frequency semantic feature from semantic features input thereto by performing densely-connected convolution processing on the semantic features respectively to obtain respective image global features, performing feature fusion on the image global features to obtain a fused image global feature, and performing pooling processing on the fused image global feature to generate and output the low-frequency semantic feature. The semantic features are selected from a group consisting of the N image sematic features and low-frequency semantic features generated by dense-refine networks. The feature processor is configured to obtain the binarized mask image based on low-frequency semantic features generated by the dense-refine networks.
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公开(公告)号：US20210012181A1
公开(公告)日：2021-01-14
申请号：US16626302
申请日：2019-07-22
Applicant: BOE Technology Group Co., Ltd.
Inventor： Dan Zhu , Lijie Zhang , Pablo Navarre Michelini , Hanwen Liu
Abstract: A computer-implemented method of training a convolutional neural network configured to morph content features of an input image with style features of a style image is provided. The computer-implemented method includes selecting a training style image; extracting style features of the training style image; selecting a training content image; extracting content features of the training content image; processing the training content image through the convolutional neural network to generate a training output image including the content features of the training content image morphed with the style features of the training style image; extracting content features and style features of the training output image; computing a total loss; and tuning the convolutional neural network based on the total loss including a content loss, a style loss, and a regularization loss.
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公开(公告)号：US10769758B2
公开(公告)日：2020-09-08
申请号：US15537677
申请日：2016-06-21
Applicant: BOE Technology Group Co., Ltd.
Inventor： Lijie Zhang , Zhenglong Li
Abstract: A resolving method and a system, the method includes: creating a sample library by utilization of an original high-resolution (HR) image set; training a convolutional structural network by utilization of the sample library; and obtaining an HR output signal by processing a low-resolution (LR) input signal by utilization of the trained convolutional structural network. In the resolving method and system according to this disclosure, data after expansion may be processed by simple expansion hardware, without a large change algorithm; and complex algorithms are allocated into parallelizing design, and different servers operate mutually independent; and also, due to the modular design, the design proposals of functional modules may be modified by latter optimization.
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Patent Agency Ranking
公开(公告)号：US10547888B2
公开(公告)日：2020-01-28
申请号：US15306212
申请日：2016-01-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Lijie Zhang , Zhenglong Li , Jianmin He
IPC: H04N21/2662 , H04N21/2343 , H04N19/184 , H04N19/44 , H04L29/06 , H04N21/4402
Abstract: According to embodiments of the present disclosure, a method for processing an adaptive media service at an encoder includes a first acquisition step of acquiring a first data stream including first image encoding data obtained by encoding a first image sequence, a second acquisition step of acquiring at least one second data stream, each second data steam including second image encoding data obtained by encoding a second image sequence and a target optimization parameter corresponding to the second image encoding data, a first selection step of selecting one data stream from a first data stream set in accordance with a condition of the receiver, the first data stream set at least including the first data stream and the at least one second data stream, and a first transmission step of transmitting the selected data stream to the receiver.
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公开(公告)号：US10311547B2
公开(公告)日：2019-06-04
申请号：US15741781
申请日：2017-06-23
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Lijie Zhang , Xiaoyu Li
Abstract: An image upscaling system includes at least two convolutional neural network modules and at least one synthesizer. The convolutional neural network module and the synthesizer are alternately connected to one another. The first convolutional neural network module receive an input image and the corresponding supplemental image, generate a first number of the feature images, and output them to the next synthesizer connected thereto. Other convolutional neural network modules each may receive the output image from the previous synthesizer and the corresponding supplemental image, generate a second number of feature images, and output them to the next synthesizer connected thereto, or output them from the image upscaling system. The synthesizer may synthesize every n*n feature images in the received feature image into one feature image and output the resultant third number of feature images to the next convolutional neural network module connected thereto or output them from the image upscaling system.
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公开(公告)号：US10019642B1
公开(公告)日：2018-07-10
申请号：US15518404
申请日：2016-03-02
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Lijie Zhang , Jianmin He
Abstract: An image upsampling system, a training method thereof and an image upsampling method are provided, the feature images of an image are obtained by using the convolutional network, upsampling processing is performed on the images with the muxer layer to synthesize every n×n feature images in the input signal into a feature image with the resolution amplified by n×n times, in the upsampling procedure with the muxer layer, information of respective feature images in the input signal is recorded in the generated feature image(s) without loss; and thus, every time when the image passes through a muxer layer with an upsampling multiple of n, the image resolution can be increased by n×n times.
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公开(公告)号：US09973712B2
公开(公告)日：2018-05-15
申请号：US14905532
申请日：2015-08-12
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Xitong Ma , Tianyue Zhao , Xiao Zhang , Lijie Zhang , Yanfu Li
CPC classification number: H04N5/265 , G06T3/4038 , H04N5/2628
Abstract: The present disclosure provides a video image mosaic system and a video image mosaic method. The video image mosaic system includes an image signal source, a field programmable gate array (FPGA) at least including image receivers, image scalers and image transmitters, and a Double Data Rate Synchronous Dynamic Random Access Memory (DDR). The image signal source is connected to the image receivers. The DDR is connected to the image receivers and the image scalers. The image scalers are connected to the image transmitters.
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公开(公告)号：US09858889B2
公开(公告)日：2018-01-02
申请号：US15094277
申请日：2016-04-08
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Tianyue Zhao , Lijie Zhang , Xitong Ma , Yanfu Li
CPC classification number: G09G5/02 , G09G2320/0666 , G09G2340/02 , H04N1/646 , H04N9/64
Abstract: A color compensation circuit comprises an acquisition unit for acquiring, from a video signal, gray image of a frame to be displayed and chrominance image of any color; wherein a chrominance value in the video signal, corresponding to at least a portion of pixels within the chrominance image, is absent; a processing unit for smoothly processing the chrominance value in the chrominance image according to the change trend of gray value in the gray image to obtain a chrominance image with color compensated. The present disclosure can solve the problem that compression and decompression of a video signal during transmission will significantly decrease the frame display effect, and thereby helping improve the frame display effect in a transmission scenario with the loss of chrominance value of the video signal.
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