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公开(公告)号：US11361222B2
公开(公告)日：2022-06-14
申请号：US16614547
申请日：2019-06-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Dan Zhu , Hanwen Liu
IPC: G06N3/00 , G06N3/08 , G06F17/16 , G06N3/04 , G06T3/40 , G06K9/62 , G06T5/00 , G06T5/50 , G06V10/75
Abstract: A cascaded system for classifying an image includes a first cascade layer including a first analysis module coupled to a first input terminal, and a first pooling module coupled to the first analysis module; a second cascade layer including a second analysis module coupled to a second input terminal, and a second pooling module coupled to the first pooling module and the second analysis module; a synthesis layer coupled to the second pooling module, and an activation layer coupled to the synthesis layer.
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公开(公告)号：US11348005B2
公开(公告)日：2022-05-31
申请号：US16614558
申请日：2019-06-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Dan Zhu , Hanwen Liu
Abstract: The present disclosure provides a method of training a generative adversarial network. The method includes iteratively enhancing a first noise input in a generative network to generate a first output image; iteratively enhancing a second noise input in the generative network to generate a second output image; transmitting the first output image and a second reference image to a discriminative network, the second reference image corresponding to the first reference image and having a higher resolution than the first reference image; obtaining a first score from the discriminative network based on the second reference image, and a second score from the discriminative network based on the first output image; calculating a loss function of the generative network based on the first score and the second score; and adjusting at least one parameter of the generative network to lower the loss function of the generative network.
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公开(公告)号：US20220005165A1
公开(公告)日：2022-01-06
申请号：US16495550
申请日：2019-05-06
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Dan Zhu , Pablo Navarrete Michelini
IPC: G06T5/00
Abstract: The present disclosure relates to an image enhancement method. The image enhancement method may include acquiring an input image; solving an incident component of the input image that minimizes a loss function; and obtaining an optimized image of the input image based on the incident component, wherein the loss function comprises an activation function.
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公开(公告)号：US11189013B2
公开(公告)日：2021-11-30
申请号：US16465294
申请日：2018-12-17
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Dan Zhu , Hanwen Liu
Abstract: The present disclosure relates to an image processing method. The image processing method may include upscaling a feature image of an input image by an upscaling convolutional network to obtain a upscaled feature image; downscaling the upscaled feature image by a downscaling convolutional network to obtain a downscaled feature image; determining a residual image between the downscaled feature image and the feature image of the input image; upscaling the residual image between the downscaled feature image and the feature image of the input image to obtain an upscaled residual image; correcting the upscaled feature image using the upscaled residual image to obtain a corrected upscaled feature image; and generating a first super-resolution image based on the input image using the corrected upscaled feature image.
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公开(公告)号：US20210326691A1
公开(公告)日：2021-10-21
申请号：US16492873
申请日：2019-03-22
Applicant: BOE Technology Group Co., Ltd.
Inventor： Hanwen Liu , Pablo Navarrete Michelini , Lijie Zhang , Dan Zhu
Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method using a convolutional neural network includes processing an input image through at least one channel of the convolutional neural network to generate an output image including content features of the input image morphed with style features of a reference style image. The at least one channel includes a down-sampling segment, a densely connected segment, and an up-sampling segment sequentially connected together. Processing the input image through the at least one channel of the convolutional neural network includes processing an input signal through the down-sampling segment to generate a down-sampling segment output; processing the down-sampling segment output through the densely connected segment to generate a densely connected segment output; and processing the densely connected segment output through the up-sampling segment to generate an up-sampling segment output. The input signal includes a component of the input image.
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Patent Agency Ranking
公开(公告)号：US12211319B2
公开(公告)日：2025-01-28
申请号：US17761021
申请日：2021-04-20
Applicant: BOE Technology Group Co., Ltd.
Inventor： Dan Zhu , Xingchen Liu
Abstract: The present disclosure discloses a method, apparatus and system for customer group analysis, and a storage medium. The method includes: obtaining video images of a customer group passing by a display apparatus; recognizing and tracking head images of the customer group in the video images, and determining behavior characteristics of individuals in the customer group; and performing statistical analysis on the behavior characteristics of the customer group corresponding to the display apparatus, so as to update a quantity of individuals corresponding to each behavior characteristic.
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公开(公告)号：US12211185B2
公开(公告)日：2025-01-28
申请号：US17434729
申请日：2020-11-27
Applicant: BOE Technology Group Co., Ltd.
Inventor： Dan Zhu , Guannan Chen , Ran Duan
IPC: G06T5/73 , G06N3/045 , G06N3/0464 , G06N3/08
Abstract: A computer-implemented image-processing method is provided. The computer-implemented image-processing method includes obtaining a pair of training samples including a training image having a first degree of sharpness and a reference image having a second degree of sharpness, the second degree greater than the first degree, at least portions of the training image and the reference image in a same pair having same contents; inputting the training image to the image-enhancing convolutional neural network to generate a training enhanced image; inputting the training enhanced image into an edge detector; generating, by the edge detector, a plurality of first edge maps; inputting the reference image into the edge detector; generating, by the edge detector, a plurality of second edge maps; and tuning parameters in the image-enhancing convolutional neural network to minimize at least the one or more first losses and a second loss.
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28.发明授权公开(公告)号：US12164560B2
公开(公告)日：2024-12-10
申请号：US17255458
申请日：2019-11-28
Applicant: BOE Technology Group Co., Ltd.
Inventor： Hanwen Liu , Pablo Navarrete Michelini , Dan Zhu
IPC: G06F3/04842 , G06F3/04847 , G06F16/55 , G06F16/58 , G06V10/20
Abstract: The present disclosure discloses a user interface system, electronic equipment and an interaction method for picture recognition. The electronic equipment includes a display screen, a memory and a processor, a first interface is displayed on the display screen, and the first interface includes at least one primary function classification tag, a plurality of secondary function classification tags and at least one tertiary function classification tag included in each of the secondary function classification tags; and by selecting a tertiary function classification tag, a second interface can be displayed such that a function effect corresponding to the tertiary function classification tag is experienced on the second interface. The electronic equipment can be used such that different tertiary function classification tags can be selected for experience in the first interface, can provide users with practicality, and has certain tool properties.
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公开(公告)号：US12148200B2
公开(公告)日：2024-11-19
申请号：US17419726
申请日：2020-12-29
Applicant: BOE Technology Group Co., Ltd.
Inventor： Yunhua Lu , Hanwen Liu , Pablo Navarrete Michelini , Lijie Zhang , Dan Zhu
Abstract: A method for processing an image includes acquiring an input image, performing down-sampling and feature extraction on the input image by an encoder network to obtain multiple feature maps, and performing up-sampling and feature extraction on the multiple feature maps by a decoder network to obtain a target segmentation image. Processing levels between the encoder network and the decoder network for outputting feature maps with the same resolution are connected with each other. The encoder network and the decoder network each includes one or more dense calculation blocks, and at least one convolution module in any dense computation block includes at least one group of asymmetric convolution kernels.
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公开(公告)号：US11954822B2
公开(公告)日：2024-04-09
申请号：US17419350
申请日：2020-10-13
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Wenbin Chen , Hanwen Liu , Dan Zhu
IPC: G06T3/4046 , G06N3/08 , G06T3/4053 , G06T5/50
CPC classification number: G06T3/4046 , G06N3/08 , G06T3/4053 , G06T5/50
Abstract: An image processing method, an image processing device, a training method of a neural network, an image processing method based on a combined neural network model, a constructing method of a combined neural network model, a neural network processor, and a storage medium are provided. The image processing method includes: obtaining, based on an input image, initial feature images of N stages with resolutions from high to low, where N is a positive integer and N>2, performing, based on initial feature images of second to N-th stages, cyclic scaling processing on an initial feature image of a first stage, to obtain an intermediate feature image; and preforming merging processing on the intermediate feature image to obtain an output image. The cyclic scaling processing includes hierarchically-nested scaling processing of N−1 stages, and scaling processing of each stage includes down-sampling processing, concatenating processing, up-sampling processing, and residual link addition processing.
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