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公开(公告)号：US20250014150A1
公开(公告)日：2025-01-09
申请号：US18891939
申请日：2024-09-20
Inventor： Xinpeng XIE , Jiawei Chen , Yuexiang Li , Kai Ma , Yefeng Zheng
Abstract: In an image processing method, style conversion is performed on a sample image by using a generation network, to obtain a reference image. Style recognition is performed on the reference image by using an adversarial network, to determine a style loss between the reference image and the sample image. Image content recognition is performed on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The generation network is trained based on the style loss and the content loss, to obtain a trained generation network.
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12.发明授权公开(公告)号：US11954863B2
公开(公告)日：2024-04-09
申请号：US17204894
申请日：2021-03-17
Inventor： Yifan Hu , Yefeng Zheng
CPC classification number: G06T7/11 , G06F18/253 , G06N3/045 , G06N20/00 , G06T7/0012 , G06V10/806 , G06V30/2504 , G06T2207/10088 , G06T2207/30096
Abstract: An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.
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13.发明授权公开(公告)号：US11755121B2
公开(公告)日：2023-09-12
申请号：US17580545
申请日：2022-01-20
Inventor： Xiaolin Hong , Qingqing Zheng , Xinmin Wang , Kai Ma , Yefeng Zheng
CPC classification number: G06F3/017 , G06N3/045 , G06T5/002 , G06T2207/20081 , G06T2207/20084
Abstract: This application provides a gesture information processing method and apparatus, an electronic device, and a storage medium. The method includes: determining an electromyography signal collection target object in a gesture information usage environment; dividing the electromyography signal sample through a sliding window having a fixed window value and a fixed stride into different electromyography signals of the target object, and denoising the different electromyography signals of the target object; recognizing the denoised different electromyography signals, and determining probabilities of gesture information represented by the different electromyography signals; and weighting the probabilities of the gesture information represented by the different electromyography signals, so as to determine gesture information matching the target object.
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公开(公告)号：US11748889B2
公开(公告)日：2023-09-05
申请号：US17241800
申请日：2021-04-27
Inventor： Heng Guo , Yuexiang Li , Yefeng Zheng
IPC: G06T7/00 , G06T7/11 , G06F18/25 , G06F18/213 , G06V10/764 , G06V10/80 , G06V10/82 , G06V10/44 , G06V10/26
CPC classification number: G06T7/11 , G06F18/213 , G06F18/25 , G06V10/26 , G06V10/454 , G06V10/764 , G06V10/80 , G06V10/82 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30016
Abstract: Embodiments of this application disclose a brain image segmentation method and apparatus, a network device, and a storage medium. The method includes obtaining, by a device, a to-be-segmented image group comprising a plurality of modal images of a brain. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes performing, by the device, skull stripping according to the plurality of modal images to obtain a skull-stripped mask; separately performing, by the device, feature extraction on the plurality of modal images to obtain extracted features, and fusing the extracted features to obtain a fused feature; segmenting, by the device, encephalic tissues according to the fused feature to obtain an initial segmentation result; and fusing, by the device, the skull-stripped mask and the initial segmentation result to obtain a segmentation result corresponding to the to-be-segmented image group.
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公开(公告)号：US20230054751A1
公开(公告)日：2023-02-23
申请号：US17969177
申请日：2022-10-19
Inventor： Luyan Liu , Xiaolin Hong , Kai Ma , Yefeng Zheng
Abstract: A method and an apparatus for classifying an electroencephalogram signal, a device and a computer-readable storage medium. The method includes: obtaining an electroencephalogram signal; performing feature extraction on the electroencephalogram signal to obtain a signal feature corresponding to the electroencephalogram signal; obtaining a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; aligning the signal feature with the source domain feature according to the difference distribution ratio to obtain an aligned signal feature; and classifying the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal.
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Patent Agency Ranking
公开(公告)号：US20230032683A1
公开(公告)日：2023-02-02
申请号：US17964705
申请日：2022-10-12
Inventor： Donghuan LU , Kai Ma , Yefeng Zheng
Abstract: This application discloses a method for reconstructing a dendritic tissue in an image performed by a computer device. The method includes: acquiring original image data corresponding to a target image of a target dendritic tissue and corresponding reconstruction reference data determined based on a local reconstruction result of the target dendritic tissue in the target image; applying a target segmentation model to the original image data and the reconstruction reference data to acquire a target segmentation result for indicating a target category of each pixel in the target image, and the target category of any pixel being used for indicating whether the pixel belongs to the target dendritic tissue or not; and reconstructing the target dendritic tissue in the target image based on the target segmentation result to obtain a complete reconstruction result of the target dendritic tissue in the target image.
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公开(公告)号：US12288383B2
公开(公告)日：2025-04-29
申请号：US17955726
申请日：2022-09-29
Inventor： Donghuan Lu , Kai Ma , Yefeng Zheng
IPC: G06V10/774 , G06F18/2415 , G06N3/045 , G06N3/08 , G06T3/40 , G06T7/11 , G06T7/149 , G06V10/40 , G06V10/764 , G06V10/82
Abstract: A method for training an image segmentation model includes calling an encoder to perform feature extraction on a sample image and a scale image to obtain a sample image feature and a scale image feature. The method also includes performing a class activation graph calculation to obtain a sample class activation graph and a scale class activation graph. The method also includes calling a decoder to obtain a sample segmentation result of the sample image, and calling the decoder to obtain a scale segmentation result of the scale image. The method also includes calculating a class activation graph loss and calculating a scale loss. The method also includes training the decoder based on the class activation graph loss and the scale loss.
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公开(公告)号：US12125170B2
公开(公告)日：2024-10-22
申请号：US17706823
申请日：2022-03-29
Inventor： Xinpeng Xie , Jiawei Chen , Yuexiang Li , Kai Ma , Yefeng Zheng
CPC classification number: G06T5/50 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: An image processing method includes obtaining a sample image and a generative adversarial network (GAN), including a generation network and an adversarial network, and performing style conversion on the sample image, to obtain a reference image. The method further includes performing global style recognition on the reference image, to determine a global style loss between the reference image and the sample image, and performing image content recognition on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The method also includes performing local style recognition on the reference image and the sample image, to determine a local style loss of the reference image and a local style loss of the sample image, training the generation network to obtain a trained generation network, and performing style conversion on a to-be-processed image by using the trained generation network.
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19.发明授权公开(公告)号：US12112556B2
公开(公告)日：2024-10-08
申请号：US17402500
申请日：2021-08-13
Inventor： Xinrui Zhuang , Yuexiang Li , Yefeng Zheng
IPC: G06V20/64 , G06F18/214 , G06N7/01 , G06V10/774 , G06V10/82
CPC classification number: G06V20/64 , G06F18/214 , G06N7/01 , G06V10/774 , G06V10/82
Abstract: An image recognition method includes: obtaining a target three-dimensional (3D) image; inputting the target 3D image to a first recognition model; and obtaining the image type of the target 3D image outputted by the first recognition model. The first recognition model is configured to perform image recognition on the target 3D image to obtain an image type of the target 3D image. A convolutional block of the first recognition model is the same as a convolutional block of a second recognition model, and configured to perform image recognition on the target 3D image. The second recognition model is obtained by training an original recognition model using a target training sample, the target training sample including cubes obtained by rotating and sorting N target cubes obtained from a 3D sample image, N being a natural number greater than 1.
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公开(公告)号：US12033330B2
公开(公告)日：2024-07-09
申请号：US17499993
申请日：2021-10-13
Inventor： Yifan Hu , Yefeng Zheng
CPC classification number: G06T7/11 , G06N5/022 , G06T7/136 , G06T2207/20081
Abstract: The present disclosure provides methods, devices, apparatus, and storage medium for determining a target image region of a target object in a target image. The method includes: obtaining a target image comprising a target object; obtaining an original mask and an image segmentation model, the image segmentation model comprising a first unit model and a second unit model; downsampling the original mask based on a pooling layer in the first unit model to obtain a downsampled mask; extracting region convolution feature information of the target image based on a convolution pooling layer in the second unit model and the downsampled mask; updating the original mask according to the region convolution feature information; and in response to the updated original mask satisfying an error convergence condition, determining a target image region of the target object in the target image according to the updated original mask.
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