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公开(公告)号：US12293480B2
公开(公告)日：2025-05-06
申请号：US17976259
申请日：2022-10-28
Inventor： Yandan Zhao , Shuheng Lin , Xuan Cao , Yanhao Ge , Chengjie Wang , Weijan Cao
Abstract: This application provides a method for reconstructing a three-dimensional model, a method for training a three-dimensional reconstruction model, an apparatus, a computer device, and a storage medium. The method for reconstructing a three-dimensional model includes: obtaining an image feature coefficient of an input image; respectively obtaining, according to the image feature coefficient, a global feature map and an initial local feature map based on a texture and a shape of the input image; performing edge smoothing on the initial local feature map, to obtain a target local feature map; respectively splicing the global feature map and the target local feature map based on the texture and the shape, to obtain a target texture image and a target shape image; and performing three-dimensional model reconstruction according to the target texture image and the target shape image, to obtain a target three-dimensional model.
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公开(公告)号：US12197640B2
公开(公告)日：2025-01-14
申请号：US17977646
申请日：2022-10-31
Inventor： Keke He , Zhengkai Jiang , Jinlong Peng , Yang Yi , Xiaoming Yu , Juanhui Tu , Yi Zhou , Yabiao Wang , Ying Tai , Chengjie Wang , Jilin Li , Feiyue Huang
Abstract: An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.
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公开(公告)号：US11928893B2
公开(公告)日：2024-03-12
申请号：US17530428
申请日：2021-11-18
Inventor： Donghao Luo , Yabiao Wang , Chenyang Guo , Boyuan Deng , Chengjie Wang , Jilin Li , Feiyue Huang , Yongjian Wu
IPC: G06V20/40 , G06F18/213 , G06T7/246 , G06V40/20 , G06N3/02
CPC classification number: G06V40/20 , G06F18/213 , G06T7/246 , G06N3/02 , G06T2207/20081
Abstract: An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.
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公开(公告)号：US11436739B2
公开(公告)日：2022-09-06
申请号：US16922196
申请日：2020-07-07
Inventor： Yabiao Wang , Yanhao Ge , Zhenye Gan , Yuan Huang , Changyou Deng , Yafeng Zhao , Feiyue Huang , Yongjian Wu , Xiaoming Huang , Xiaolong Liang , Chengjie Wang , Jilin Li
Abstract: This present disclosure describes a video image processing method and apparatus, a computer-readable medium and an electronic device, relating to the field of image processing technologies. The method includes determining, by a device, a target-object region in a current frame in a video. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes determining, by the device, a target-object tracking image in a next frame and corresponding to the target-object region; and sequentially performing, by the device, a plurality of sets of convolution processing on the target-object tracking image to determine a target-object region in the next frame. A quantity of convolutions of a first set of convolution processing in the plurality of sets of convolution processing is less than a quantity of convolutions of any other set of convolution processing.
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公开(公告)号：US10713812B2
公开(公告)日：2020-07-14
申请号：US15944656
申请日：2018-04-03
Inventor： Chengjie Wang
Abstract: A method of determining a facial pose angle of a human face within an image is provided. After capturing a first image of the human face, respective coordinates of a predefined set of facial feature points of the human face in the first image are obtained. The predefined set of facial feature points includes an odd number of facial feature points, e.g., at least a first pair of symmetrical facial feature points, a second pair of symmetrical facial feature points, and a first single facial feature point. The predefined set of facial feature points are not coplanar. Next, one or more predefined key values based on the respective coordinates of the predefined set of facial feature points of the human face in the first image are calculated. Finally, a pre-established correspondence table is queried using the one or more predefined key values to determine the facial pose angle of the human face in the first image.
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Patent Agency Ranking
公开(公告)号：US10713532B2
公开(公告)日：2020-07-14
申请号：US15925028
申请日：2018-03-19
Inventor： Shouhong Ding , Jilin Li , Chengjie Wang , Feiyue Huang , Yongjian Wu , Guofu Tan
Abstract: The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.
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公开(公告)号：US10395094B2
公开(公告)日：2019-08-27
申请号：US15696016
申请日：2017-09-05
Inventor： Chengjie Wang , Guofu Tan , Hui Ni
Abstract: This application discloses a method and a terminal for detecting glasses in a face image. The method includes: obtaining a face image; determining a nose bridge region in the face image; detecting an image change in the nose bridge region to obtain an image change result of the nose bridge region; and determining whether there are glasses in the face image according to the image change result of the nose bridge region. The terminal for detecting glasses in a face image matches the method.
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公开(公告)号：US10055879B2
公开(公告)日：2018-08-21
申请号：US15652009
申请日：2017-07-17
Inventor： Chengjie Wang , Jilin Li , Feiyue Huang , Lei Zhang
CPC classification number: G06T15/10 , G06K9/00214 , G06K9/00281 , G06T7/73 , G06T15/04 , G06T19/20 , G06T2207/30201 , G06T2210/44 , G06T2215/16 , G06T2219/2021
Abstract: A 3D human face reconstruction method and apparatus, and a server are provided. In some embodiments, the method includes determining feature points on an acquired 2D human face image; determining posture parameters of a human face according to the feature points, and adjusting a posture of a universal 3D human face model according to the posture parameters; determining points on the universal 3D human face model corresponding to the feature points, and adjusting the corresponding points in a sheltered status to obtain a preliminary 3D human face model; and performing deformation adjusting on the preliminary 3D human face model, and performing texture mapping on the deformed 3D human face model to obtain a final 3D human face.
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公开(公告)号：US20180204094A1
公开(公告)日：2018-07-19
申请号：US15925028
申请日：2018-03-19
Inventor： Shouhong Ding , Jilin Li , Chengjie Wang , Feiyue Huang , Yongjian Wu , Guofu Tan
Abstract: The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.
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公开(公告)号：US11961242B2
公开(公告)日：2024-04-16
申请号：US17033675
申请日：2020-09-25
Inventor： Yandan Zhao , Chengjie Wang , Weijian Cao , Yun Cao , Pan Cheng , Yuan Huang
CPC classification number: G06T7/246 , G06N20/00 , G06T7/20 , G06V10/25 , G06V10/40 , G06V10/764 , G06V20/52 , G06T2207/20076 , G06T2207/20081
Abstract: A target tracking method is provided for a computer device. The method includes determining a target candidate region of a current image frame; capturing a target candidate image matching the target candidate region from the current image frame; determining a target region of the current image frame according to an image feature of the target candidate image; determining motion prediction data of a next image frame relative to the current image frame by using a motion prediction model and according to the image feature of the target candidate image; and determining a target candidate region of the next image frame according to the target region and the motion prediction data.
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