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公开(公告)号：US10964026B2
公开(公告)日：2021-03-30
申请号：US16968575
申请日：2019-04-19
Inventor： Xin Zhao , Kaiqi Huang , Yupei Wang
Abstract: A refined segmentation system, method and device of an image shadow area are provided. The system of the present invention includes: a feature extraction network, a reverse fusion network, and a weighted fusion network. The feature extraction network includes a plurality of sampling layers which are arranged sequentially, a plurality of segmentation features of the shadow areas in the input images are obtained through the sampling layers sequentially. The reverse fusion network includes a plurality of layered reverse fusion branches, each of which includes a plurality of feature fusion layers arranged in sequence, and two input features are fused in sequence through each feature fusion layer. The weighted fusion network is configured to perform weighted fusion on outputs of the plurality of reverse fusion branches to obtain a final segmentation result of the shadow area of the input image.
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2.发明授权公开(公告)号：US10685434B2
公开(公告)日：2020-06-16
申请号：US16068912
申请日：2016-03-30
Inventor： Kaiqi Huang , Tieniu Tan , Ran He , Yueying Kao
Abstract: The present application discloses a method for assessing aesthetic quality of a natural image based on multi-task deep learning. Said method includes: step 1: automatically learning aesthetic and semantic characteristics of the natural image based on multi-task deep learning; step 2: performing aesthetic categorization and semantic recognition to the results of automatic learning based on multi-task deep learning, thereby realizing assessment of aesthetic quality of the natural image. The present application uses semantic information to assist learning of expressions of aesthetic characteristics so as to assess aesthetic quality more effectively, besides, the present application designs various multi-task deep learning network structures so as to effectively use the aesthetic and semantic information for obtaining highly accurate image aesthetic categorization. The present application can be applied to many fields relating to image aesthetic quality assessment, including image retrieval, photography and album management, etc.
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公开(公告)号：US09996939B2
公开(公告)日：2018-06-12
申请号：US15307805
申请日：2014-04-30
Inventor： Kaiqi Huang , Lijun Cao , Weihua Chen
CPC classification number: G06T7/246 , G06K9/00771 , G06K9/4642 , G06K9/4652 , G06K9/6212 , G06K9/6215 , G06T7/215 , G06T7/277 , G06T7/292 , G06T2207/30241
Abstract: The present invention relates to a large-range-first cross-camera visual target re-identification method. The method comprises: step S1, obtaining initial single-camera tracks of targets; step S2, calculating a piecewise major color spectrum histogram feature of each track, and obtaining a track feature representation; step S3, obtaining a calculation formula of the similarity between any two tracks by using a minimum uncertainty method, so as to obtain the similarity between any two tracks; and step S4, performing global data association on all the tracks by using a maximum posterior probability method, so as to obtain a cross-camera tracking result. The target re-identification method of the present invention achieves high correct identification accuracy.
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Patent Agency Ranking
4.发明申请公开(公告)号：US20180268251A1
公开(公告)日：2018-09-20
申请号：US15746568
申请日：2015-11-25
Inventor： Kaiqi Huang , Xin Zhao , Yanhua Cheng
CPC classification number: G06K9/6215 , G06K9/4652 , G06K9/6211 , G06K9/627 , G06K9/629 , G06N3/0454 , G06N3/08 , G06N20/10
Abstract: The application discloses a method and an apparatus for recognizing RGB-D objects based on adaptive similarity measure of dense matching item, wherein the method can include at least the following steps: convolution neural network features of a to-be-queried object and a reference object are extracted; dense matching is carried out on the reference object and the to-be-queried object on the basis of the convolution neural network features fused with RGB and depth information; similarity between the reference object and the to-be-queried object is measured according to a dense matching result; and the to-be-queried object is classified based on the similarity between the reference object and the to-be-queried object. With the embodiments of the present application, at least in part, the technical problem of how to improve the robustness of object recognition is solved.
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公开(公告)号：US11836619B2
公开(公告)日：2023-12-05
申请号：US17039544
申请日：2020-09-30
Inventor： Bailan Feng , Chunfeng Yao , Kaiqi Huang , Zhang Zhang , Xiaotang Chen , Houjing Huang , Dangwei Li
IPC: G06N3/08 , G06N20/00 , G06T3/40 , G06F18/213 , G06V10/774 , G06V20/52 , G06N3/084 , G06N5/046
CPC classification number: G06N3/08 , G06F18/213 , G06N20/00 , G06T3/4007 , G06V10/774 , G06V20/52 , G06N3/084 , G06N5/046
Abstract: An image processing method, a related device, and a computer storage medium are provided. The method includes: obtaining a feature intensity image corresponding to a training image, where an intensity value of a pixel in the feature intensity image is used to indicate importance of the pixel for recognizing the training image, and resolution of the training image is the same as resolution of the feature intensity image; and occluding, based on the feature intensity image, a to-be-occluded region in the training image by using a preset window, to obtain a new image, where the to-be-occluded region includes a to-be-occluded pixel, and the new image is used to update an image recognition model. According to the embodiments of the present application, a prior-art problem that a model has low accuracy and relatively poor generalization performance because of limited training data can be resolved.
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公开(公告)号：US20230169749A1
公开(公告)日：2023-06-01
申请号：US17921304
申请日：2021-04-20
Inventor： Yidan Zhou , Yuewan Lu , Xiaotang Chen , Kaiqi Huang , Chen Dong , Wenmei Gao
CPC classification number: G06V10/56 , G06V10/25 , G06V10/60 , G06V10/449 , G06V40/171
Abstract: This application provides a skin color detection method and apparatus. The skin color detection method includes: obtaining a face image (101); determining a face key point (102) in the face image; determining a skin color estimation region of interest (Region Of Interest, ROI) and an illumination estimation region of interest ROI (103) in the face image based on the face key point; obtaining a detected skin color value (104) corresponding to the skin color estimation region of interest; obtaining a detected illumination color value (105) corresponding to the illumination estimation region of interest; and using the detected skin color value and the detected illumination color value as feature input of a skin color estimation model, and obtaining a corrected skin color value (106) output by the skin color estimation model.
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公开(公告)号：US11488308B2
公开(公告)日：2022-11-01
申请号：US16968573
申请日：2019-04-19
Inventor： Xin Zhao , Kaiqi Huang , Zhe Liu
Abstract: A three-dimensional object detection method includes: extracting a target in a two-dimensional image by a pre-trained deep convolutional neural network to obtain a plurality of target objects; determining a point cloud frustum in a corresponding three-dimensional point cloud space based on each target object; segmenting the point cloud in the frustum based on a point cloud segmentation network to obtain a point cloud of interest; and estimating parameters of a 3D box in the point cloud of interest based on a network with the weighted channel features to obtain the parameters of the 3D box for three-dimensional object detection. According to the present invention, the features of the image can be learned more accurately by the deep convolutional neural network and the parameters of the 3D box in the point cloud of interest are estimated based on the network with the weighted channel features.
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8.发明授权公开(公告)号：US11574187B2
公开(公告)日：2023-02-07
申请号：US16809270
申请日：2020-03-04
Inventor： Bailan Feng , Chunfeng Yao , Kaiqi Huang , Zhang Zhang , Yang Zhou
IPC: G06N3/08 , G06F9/54 , G06K9/62 , G08G1/005 , G06V30/262
Abstract: A method for pedestrian attribute identification and positioning is provided. The method includes: performing feature extraction on a to-be-detected image at a plurality of different abstraction degrees, to obtain a plurality of first feature maps of a pedestrian attribute; performing convolution on the plurality of first feature maps, to obtain a plurality of second feature maps; mapping each second feature map to a plurality of areas (bins) that overlap each other, and performing max pooling on each bin, to obtain a plurality of high-dimensional feature vectors, where the plurality of bins that overlap each other evenly cover each second feature map; processing the plurality of high-dimensional feature vectors into a low-dimensional vector, to obtain an identification result of the pedestrian attribute; and further obtaining a positioning result of the pedestrian attribute based on the plurality of second feature maps and the plurality of high-dimensional feature vectors.
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