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公开(公告)号：US20220207776A1
公开(公告)日：2022-06-30
申请号：US17604288
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY , PENG CHENG LABORATORY
Inventor： Wei ZHONG , Hong ZHANG , Haojie LI , Zhihui WANG , Risheng LIU , Xin FAN , Zhongxuan LUO , Shengquan LI
Abstract: A disparity image fusion method for multiband stereo cameras belongs to the field of image processing and computer vision. The method obtains pixel disparity confidence information by using the intermediate output of binocular disparity estimation. The confidence information can be used to judge the disparity credibility of the position and assist disparity fusion. The confidence acquisition process makes full use of the intermediate output of calculation, and can be conveniently embedded into the traditional disparity estimation process, with high calculation efficiency and simple and easy operation. In the disparity image fusion method for multiband stereo cameras proposed by the method, the disparity diagrams participating in the fusion are obtained according to the binocular images of the corresponding bands, which makes full use of the information of each band and simultaneously avoiding introducing uncertainty and errors.
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公开(公告)号：US20220092809A1
公开(公告)日：2022-03-24
申请号：US17604239
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY , PENG CHENG LABORATORY
Inventor： Wei ZHONG , Hong ZHANG , Haojie LI , Zhihui WANG , Risheng LIU , Xin FAN , Zhongxuan LUO , Shengquan LI
Abstract: The present invention discloses a disparity estimation method for weakly supervised trusted cost propagation, which utilizes a deep learning method to optimize the initial cost obtained by the traditional method. By combining and making full use of respective advantages, the problems of false matching and difficult matching of untextured regions in the traditional method are solved, and the method for weakly supervised trusted cost propagation avoids the problem of data label dependency of the deep learning method.
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公开(公告)号：US20200273190A1
公开(公告)日：2020-08-27
申请号：US16650331
申请日：2019-01-07
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xinchen YE , Wei ZHONG , Zhihui WANG , Haojie LI , Lin LIN , Xin FAN , Zhongxuan LUO
Abstract: The present invention provides a method of dense 3D scene reconstruction based on monocular camera and belongs to the technical field of image processing and computer vision, which builds the reconstruction strategy with fusion of traditional geometry-based depth computation and convolutional neural network (CNN) based depth prediction, and formulates depth reconstruction model solved by efficient algorithm to obtain high-quality dense depth map. The system is easy to construct because of its low requirement for hardware resources and achieves dense reconstruction only depending on ubiquitous monocular cameras. Camera tracking of feature-based SLAM provides accurate pose estimation, while depth reconstruction model with fusion of sparse depth points and CNN-inferred depth achieves dense depth estimation and 3D scene reconstruction; The use of fast solver in depth reconstruction avoids solving inversion of large-scale sparse matrix, which improves running speed of the algorithm and ensures the real-time dense 3D scene reconstruction based on monocular camera.
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公开(公告)号：US20220198694A1
公开(公告)日：2022-06-23
申请号：US17604588
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY , PENG CHENG LABORATORY
Inventor： Wei ZHONG , Hong ZHANG , Haojie LI , Zhihui WANG , Risheng LIU , Xin FAN , Zhongxuan LUO , Shengquan LI
Abstract: The present invention discloses a disparity estimation optimization method based on upsampling and exact rematching, which conducts exact rematching within a small range in an optimized network, improves previous upsampling methods such as neighbor interpolation and bilinear interpolation for disparity maps or cost maps, and works out a propagation-based upsampling method by the way of network so that accurate disparity values can be better restored from disparity maps in the upsampling process.
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公开(公告)号：US20220148213A1
公开(公告)日：2022-05-12
申请号：US17442937
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Weiqiang KONG , Deyun LV , Wei ZHONG , Risheng LIU , Xin FAN , Zhongxuan LUO
Abstract: The present invention discloses a method for fully automatically detecting chessboard corner points, and belongs to the field of image processing and computer vision. Full automatic detection of chessboard corner points is completed by setting one or a plurality of marks with colors or certain shapes on a chessboard to mark an initial position, shooting an image and conducting corresponding processing, using a homography matrix H calculated by initial pixel coordinates of a unit grid in a pixel coordinate system and manually set world coordinates in a world coordinate system to expand outwards, and finally spreading to the whole chessboard region. The method has the advantages of simple procedure and easy implementation; the principle of expanding outwards by a homography matrix is used, so that the running speed of the algorithm is fast; and the corner points obtained by a robustness enhancement algorithm is more accurate, so that the situation of inaccurate corner point detection in the condition of complex illumination is avoided.
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Patent Agency Ranking
公开(公告)号：US20210390723A1
公开(公告)日：2021-12-16
申请号：US17109838
申请日：2020-12-02
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xinchen YE , Rui XU , Xin FAN
Abstract: The present invention provides a monocular unsupervised depth estimation method based on contextual attention mechanism, belonging to the technical field of image processing and computer vision. The invention adopts a depth estimation method based on a hybrid geometric enhancement loss function and a context attention mechanism, and adopts a depth estimation sub-network, an edge sub-network and a camera pose estimation sub-network based on convolutional neural network to obtain high-quality depth maps. The present invention uses convolutional neural network to obtain the corresponding high-quality depth map from the monocular image sequences in an end-to-end manner. The system is easy to construct, the program framework is easy to implement, and the algorithm runs fast; the method uses an unsupervised method to solve the depth information, avoiding the problem that ground-truth data is difficult to obtain in the supervised method.
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7.发明申请公开(公告)号：US20200265597A1
公开(公告)日：2020-08-20
申请号：US16649322
申请日：2019-01-07
Applicant: Dalian University of Technology
Inventor： Xinchen YE , Wei ZHONG , Haojie LI , Lin LIN , Xin FAN , Zhongxuan LUO
IPC: G06T7/55
Abstract: The present invention provides a method for estimating high-quality depth map based on depth prediction and enhancement sub-networks, belonging to the technical field of image processing and computer vision. This method constructs depth prediction subnetwork to predict depth information from color image and uses depth enhancement subnetwork to obtain high-quality depth map by recovering the low-resolution depth map. It is easy to construct the system, and can obtain the high-quality depth map from the corresponding color image directly by the well-trained end to end network. The algorithm is easy to be implemented. It uses high-frequency component of color image to help to recover the lost depth boundaries information caused by down-sampling operators in depth prediction sub-network, and finally obtains high-quality and high-resolution depth maps. It uses spatial pyramid pooling structure to increase the accuracy of depth map prediction for multi-scale objects in the scene.
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公开(公告)号：US20220198712A1
公开(公告)日：2022-06-23
申请号：US17604185
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY , PENG CHENG LABORATORY
Inventor： Wei ZHONG , Deyun LV , Weiqiang KONG , Risheng LIU , Xin FAN , Zhongxuan LUO , Shengquan LI
Abstract: The present invention discloses a method for adaptively detecting chessboard sub-pixel level corner points. Adaptive detection of chessboard sub-pixel level corner points is completed by marking position of an initial unit grid on a chessboard, using a homography matrix H calculated by pixel coordinates of four corner points of the initial unit grid in a pixel coordinate system and world coordinates in a world coordinate system to expand outwards, adaptively adjusting size of an iteration window in the process of expanding outwards, and finally spreading to the whole chessboard region.
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公开(公告)号：US20210312197A1
公开(公告)日：2021-10-07
申请号：US17280745
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Wei ZHONG , Shenglun CHEN , Haojie LI , Zhihui WANG , Risheng LIU , Xin FAN , Zhongxuan LUO
Abstract: The present invention discloses a grid map obstacle detection method fusing probability and height information, and belongs to the field of image processing and computer vision. A high-performance computing platform is constructed by using a GPU, and a high-performance solving algorithm is constructed to obtain obstacle information in a map. The system is easy to construct, the program is simple, and is easy to implement. The positions of obstacles are acquired in a multi-layer grid map by fusing probability and height information, so the robustness is high and the precision is high.
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10.发明申请公开(公告)号：US20200258218A1
公开(公告)日：2020-08-13
申请号：US16649650
申请日：2019-01-07
Applicant: Dalian University of Technology
Inventor： Rui XU , Xinchen YE , Lin LIN , Haojie LI , Xin FAN , Zhongxuan LUO
Abstract: Provided is a method based on deep neural network to extract appearance and geometry features for pulmonary textures classification, which belongs to the technical fields of medical image processing and computer vision. Taking 217 pulmonary computed tomography images as original data, several groups of datasets are generated through a preprocessing procedure. Each group includes a CT image patch, a corresponding image patch containing geometry information and a ground-truth label. A dual-branch residual network is constructed, including two branches separately takes CT image patches and corresponding image patches containing geometry information as input. Appearance and geometry information of pulmonary textures are learnt by the dual-branch residual network, and then they are fused to achieve high accuracy for pulmonary texture classification. Besides, the proposed network architecture is clear, easy to be constructed and implemented.
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