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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：US20220215569A1
公开(公告)日：2022-07-07
申请号：US17603856
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 belongs to the field of image processing and computer vision, and discloses an acceleration method of depth estimation for multiband stereo cameras. In the process of depth estimation, during binocular stereo matching in each band, through compression of matched images, on one hand, disparity equipotential errors caused by binocular image correction can be offset to make the matching more accurate, and on the other hand, calculation overhead is reduced. In addition, before cost aggregation, cost diagrams are transversely compressed and sparsely matched, thereby reducing the calculation overhead again. Disparity diagrams obtained under different modes are fused to obtain all-weather, more complete and more accurate depth information.
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公开(公告)号：US20220028043A1
公开(公告)日：2022-01-27
申请号：US17284394
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Wei ZHONG , Haojie LI , Boqian LIU , Zhihui WANG , Risheng LIU , Zhongxuan LUO , Xin FAN
Abstract: A multispectral camera dynamic stereo calibration algorithm is based on saliency features. The joint self-calibration method comprises the following steps: step 1: conducting de-distortion and binocular correction on an original image according to internal parameters and original external parameters of an infrared camera and a visible light camera. Step 2: Detecting the saliency of the infrared image and the visible light image respectively based on a histogram contrast method. Step 3: Extracting feature points on the infrared image and the visible light image. Step 4: Matching the feature points extracted in the previous step. Step 5: judging a feature point coverage area. Step 6: correcting the calibration result. The present invention solves the change of a positional relationship between an infrared camera and a visible light camera due to factors such as temperature, humidity and vibration.
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10.发明申请公开(公告)号：US20210390339A1
公开(公告)日：2021-12-16
申请号：US17112499
申请日：2020-12-04
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xinchen YE , Rui XU , Xin FAN
Abstract: The invention discloses a depth estimation and color correction method for monocular underwater images based on deep neural network, which belongs to the field of image processing and computer vision. The framework consists of two parts: style transfer subnetwork and task subnetwork. The style transfer subnetwork is constructed based on generative adversarial network, which is used to transfer the apparent information of underwater images to land images and obtain abundant and effective synthetic labeled data, while the task subnetwork combines the underwater depth estimation and color correction tasks with the stack network structure, carries out collaborative learning to improve their respective accuracies, and reduces the gap between the synthetic underwater image and the real underwater image through the domain adaptation strategy, so as to improve the network's ability to process the real underwater image.
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