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公开(公告)号：US11315273B2
公开(公告)日：2022-04-26
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：US11302105B2
公开(公告)日：2022-04-12
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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Patent Agency Ranking
公开(公告)号：US12108022B2
公开(公告)日：2024-10-01
申请号：US17442967
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Weiqiang Kong , Deyun Lv , Wei Zhong , Risheng Liu , Xin Fan , Zhongxuan Luo
IPC: G06K9/00 , G06T7/155 , G06V10/28 , H04N13/271
CPC classification number: H04N13/271 , G06T7/155 , G06V10/28 , G06T2207/10048 , G06V2201/07
Abstract: The present invention discloses a method for infrared small target detection based on a depth map in a complex scene, and belongs to the field of target detection. An infrared image is collected, the image is binarized by using priori knowledge of a to-be-detected target and adopting a pixel value method, the binary image is further limited based on deep priori knowledge, then static and dynamic scoring strategies are formulated to score a candidate connected component in the morphologically processed image, and an infrared small target in a complex scene is detected finally. The method can screen out targets within a specific range, has high reliability; has strong robustness; is simple in program and easy to implement, can be used in sea, land, and air, and has a significant advantage under a complex jungle background.
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公开(公告)号：US11908152B2
公开(公告)日：2024-02-20
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
CPC classification number: G06T7/596 , G06T7/85 , G06T2207/10012 , G06T2207/20228
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：US11900634B2
公开(公告)日：2024-02-13
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
CPC classification number: G06T7/80 , G06V10/75 , H04N17/002
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：US11823363B2
公开(公告)日：2023-11-21
申请号：US17282597
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Risheng Liu , Xin Fan , Jinyuan Liu , Wei Zhong , Zhongxuan Luo
CPC classification number: G06T5/50 , G06T3/4053 , G06T5/10 , G06T7/30 , G06T7/80 , G06T7/90 , G06T2207/10024 , G06T2207/10048 , G06T2207/20221
Abstract: The present invention provides an infrared and visible light fusion method, and belongs to the field of image processing and computer vision. The present invention adopts a pair of infrared binocular camera and visible light binocular camera to acquire images, relates to the construction of a fusion image pyramid and a significant vision enhancement algorithm, and is an infrared and visible light fusion algorithm using multi-scale transform. The present invention uses the binocular cameras and NVIDIATX2 to construct a high-performance computing platform and to construct a high-performance solving algorithm to obtain a high-quality infrared and visible light fusion image. The present invention constructs an image pyramid by designing a filtering template according to different imaging principles of infrared and visible light cameras, obtains image information at different scales, performs image super-resolution and significant enhancement, and finally achieves real-time performance through GPU acceleration.
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公开(公告)号：US11210803B2
公开(公告)日：2021-12-28
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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