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11.发明授权公开(公告)号：US11350073B2
公开(公告)日：2022-05-31
申请号：US17283119
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Xin Fan , Risheng Liu , Zhuoxiao Li , Wei Zhong , Zhongxuan Luo
IPC: H04N13/156 , H04N13/239 , H04N13/246 , G06T7/11 , G06T7/80 , H04N13/15
Abstract: The present invention discloses a disparity image stitching and visualization method based on multiple pairs of binocular cameras. A calibration algorithm is used to solve the positional relationship between binocular cameras, and the prior information is used to solve a homography matrix between images; internal parameters and external parameters of the cameras are used to perform camera coordinate system transformation of depth images; the graph cut algorithm has high time complexity and depends on the number of nodes in a graph; the present invention divides the images into layers, and solutions are obtained layer by layer and iterated; then the homography matrix is used to perform image coordinate system transformation of the depth images, and a stitching seam is synthesized to realize seamless panoramic depth image stitching; and finally, depth information of a disparity image is superimposed on a visible light image.
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公开(公告)号：US11170502B2
公开(公告)日：2021-11-09
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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公开(公告)号：US11830222B2
公开(公告)日：2023-11-28
申请号：US17282662
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: G06T7/80 , G06T5/50 , G06T7/90 , G06T2207/10024 , G06T2207/10048 , G06T2207/20221
Abstract: The present invention proposes a bi-level optimization-based infrared and visible light fusion method, adopts a pair of infrared camera and visible light camera to acquire images, and relates to the construction of a bi-level paradigm infrared and visible light image fusion algorithm, which is an infrared and visible light fusion algorithm using mathematical modeling. Binocular cameras and NVIDIA TX2 are used 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 system is easy to construct, and the input data can be acquired by using stereo binocular infrared and visible light cameras respectively; the program is simple and easy to implement; and the fusion image is divided into an image domain and a gradient domain for fusion by means of mathematical modeling according to different imaging principles of infrared and visible light cameras.
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公开(公告)号：US11315318B2
公开(公告)日：2022-04-26
申请号：US17278583
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
IPC: G06T17/05 , H04N13/239
Abstract: The present invention discloses a method for constructing a grid map by using a binocular stereo camera. A high-performance computing platform is constructed by using a binocular camera and a GPU, and a high-performance solving algorithm is constructed to obtain a high-quality grid map containing three-dimensional information. The system in the present invention is easy to construct, so the input data may be collected by using the binocular stereo camera; the program is simple and easy to implement. According to the present invention, the grid height is calculated by using spatial prior information and statistical knowledge, so that a three-dimensional result is more robust; and according to the present invention, the adaptive threshold of grids is solved by using spatial geometry, filtering and screening of the grids are completed, and thus the generalization ability and robustness of the algorithm are improved.
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公开(公告)号：US11783446B2
公开(公告)日：2023-10-10
申请号：US17282635
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Wei Zhong , Yuankai Xiang , Haojie Li , Zhihui Wang , Risheng Liu , Xin Fan , Zhongxuan Luo
CPC classification number: G06T3/4038 , G06T5/40 , G06T7/80 , G06T2207/20021 , G06T2207/20221
Abstract: The present invention discloses a large-field-angle image real-time stitching method based on calibration. First, a calibration algorithm is used to solve the positional relationship between cameras, and the prior information is used to solve a homography matrix between images. The system is easy to build, and the program is simple and easy to implement; an overlapping area ROI of images can be calculated by the homography matrix between images, and an energy model thereof can be built and solved with a graph cut algorithm; the graph cut algorithm has high time complexity and depends on the number of nodes in a graph; here, images are divided into layers, and solutions are obtained layer by layer and iterated; and finally, a stitched image is further optimized by simple linear fusion of stitching seams and histogram equalization of the stitched image.
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Patent Agency Ranking
16.发明授权公开(公告)号：US11238602B2
公开(公告)日：2022-02-01
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
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：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：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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19.发明授权公开(公告)号：US11295168B2
公开(公告)日：2022-04-05
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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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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