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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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2.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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3.发明申请公开(公告)号：US20220036589A1
公开(公告)日：2022-02-03
申请号：US17279461
申请日：2020-03-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Wei ZHONG , Boqian LIU , Haojie LI , Zhihui WANG , Risheng LIU , Xin FAN , Zhongxuan LUO
IPC: G06T7/80 , H04N13/239 , H04N13/156 , H04N13/25 , G06T7/13
Abstract: The present invention discloses a multispectral camera external parameter self-calibration algorithm based on edge features, and belongs to the field of image processing and computer vision. Because a visible light camera and an infrared camera belong to different modes, fewer satisfactory point pairs are obtained by directly extracting and matching feature points. In order to solve the problem, the method starts from the edge features, and finds an optimal corresponding position of an infrared image on a visible light image through edge extraction and matching. In this way, a search range is reduced and the number of the satisfactory matched point pairs is increased, thereby more effectively conducting joint self-calibration on the infrared camera and the visible light camera. The operation is simple and results are accurate.
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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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Patent Agency Ranking
6.发明申请公开(公告)号：US20210390686A1
公开(公告)日：2021-12-16
申请号：US17112623
申请日：2020-12-04
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Rui XU , Xinchen YE , Haojie LI , Lin LIN
Abstract: The invention discloses an unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition, which belongs to the field of image processing and computer vision. This method enables the deep network model of lung texture recognition trained in advance on one type of CT data (on the source domain), when applied to another CT image (on the target domain), under the premise of only obtaining target domain CT image and not requiring manually label the typical lung texture, the adversarial learning mechanism and the specially designed content consistency network module can be used to fine-tune the deep network model to maintain high performance in lung texture recognition on the target domain. This method not only saves development labor and time costs, but also is easy to implement and has high practicability.
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公开(公告)号：US20210390338A1
公开(公告)日：2021-12-16
申请号：US17112367
申请日：2020-12-04
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Rui XU , Xinchen YE , Haojie LI , Lin LIN
Abstract: The invention discloses a deep network lung texture recognition method combined with multi-scale attention, which belongs to the field of image processing and computer vision. In order to accurately recognize the typical texture of diffuse lung disease in computed tomography (CT) images of the lung, a unique attention mechanism module and multi-scale feature fusion module were designed to construct a deep convolutional neural network combing multi-scale and attention, which achieves high-precision automatic recognition of typical textures of diffuse lung diseases. In addition, the proposed network structure is clear, easy to construct, and easy to implement.
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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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9.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：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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