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    Method for constructing grid map by using binocular stereo camera
    1.
    发明授权
    Method for constructing grid map by using binocular stereo camera 有权

    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.

    Method for 3D scene dense reconstruction based on monocular visual slam
    7.
    发明授权
    Method for 3D scene dense reconstruction based on monocular visual slam 有权

    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

    IPC: G06T7/00 ,  G06T7/579 ,  G06T7/246 ,  G06T7/70 ,  G06T17/00

    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.

    Method based on deep neural network to extract appearance and geometry features for pulmonary textures classification
    10.
    发明授权
    Method based on deep neural network to extract appearance and geometry features for pulmonary textures classification 有权

    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

    IPC: G06T7/41 ,  G06N3/08 ,  G06T7/00 ,  G06N3/04

    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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