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.

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

IPC: G06T3/40 ,  G06T7/80 ,  G06T5/40

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.

公开(公告)号：US11501435B2

公开(公告)日：2022-11-15

申请号：US17112623

申请日：2020-12-04

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Rui Xu ,  Xinchen Ye ,  Haojie Li ,  Lin Lin

IPC: G06T7/40 ,  G06T7/00

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.

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

IPC: G06K9/00 ,  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.