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Deep Space Probing for Point Cloud Analysis 会议论文
线上, 2021-1
作者:  Yirong, Yang;  Bin Fan;  Yongcheng Liu;  Hua Lin;  Jiyong Zhang
收藏  |  浏览/下载：69/0  |  提交时间：2021/05/27
Boosting Local Shape Matching for Dense 3D Face Correspondence 会议论文
Long Beach, California, June 16 - June 20
作者:  Zhenfeng, Fan;  Xiyuan, Hu;  Chen, Chen;  Silong, Peng
收藏  |  浏览/下载：49/0  |  提交时间：2019/11/11
Correspondence Between Multiscale Frame Shrinkage and High-Order Nonlinear Diffusion 会议论文
Conference on Noncommutative Analysis and Partial Differential Equations, 2016, London, United kingdom, 2016-04-11
作者:  Wang, Haihui;  Huang, Qi;  Meng, Bo
收藏  |  浏览/下载：1/0  |  提交时间：2019/12/30
Encoding the Models with Object-aware Feature Basis: A New Analytical Tool for Graphic Applications 会议论文
2018 7TH INTERNATIONAL CONFERENCE ON DIGITAL HOME (ICDH 2018), 2018-01-01
作者:  Li, Nannan;  Li, Haohao;  Hu, Jiangbei;  Wang, Shengfa;  Su, Zhixun
收藏  |  浏览/下载：6/0  |  提交时间：2019/12/02
Design and Analysis of a Novel Bionic Cardiac Simulator 会议论文
Wuyishan, China, August 11-13, 2018
作者:  Wang CY(王重阳);  Cao L(曹蕾);  Zhou YY(周圆圆);  Liu H(刘浩);  Zhang C(张诚)
收藏  |  浏览/下载：15/0  |  提交时间：2018/12/27
Optimization on stereo correspondence based on local feature algorithm 会议论文
Chengdu, China, 2017-06-02
作者:  Li, Xiaohan;  Zongxi, Song
收藏  |  浏览/下载：11/0  |  提交时间：2017/12/28
Improved Optimization Based on Graph Cuts for Discrete Energy Minimization 会议论文
瑞典, 2014
作者:  Liu KW(刘康伟);  Zhang JG(张俊格);  Huang KQ(黄凯奇);  Huang KQ(黄凯奇)
收藏  |  浏览/下载：15/0  |  提交时间：2016/06/30
Typology of online consumers motivations and clickstream data 会议论文
Sanya, PEOPLES R CHINA, JAN 02-04, 2014
作者:  Chen, X (Chen, Xi);  Li, SS (Li, Shuang-shuang);  Chen, YW (Chen, Yi-wen)
收藏  |  浏览/下载：16/0  |  提交时间：2018/09/18
Analysis and research of stereo correspondence algorithms 会议论文
作者:  Liu, Zhao-Hui;  Zhou, Liang
收藏  |  浏览/下载：7/0  |  提交时间：2015/03/31
A line mapping based automatic registration algorithm of infrared and visible images 会议论文
5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013
作者:  Ai R(艾锐);  Shi ZL(史泽林);  Xu DJ(徐德江);  Zhang CS(张程硕)
收藏  |  浏览/下载：22/0  |  提交时间：2013/12/26
There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit linesegment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.  

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