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Gcan: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation 会议论文
Long Beach, CA, USA, 15-20 June 2019
作者:  Ma, Xinhong;  Zhang, Tianzhu;  Xu, Changsheng
收藏  |  浏览/下载：3/0  |  提交时间：2022/06/14
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation 会议论文
USA, 2019
作者:  Jian Liang;  Ran He;  Zhenan Sun;  Tieniu Tan
收藏  |  浏览/下载：110/0  |  提交时间：2019/06/10
Iterative template matching with rotation invariant best-buddies pairs 会议论文
作者:  Chen, Zhuo;  Yang, Yang;  Chen, Weile;  Kou, Qian;  Zhong, Dexing
收藏  |  浏览/下载：6/0  |  提交时间：2019/11/26
The Anti-geometric Attack Digital Watermarking Algorithm Based on Image Normalization of Local Information 会议论文
作者:  Jia Xiaolin;  Yang Zhou;  Qi Yanli;  Shao Liping
收藏  |  浏览/下载：12/0  |  提交时间：2019/12/02
Robust Image Hashing Using Radon Transform and Invariant Features 期刊论文
Radioengineering, 2016, 卷号: Vol.25 No.3, 页码: 556-564
作者:  Liu, YL;  Xin, GJ;  Xiao, Y
收藏  |  浏览/下载：2/0  |  提交时间：2019/12/31
Robust image hashing using radon transform and invariant features 期刊论文
Radioengineering, 2016, 卷号: 25, 期号: 3, 页码: 556-564
作者:  Liu, Yuling*;  Xin, Guojiang;  Xiao, Yong
收藏  |  浏览/下载：3/0  |  提交时间：2019/12/27
基于图像局部特征的康复机器人目标识别方法研究 学位论文
博士: 中国科学院大学, 2015
作者:  聂海涛
收藏  |  浏览/下载：79/0  |  提交时间：2015/11/30
局部仿射不变特征的提取技术研究 学位论文
博士: 中国科学院大学, 2015
作者:  吴伟平
收藏  |  浏览/下载：38/0  |  提交时间：2015/11/30
A shape context based Hausdorff similarity measure in image matching 会议论文
5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013
作者:  Ma TL(马天磊);  Liu YP(刘云鹏);  Shi ZL(史泽林);  Yin J(尹健)
收藏  |  浏览/下载：24/0  |  提交时间：2013/12/26
The traditional Hausdorff measure, which uses Euclidean distance metric (L2 norm) to define the distance between coordinates of any two points, has poor performance in the presence of the rotation and scale change although it is robust to the noise and occlusion. To address the problem, we define a novel similarity function including two parts in this paper. The first part is Hausdorff distance between shapes which is calculated by exploiting shape context that is rotation and scale invariant as the distance metric. The second part is the cost of matching between centroids. Unlike the traditional method, we use the centroid as reference point to obtain its shape context that embodies global information of the shape. Experiment results demonstrate that the function value between shapes is rotation and scale invariant and the matching accuracy of our algorithm is higher than that of previously proposed algorithm on the MEPG-7 database.  
A fast spin images matching method for 3D object recognition 会议论文
5th International Symposium on Photoelectronic Detection and Imaging 2013, Beijing, China, June 25-27, 2013
作者:  Wang MM(王明明);  Hao YM(郝颖明);  Zhu F(朱枫)
收藏  |  浏览/下载：15/0  |  提交时间：2013/12/26

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