机构

• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
• 软件研究所 [1]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [2]
• 期刊论文 [1]

发表日期

• 2011 [1]
• 2007 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Computation of Level-Set Components From Level Lines 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2011, 卷号: 20, 期号: 10, 页码: 2722-2729 作者:  Song, Yuqing;  Chen, Xilin;  Ma, Zhiguo   |  收藏  |  浏览/下载：20/0  |  提交时间：2019/12/16 Contour tree  level line tree  level set  image representation   a new invariant descriptor for shape representation and recognition 会议论文  OAI收割 IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, HI, APR 01-05, Wang Xiying; Dai Guozhong   |  收藏  |  浏览/下载：16/0  |  提交时间：2011/06/29 contour representation  invariant descriptor  polygon Fourier descriptor  polygonal approximation  shape recognition  shape representation  static gesture recognition  Fourier transforms  feature extraction  gesture recognition  image representation  object recognit   A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Hu S.; Zhu M.; Wu C.; Song H.-J. 收藏  |  浏览/下载：18/0  |  提交时间：2013/03/25 In many computer vision tasks  in order to improve the accuracy and robustness to the noise  wavelet analysis is preferred for the natural multi-resolution property. However  the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour  the Zernike moments are introduced  and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours  and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments  consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image  which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient  precise  and robust.