期刊文献+

基于拟Haar变换的模板匹配算法

Quasi Haar Transform Template Matching Algorithm
下载PDF
导出
摘要 基于正交Haar变换(orthogonal Haar transform,OHT)的模板匹配算法在处理二维图像时采用条形和来替代积分图,从而获得了较高的运行效率,但它要求模板必须是标准大小的,即模板的高和宽必须相等且为2的幂次.为解决OHT算法的这一问题,提出了另一种基于拟Haar变换(quasi Haar transform,QHT)的模板匹配算法,它使用树分解策略来加速非标准模板时的匹配处理.QHT算法不仅能处理非标准模板的情况,也同样能处理标准模板的情况.在标准模板情况下,实验结果表明,QHT算法在低噪声等级时比OHT算法拥有更快的运行速度. Orthogonal Haar transform (OHT) template matching algorithm uses strip sum instead of integral image to achieve better performance in 2D image process, however, it requires a standard template that has the same height and width with the power of 2. To solve this problem in OHT algorithm, the authors propose a quasi Haar transform (QHT) template matching algorithm, which uses tree division strategy to accelerate nonstandard template matching process. The QHT algorithm is applicable to both cases of standard and nonstandard templates. For standard templates, experimental results show that the QHT algorithm can be faster than the OHT algorithm in low noise levels.
出处 《北京工业大学学报》 CAS CSCD 北大核心 2014年第2期278-284,共7页 Journal of Beijing University of Technology
基金 国家自然科学基金资助项目(61175004) 北京市自然科学基金资助项目(4112009) 北京市教委科技发展重点项目(KZ01210005007)
关键词 模板匹配 非标准模板 正交Haar变换(OHT) 拟Haar变换(QHT) 树分解策略 template matching nonstandard template orthogonal Haar transform (OHT) quasi Haar transform (QHT) tree division strategy
  • 相关文献

参考文献13

  • 1WANG Quan, YOU Su-ya. Real-time image matching based on multiple view kernel projection [ C ] Jj IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, June 18-23. Piscataway, NJ: IEEE Computer Society, 2007 : 1-8.
  • 2FITZGIBBON A, WEXLER Y, ZISSERMAN A. Image- based rendering using image-based priors [ C I JJ IEEE International Conference on Computer Vision, Nice, France, October 14-17. Piscataway, NJ: IEEE Computer Society, 2003 : 1176-1183.
  • 3FREEMAN W, JONES T, PASZTOR E. Example-based super-resolution [ J 1. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
  • 4DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform domain collaborative filtering[ J]. IEEE Transactions on Image Process, 2007, 16(8) : 2080-2095.
  • 5BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising [ C ] //IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, June 20-26. Piscataway, N J: IEEE Computer Society, 2005: 60-65.
  • 6ALKHANSARI M G. A fast globally optimal algorithm for template matching using low-resolution pruning [ J ]. IEEE Transactions on Image Process, 2001, 10 (4) : 526-533.
  • 7HEL-OR Y, HEL-OR H. Real time pattern matching using projection kernels [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27 (9): 1430- 1445.
  • 8BEN-ARTZ G, HEL-OR H, HEL-OR Y. The gray-code filter kernels [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29 ( 3 ) : 382- 393.
  • 9TOMBARI F, MATTOCCIA S, STEFANO L D. Full search-equivalent pattern matching with incremental dissimilarity approximations [ J ] IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31 (1) : 129-141.
  • 10OUYANG Wan-li, CHAM W. Fast algorithm for walsh hadamard transform on sliding windows [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32 ( 1 ) : 165-171.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部