基于Hessian矩阵和Gabor函数的局部兴趣点检测

被引:8
作者
文朝辉
路红
机构
[1] 复旦大学计算机科学技术学院
基金
上海市自然科学基金;
关键词
局部特征; 兴趣点检测; Gabor核函数; Hessian矩阵; 图像匹配;
D O I
暂无
中图分类号
TP391.41 [];
学科分类号
080203 ;
摘要
局部特征方法是基于内容的图像与视频检索的重要方法。提出一种新的基于Hessian矩阵和Gabor函数的尺度不变局部特征点检测方法(Hessian-Gabor Detector)。该方法首先利用基于Hessian矩阵的检测子定位图像在空间域上的候选特征点位置,然后用基于Gabor函数的算子检测候选兴趣点在尺度空间的特征尺度,从而获得具有尺度不变特性的局部特征点。实验证明,与DOG、Harris-Laplace等方法相比,计算简单。应用于图像匹配中,能够显著地提高匹配效率。
引用
收藏
页码:15 / 18+22 +22
页数:5
相关论文
共 15 条
[1]   Distinctive image features from scale-invariant keypoints [J].
Lowe, DG .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2004, 60 (02) :91-110
[2]   一种基于Gabor小波的局部特征尺度提取方法 [J].
徐婉莹 ;
黄新生 ;
刘育浩 ;
张巍 .
中国图象图形学报, 2011, 16 (01) :72-78
[3]  
Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention[J] . Tony Lindeberg. &nbspInternational Journal of Computer Vision . 1993 (3)
[4]  
A Performance Evaluationof Local Descriptors. MIKOLAJCZYK K,SCHMID C. Proceedings of theIEEE ComputerSociety Conference on Computer Vision and Pattern Recog-nition(CVPR′03) . 2003
[5]   Scale & affine invariant interest point detectors [J].
Mikolajczyk, K ;
Schmid, C .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2004, 60 (01) :63-86
[6]  
International Energy Outlook 2007 with Projections to 2030. EIA (US Energy Information Administration). http://www.eia.doe.gov///oiaf/aeo/index.html . 2009
[7]  
Indexing based on scale invariant interest points. Mikolajczyk K,Schmid C. Proceedings of the 8th IEEE International Conference on Computer Vision . 2001
[8]  
Object recognition from local scale-invariant features. David G Lowe. Proceedings of the 7th IEEE International Conference on Computer Vision(ICCV ’99) . 1999
[9]  
Scale-space theory: A basic tool for analyzing structures at different scales. Lindeberg T. Journal of Applied Mechanics . 1994
[10]  
A combined corner and edge detector. Harris C,Stephens M. Proceedings of the Fourth Alvey Vision Conference . 1988