DocumentCode :
3000983
Title :
Curvature and singularity driven diffusion for oriented pattern enhancement with singular points
Author :
Qijun Zhao ; Lei Zhang ; Zhang, Dejing ; Wenyi Huang ; Jian Bai
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
fYear :
2009
fDate :
20-25 June 2009
Firstpage :
2129
Lastpage :
2135
Abstract :
Oriented patterns, e.g. fingerprints, consist of smoothly varying flow-like patterns, together with important singular points (i.e. cores and deltas) where the orientation changes abruptly. Gabor filters and anisotropic diffusion methods have been widely used to enhance oriented patterns. However, none of them can well cope with regions of varying curvatures or regions surrounding singular points. By incorporating the ridge curvatures and the singularities into the diffusion model, we propose a new diffusion method to better exploit the global characteristics of oriented patterns. Specifically, we first locate the singular points, and regularize the estimated orientation field by using a singularity driven nonlinear diffusion process. We then enhance the oriented patterns by applying an oriented diffusion process which is driven by the curvature and singularity. Experiments on synthetic data and real fingerprint images validated that the proposed method is capable of consistently enhancing oriented patterns while well preserving the ridge structures in singular regions.
Keywords :
Gabor filters; image enhancement; Gabor filters; anisotropic diffusion; estimated orientation field; oriented pattern enhancement; ridge curvatures; Anisotropic magnetoresistance; Computer vision; Contracts; Diffusion processes; Fingerprint recognition; Frequency estimation; Gabor filters; Image matching; Pattern recognition; Smoothing methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
ISSN :
1063-6919
Print_ISBN :
978-1-4244-3992-8
Type :
conf
DOI :
10.1109/CVPR.2009.5206490
Filename :
5206490
Link To Document :
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