Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30812
Title: Curvature and singularity driven diffusion for oriented pattern enhancement with singular points
Authors: Zhao, Q
Zhang, L 
Zhang, D 
Huang, W
Bai, J
Issue Date: 2009
Source: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, p. 2129-2135 How to cite?
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.
Description: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, Miami, FL, 20-25 June 2009
URI: http://hdl.handle.net/10397/30812
ISBN: 9781424439935
DOI: 10.1109/CVPRW.2009.5206490
Appears in Collections:Conference Paper

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