Image Anisotropic Diffusion Based on Gradient Vector Flow Fields.

Yu, H. and Chua, C.-s., 2004. Image Anisotropic Diffusion Based on Gradient Vector Flow Fields. In: Computer Vision - ECCV 2004. Berlin / Heidelberg: Springer, pp. 288-301.

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Official URL: http://www.springerlink.com/content/f5ph3vwarardaa...

DOI: 10.1007/b97871

Abstract

In this paper, the gradient vector flow fields are introduced in the image anisotropic diffusion, and the shock filter, mean curvature flow and Perona-Malik equation are reformulated respectively in the context of this flow fields. Many advantages over the original models can be obtained, such as numerical stability, a large capture range, and computational simplification etc. In addition, the fairing process is introduced in the anisotropic diffusion, which contains the fourth order derivative and is reformulated as the intrinsic Laplacian of curvature under the level set framework. By this fairing process, the boundaries of shape will become more outstanding. In order to overcome numerical errors, the intrinsic Laplacian of curvature is computed from the gradient vector flow fields, but not directly from the observed images.

Item Type:Book Section
ISBN:978-3-540-21982-8
Series Name:Lecture Notes in Computer Science
Number:3023
Number of Pages:288
Series Name:Lecture Notes in Computer Science
Additional Information:8th European Conference on Computer Vision, Prague, Czech Republic, 11-14 May 2004. Proceedings, Part III
Subjects:Generalities > Computer Science and Informatics
Group:Media School > National Centre for Computer Animation
ID Code:14716
Deposited By:Mr Hongchuan Yu
Deposited On:21 May 2010 12:15
Last Modified:07 Mar 2013 15:29
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