ICIP 2006, Atlanta, GA

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Paper Detail

Session:Active-Contour Diffusion and Level-Set-Based Methods
Time:Monday, October 9, 09:40 - 12:20
Presentation: Poster
Topic: Image & Video Segmentation: Texture segmentation
Authors: Sang-Mook Lee; Virginia Tech 
 A. Lynn Abbott; Virginia Tech 
 Neil A. Clark; USDA, Forest Service 
 Philip A. Araman; USDA, Forest Service 
Abstract: This paper presents a new diffusion scheme on statistical manifolds for the detection of texture boundaries. The technique derives from our previous work, in which 2-dimensional Riemannian manifolds were statistically defined by maps that transform a parameter domain onto a set of probability density functions. In the earlier approach, a modified Kullback-Leibler divergence, measuring dissimilarity between two density distributions, was added to the statistical manifolds so that a geometric interpretation of the manifolds becomes possible. Although the previous framework produced good segmentation results, the approach led to offsets in texture boundaries for some situations. This paper introduces a diffusion scheme on statistical manifolds that leads to substantially improved localization accuracy in segmentation of textured images.