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Technical Program
Paper Detail
| Paper: | MA-P1.11 |
| Session: | Active-Contour Diffusion and Level-Set-Based Methods |
| Time: | Monday, October 9, 09:40 - 12:20 |
| Presentation: |
Poster
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| Title: |
DIFFUSION ON STATISTICAL MANIFOLDS |
| 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. |
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