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Diffusion on Statistical Manifolds

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4 Author(s)
Lee, S. ; Bradley Dept. of Electr. and Comput. Eng., Virginia Polytech. Inst. and State Univ., Blacksburg, VA, USA ; Abbott, A.L. ; Clark, N.A. ; Araman, P.A.

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.

Published in:

Image Processing, 2006 IEEE International Conference on

Date of Conference:

8-11 Oct. 2006