0.086791038513181112113SRSOTHERPublished to Web1Publication2Informally Refereed (Peer-Reviewed)53Miscellaneous Publication<![CDATA[Active contours on statistical manifolds and texture segmentaiton]]> 2005-1A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2- dimensional Riemannian manifolds that are statistically defined by maps that transform a parameter domain onto-a set of probability density functions. In this novel framework, color or texture features are measured at each Image point and their statistical characteristics are estimated. This is different from statistical representation of bounded regions. A modified Kullback-Leibler divergence, that measures dissimilarity between two density distributions, is added to the statistical manifolds so that a geometric interpretation of the manifolds becomes possible. With this framework, we can formulate a metric tensor on the statistical manifolds. Then, a geodesic active contour is evolved with the aid of the metric tensor. We show that the statistical manifold framework provides more robust and accurate texture segmentation results.https://www.srs.fs.usda.gov/pubs/ja/ja_lee008.pdf802 KBhttps://www.fs.usda.gov/treesearch/pubs/25462254620IEEE: 828-8310T0Lee, Sang-Mook; Abbott, A. Lynn; Clark, Neil A.; Araman, Philip A.; 03-JAN-2007 09:43:4803-JAN-2007 12:32:45AYLee, Sang-Mook001Abbott, A. Lynn002Clark, Neil A.003Araman, Philip A.SRS4704paraman2536014Lee, Sang-Mook001Abbott, A. Lynn002Clark, Neil A.003Araman, Philip A.SRS4703paraman2536014This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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