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3D Texture Classification Using the Belief Net of a Segmentation Tree Sinisa Todorovic and Narendra Ahuja
 

Summary: 3D Texture Classification Using the Belief Net of a Segmentation Tree
Sinisa Todorovic and Narendra Ahuja
Beckman Institute for Advanced Science and Technology
University of Illinois at Urbana-Champaign, U.S.A.
{sintod, ahuja}@vision.ai.uiuc.edu
Abstract
This paper presents a statistical approach to 3D texture
classification from a single image obtained under unknown
viewpoint and illumination. Unlike in prior work, in which
texture primitives (textons) are defined in a filter-response
space, and texture classes modeled by frequency histograms
of these textons, we seek to extract and model geometric
and photometric properties of image regions defining the
texture. To this end, texture images are first segmented by
a multiscale segmentation algorithm, and a universal set
of texture primitives is specified over all texture classes in
the domain of region geometric and photometric proper-
ties. Then, for each class, a tree-structured belief network
(TSBN) is learned, where nodes represent the correspond-
ing image regions, and edges, their statistical dependecies.

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering