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Proceedings Paper

Multiresolution segmentation of range images based on Bayesian decision theory
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Paper Abstract

This paper describes recent work on hierarchical segmentation of range images. The algorithm starts with an initial partition of small planar regions using a robust fitting method constrained by the detection of depth and orientation discontinuities. From this initial partition represented by an adjacency graph structure, we optimally group these regions into larger and larger regions until an approximation limit is reached. The algorithm uses Bayesian decision theory to determine the local optimal grouping and the geometrical complexity of the approximation surface. This algorithm produces a hierarchical structure that can be used to represent objects with a varying level of detail by scanning through the hierarchical structure generated. Experimental results are presented.

Paper Details

Date Published: 1 November 1992
PDF: 13 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131542
Show Author Affiliations
Pierre Boulanger, National Research Council Canada (Canada)
Guy Godin, National Research Council Canada (Canada)


Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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