Multiresolution segmentation of range images based on Bayesian decision theory

Pierre Boulanger; Guy Godin

doi:10.1117/12.131542

1 November 1992 Multiresolution segmentation of range images based on Bayesian decision theory

Pierre Boulanger, Guy Godin

Proceedings Volume 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision; (1992) https://doi.org/10.1117/12.131542
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

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.

Citation Download Citation

Pierre Boulanger and Guy Godin "Multiresolution segmentation of range images based on Bayesian decision theory", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); https://doi.org/10.1117/12.131542

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