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Proceedings Paper

Consistent object representation method for computer vision applications
Author(s): Kwanghoon Sohn; Jung H. Kim; Winser E. Alexander
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Paper Abstract

The human visual system uses two-dimensional (2D) boundary information to recognize objects since the shape of the boundary usually contains the pertinent information about an object. Thus, representing a boundary concisely and consistently is necessary for object recognition. In this paper, we propose a consistent object representation method using mean field annealing (MFA) technique for computer vision applications. Since a curvature function computed on a preprocessed smooth boundary, which is obtained by the MFA approach is consistent, we can consistently detect corner points in this curvature function space. Furthermore, the MFA approach preserves the sharpness of corner points very well. Thus, we can detect corner points easier and better with this method than with other existing methods. Ideal corner points rarely exist for a real boundary. They are often rounded due to the smoothing effect of the preprocessing. In addition, a human recognizes both sharp corner points and slightly rounded segments as corner points. Thus, we use `corner sharpness,' which is qualitatively similar to a human's capability of detecting corner points, to increase the robustness of the proposed algorithm.

Paper Details

Date Published: 10 October 1994
PDF: 9 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188889
Show Author Affiliations
Kwanghoon Sohn, Georgetown Univ. Medical Ctr. (South Korea)
Jung H. Kim, North Carolina A&T State Univ. (United States)
Winser E. Alexander, North Carolina State Univ. (United States)


Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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