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Proceedings Paper

Qualitative descriptors for digital contour segments (Invited Paper)
Author(s): Kathryn Shaker Baummer; Kie Bum Eom; Murray H. Loew
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Paper Abstract

Earlier studies of the human vision system suggest that a complex object may be recognized from the qualitative features associated with its boundary. Since the human vision system uses qualitative features for object recognition, it is extremely robust to deformation caused by noise, obstruction, scale change, or optical distortion. We considered various qualitative descriptors for digital images. The descriptors developed in this research are based on the estimated curvature function from digital boundaries and can discriminate features such as straightness of a contour segment, perpendicularity of two contour segments, parallelness of two straight contour segments, parallelness of two curved contour segments in a global sense, and the direction of convergence if two straight contour segments are not parallel. We demonstrated that the qualitative descriptors developed in this paper can be applied for identification of elementary shapes, such as cylinders, bricks, cones, etc. We also discussed the recognition of more complex shapes after decomposing them into simpler components.

Paper Details

Date Published: 1 February 1992
PDF: 12 pages
Proc. SPIE 1610, Curves and Surfaces in Computer Vision and Graphics II, (1 February 1992); doi: 10.1117/12.135155
Show Author Affiliations
Kathryn Shaker Baummer, George Washington Univ. (United States)
Kie Bum Eom, George Washington Univ. (United States)
Murray H. Loew, George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 1610:
Curves and Surfaces in Computer Vision and Graphics II
Martine J. Silbermann; Hemant D. Tagare, Editor(s)

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