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Proceedings Paper

Fractal image compression and texture analysis
Author(s): Baback Moghaddam; Kenneth J. Hintz; Clayton V. Stewart
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Paper Abstract

This paper describes a new method for building object models for the purpose of overlapped object recognition. The method relies on local fragments of the boundary to derive a set of autoregressive parameters that serve to detect similar boundary fragments. First a rule based algorithm which detects the occlusion of two or more objects is introduced. This algorithm makes use of aheuristic rule which take into account the number of intersection points of the boundary with a standard invariant shape and of global features (area, perimeter) to confirm the presence of occlusion. The object is then decomposed into visible parts by using first a polygonal approximation method and then the concave vertices obtained at the latter step. The decomposition algorithm prepares the input data for the description of the model and the object through the autoregressive filter method.

Paper Details

Date Published: 1 April 1991
PDF: 16 pages
Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); doi: 10.1117/12.47964
Show Author Affiliations
Baback Moghaddam, George Mason Univ. (United States)
Kenneth J. Hintz, George Mason Univ. (United States)
Clayton V. Stewart, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 1406:
Image Understanding in the '90s: Building Systems that Work
Brian T. Mitchell, Editor(s)

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