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Proceedings Paper

Boundary detection in textured images using constrained graduated nonconvexity method
Author(s): Sadiye Gueler; Haluk Derin
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Paper Abstract

This paper addresses the boundary detection problem for textured images using weak continuity constraints on the local statistics by a constrained graduated nonconvexity (CGNC) method. A parameter vector consisting of a set of first- and second-order statistics of the textured image assumed to be a Gaussian Markov random field (GMRF) is estimated locally for each pixel. This vector is then compressed to a single parameter and this parameter is considered as the data value at that pixel. We assume a model for textured images where these data values are allowed to change smoothly within textures of the image and abruptly across texture boundaries. The problem can then be considered as the reconstruction of piecewise smooth parameter surfaces measured in noise. For the solution of this problem, we adopted a weak continuity constraints approach. The weak-membrane is specified by its associated energy function constrained by a line process that organizes the boundaries, and the estimates for the parameter values are obtained by minimizing this energy using a continuation method.

Paper Details

Date Published: 1 February 1992
PDF: 10 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57065
Show Author Affiliations
Sadiye Gueler, Univ. of Massachusetts/Amherst (United States)
Haluk Derin, Univ. of Massachusetts/Amherst (United States)

Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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