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1 February 1992 Boundary detection in textured images using constrained graduated nonconvexity method
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sadiye Gueler and Haluk Derin "Boundary detection in textured images using constrained graduated nonconvexity method", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57065
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