Image Restoration and Segmentation by Constrained Optimization 3
نویسنده
چکیده
(C) 1998 IEEE Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Abstract The combinatorial optimization problem of MAP estimation is converted to one of constrained real optimization and then solved by using the proposed augmented Lagrange-Hoppeld (ALH) method. The ALH eeectively overcomes instabilities that are inherent in the penalty method or the Lagrange multiplier method in constrained optimization. It produces good solutions with reasonable costs.
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Bayesian Image Restoration and Segmentationby Constrained
A constrained optimization method, called the Lagrange-Hoppeld (LH) method, is presented for solving Markov random eld (MRF) based Bayesian image estimation problems for restoration and segmentation. The method combines the augmented Lagrangian mul-tiplier technique with the Hoppeld network to solve a constrained optimization problem into which the original Bayesian estimation problem is reform...
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