Bayesian image restoration and segmentation by constrained optimization

Authors:
S. Z. Li;K. L. Chan;H. Wang
Affiliations:
-;-;-
Venue:
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Year:
1996

Citing 0
Cited 4

CoCRF deformable model: a geometric model driven by collaborative conditional random fields

IEEE Transactions on Image Processing
Analysis of nerve activity and optical signals from mouse brain stem to identify cells generating respiratory rhythms

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Deformable probability maps: Probabilistic shape and appearance-based object segmentation

Computer Vision and Image Understanding
An efficient and effective tool for image segmentation, total variations and regularization

SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: A constrained optimization method, called the Lagrange-Hopfield (LH) method, is presented for solving Markov random field (MRF) based Bayesian image estimation problems for restoration and segmentation. The method combines the augmented Lagrangian multiplier technique with the Hopfield network to solve a constrained optimization problem into which the original Bayesian estimation problem is reformulated. The LH method effectively overcomes instabilities that are inherent in the penalty method (e.g. Hopfield network) or the Lagrange multiplier method in constrained optimization. An additional advantage of the LH method is its suitability for neural-like analog implementation. Experimental results are presented which show that LH yields good quality solutions at reasonable computational costs.