Title :
Gibbs sampling via neural network probability estimation
Author :
Hwang, Jenq-Neng ; Lippman, Alan ; Chen, Eric Tsung-Yen
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
The authors propose a neural network approach an efficient nonparametric approach, for Markov random field (MRF) modeling to provide a good estimate of Bayesian a posteriori probability. The approach overcomes the difficulties encountered in estimating the parameters of the Gibbs distribution that characterizes the MRFs and the underlying texture. Its successful application to textured image segmentation using the Gibbs sampling technique is shown
Keywords :
Bayes methods; Markov processes; image segmentation; image texture; neural nets; Bayesian a posteriori probability; Gibbs sampling; Markov random field modelling; neural network probability estimation; nonparametric approach; textured image segmentation; Image sampling; Image segmentation; Information processing; Lattices; Least squares methods; Markov random fields; Neural networks; Parameter estimation; Pixel; Sampling methods;
Conference_Titel :
Signals, Systems and Computers, 1992. 1992 Conference Record of The Twenty-Sixth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-3160-0
DOI :
10.1109/ACSSC.1992.269233
