Title :
Image Processing Algorithms Based on Finite-State Gibbs Models
Author :
Vasyukov, Vasily N.
Author_Institution :
Novosibirsk State Tech. Univ.
Abstract :
Gibbs (Markov) random fields are used as stochastic picture models in image processing because of their conceptual simplicity and due to the fact that Gibbs models are fit to synthesize algorithms based on Bayes approach. In this paper, we are concerned with Gibbs fields taking on values from finite sets. This restriction allows to overcome difficulties in estimating Gibbs distribution parameters and to synthesize some useful algorithms of image processing.
Keywords :
Bayes methods; Markov processes; image processing; Bayes approach; Gibbs distribution parameters; Gibbs random fields; Markov process; finite sets; finite-state Gibbs models; image processing algorithms; stochastic picture models; Additive noise; Artificial intelligence; Bayesian methods; Image processing; Intelligent robots; Lattices; Markov random fields; Parameter estimation; Probability; Stochastic processes;
Conference_Titel :
Strategic Technology, The 1st International Forum on
Conference_Location :
Ulsan
Print_ISBN :
1-4244-0426-6
Electronic_ISBN :
1-4244-0427-4
DOI :
10.1109/IFOST.2006.312309
