Title :
A stochastic model for image segmentation involving constrained least squares estimation
Author :
Kaup, André ; Aach, Til
Author_Institution :
Inst. for Commun. Eng., Aachen Univ. of Technol., Germany
fDate :
27 Jun-1 Jul 1994
Abstract :
The aim of the paper is to outline a layered statistical image model suitable for unsupervised image segmentation. The segment internal texture signal is described based on its spatial frequency representation while the image partition is modelled as a sample of a Gibbs/Markov random field. The most likely segmentation is estimated using a maximum a posteriori (MAP) formulation with the unknown parameters being determined by constrained least squares (CLS) estimation
Keywords :
Markov processes; image representation; image segmentation; image texture; least squares approximations; maximum likelihood estimation; random processes; Gibbs random field; Markov random field; constrained least squares estimation; image partition; image segmentation; layered statistical image model; maximum a posteriori formulation; segment internal texture signal; spatial frequency representation; stochastic model; unsupervised image segmentation; Equations; Image coding; Image segmentation; Image texture analysis; Least squares approximation; Markov random fields; Parameter estimation; Shape; Stochastic processes; Surface texture;
Conference_Titel :
Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on
Conference_Location :
Trondheim
Print_ISBN :
0-7803-2015-8
DOI :
10.1109/ISIT.1994.394630
