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

fYear

1994

fDate

27 Jun-1 Jul 1994

Firstpage

389

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on

Conference_Location

Trondheim

Print_ISBN

0-7803-2015-8

Type

conf

DOI

10.1109/ISIT.1994.394630

Filename

394630