Filter estimation maximization algorithm for image segmentation

Author

Cherifi, H. ; Grisel, R.

Author_Institution

TSI, CNRS, Saint Etienne, France

Volume

v

fYear

1994

fDate

19-22 Apr 1994

Abstract

In this paper we present an EM based algorithm tailored to image segmentation. This algorithm, which incorporates a filtering step increases the convergence rate and improves the classification process. It is called filter EM (FEM). After a brief theoretical introduction of the algorithm we show applications and improvements on synthetic and real data for the two aspects which are the undersampling of the probability density function and the filtering effect on the probability images obtained

Keywords

convergence of numerical methods; filtering theory; image classification; image sampling; image segmentation; iterative methods; maximum likelihood estimation; probability; convergence rate; filter EM; filter estimation maximization algorithm; filtering effect; image classification; image segmentation; iterative estimation algorithm; maximum likelihood estimation; probability density function; probability images; real data; synthetic data; undersampling; Computational efficiency; Convergence; Filtering algorithms; Image segmentation; Information filtering; Information filters; Iterative algorithms; Maximum likelihood estimation; Numerical analysis; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389430

Filename

389430