Title :
Filter estimation maximization algorithm for image segmentation
Author :
Cherifi, H. ; Grisel, R.
Author_Institution :
TSI, CNRS, Saint Etienne, France
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389430
