Share Email Print

Proceedings Paper

Semi-Markov Random Field Models For Texture Synthesis
Author(s): John Goutsias; Jerry M. Mendel
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper we focus our attention on modeling nons-tationary texture behavior, thus resulting in accurate models for texture analysis and synthesis. We present a new class of nonstationary signals, the class of semi-Markov random fields. The likelihood function, which uniquely describes the statistical behavior of these random fields, is derived. We examine the validity of our two-dimensional semi-Markov random field models in synthesizing and analyzing textures. The appropriateness of the semi-Markov random field models for synthesizing images similar to real textures is studied and different models are optimally fitted to real textures by the use of a maximum-likelihood procedure.

Paper Details

Date Published: 10 September 1987
PDF: 6 pages
Proc. SPIE 0768, Pattern Recognition and Acoustical Imaging, (10 September 1987); doi: 10.1117/12.940284
Show Author Affiliations
John Goutsias, The Johns Hopkins University (United States)
Jerry M. Mendel, University of Southern California (United States)

Published in SPIE Proceedings Vol. 0768:
Pattern Recognition and Acoustical Imaging
Leonard A. Ferrari, Editor(s)

© SPIE. Terms of Use
Back to Top