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Proceedings Paper

Semi-Markov Random Field Models For Texture Synthesis
Author(s): John Goutsias; Jerry M. Mendel
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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)

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