Share Email Print
cover

Proceedings Paper

Development of criteria to compare model-based texture analysis methods
Author(s): Young-Sung Soh; S. N. Jayaram Murthy; Terrance L. Huntsberger
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

Texture is an important property useful for image segmentation and the inference of 3-D information in the scene. Many approaches were proposed for analyzing textures. Among them are feature-based approaches and model-based approaches. In a feature-based environment various textural features are extracted from each textured image(or subimage) and are used to classify or discriminate given textures i. e. no explicit consideration of models is taken into account and thus the generation aspect is ignored. In model-based analysis we describe texture in terms of mathematical model which has both analysis and synthesis abilities. In the literature several comparative studies of feature-based methods are found. However few explicit comparative studies of model-based methods have been reported. This paper describes the development of some criteria to compare two model-based texture analysis methods (Time Series model and Markov Random Field model).

Paper Details

Date Published: 1 February 1991
PDF: 13 pages
Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); doi: 10.1117/12.25187
Show Author Affiliations
Young-Sung Soh, Korea Advanced Institute of Science and Technology (South Korea)
S. N. Jayaram Murthy, Central Michigan Univ. (United States)
Terrance L. Huntsberger, Univ. of South Carolina (United States)


Published in SPIE Proceedings Vol. 1381:
Intelligent Robots and Computer Vision IX: Algorithms and Techniques
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top