NDLI: Feature extraction for texture discrimination via random field models with random spatial interaction

Content Provider	IEEE Xplore Digital Library
Author	Speis, A. Healey, G.
Copyright Year	1992
Abstract	In this paper, we attack the problem of distinguishing textured images of real surfaces using small samples. We first analyze experimental data that results from applying ordinary conditional Markov fields. In the face of the disappointing performance of these models, we introduce a random field with spatial interaction that is itself a random variable (usually referred to as a random field in a random environment). For this class of models, we establish the power spectrum and the autocorrelation function as well-defined quantities, and we devise a scheme for the estimation of related parameters. The new set of features that resulted from this approach was applied to real images. Accurate discrimination was observed even for boxes of size 10/spl times/16.
Sponsorship	IEEE Signal Processing Society
Starting Page	635
Ending Page	645
Page Count	11
File Size	1304562
File Format	PDF
ISSN	10577149
Volume Number	5
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Surface texture Image analysis Algorithm design and analysis Data analysis Autocorrelation Parameter estimation Image restoration Image coding Image texture analysis
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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