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Proceedings Paper

Optimal sign selection for discriminant analysis of textures
Author(s): Vitalij N. Kurashov; Andry V. Kovalenko; Alexandr G. Chumakov; Alexandr V. Kisil
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Paper Abstract

In this paper, we investigate the problem of optimal (feature) selection for texture recognition for the case, when statistical properties of the image general population are satisfactorally represented by the a prior classified training set of small size (i.e. the number of images in the training set is much smaller then the number of pixels on the image). We examine criteria, defined by the trace norm of the certain self-conjugate operator, constructed in the special manner from the elements of the training set. Karhunen-Loeve expansion, Hoteling criteria, and some of their modifications are considered for recognition of computer generated regular textures, distorted with white noise. Comparative analysis of criteria efficiency is presented for several possible kinds of classification of the training set.

Paper Details

Date Published: 22 August 1995
PDF: 6 pages
Proc. SPIE 2564, Applications of Digital Image Processing XVIII, (22 August 1995); doi: 10.1117/12.217425
Show Author Affiliations
Vitalij N. Kurashov, Kiev Univ. (Ukraine)
Andry V. Kovalenko, Kiev Univ. (Ukraine)
Alexandr G. Chumakov, Kiev Univ. (Ukraine)
Alexandr V. Kisil, Kiev Univ. (Ukraine)


Published in SPIE Proceedings Vol. 2564:
Applications of Digital Image Processing XVIII
Andrew G. Tescher, Editor(s)

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