On the use of gradient space eigenvalues for rotation invariant texture classification

M. J. Chantler, G. McGunnigle

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Many image-rotation invariant texture classification approaches have been presented previously. This paper proposes a novel scheme that is surface-rotation invariant. It uses the eigenvalues of a surface's gradient-space distribution as its features. Unlike the partial derivatives, from which they are computed, these eigenvalue features are invariant to surface rotation. First we show that a simple classifier using a single isotropic feature (grey-level standard deviation) is not invariant to surface rotation. Then a practical surface rotation invariant classifier that uses photometric stereo to estimate surface derivatives is developed. Results for both classifiers are presented. © 2000 IEEE.

Original languageEnglish
Pages (from-to)931-934
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume15
Issue number3
Publication statusPublished - 2000

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