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On the Use of Gradient Space Eigenvalues for Rotation Invariant Texture Classification

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On the Use of Gradient Space Eigenvalues for Rotation Invariant Texture Classification
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.
Mike J. Chantler, Ged McGunnigle
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2000
Where ICPR
Authors Mike J. Chantler, Ged McGunnigle
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