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

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2 Author(s)
Chantler, M.J. ; Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK ; McGunnigle, G.

Many image-rotation invariant texture classification approaches have been presented previously. This paper proposes a novel surface-rotation invariant scheme. 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

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Pattern Recognition, 2000. Proceedings. 15th International Conference on  (Volume:3 )

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