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Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes

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2 Author(s)
Porter, R. ; Centre for Commun. Res., Bristol Univ., UK ; Canagarajah, N.

Three novel feature extraction schemes for texture classification are proposed. The schemes employ the wavelet transform, a circularly symmetric Gabor filter or a Gaussian Markov random field with a circular neighbour set to achieve rotation-invariant texture classification. The schemes are shown to give a high level of classification accuracy compared to most existing schemes, using both fewer features (four) and a smaller area of analysis (16×16). Furthermore, unlike most existing schemes, the proposed schemes are shown to be rotation invariant demonstrate a high level of robustness noise. The performances of the three schemes are compared, indicating that the wavelet-based approach is the most accurate, exhibits the best noise performance and has the lowest computational complexity

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Vision, Image and Signal Processing, IEE Proceedings -  (Volume:144 ,  Issue: 3 )