Quaternionic wavelets for texture classification

Authors:
Raphaël Soulard;Philippe Carré
Affiliations:
XLIM-SIC Laboratory, CNRS UMR 6172, University of Poitiers, France;XLIM-SIC Laboratory, CNRS UMR 6172, University of Poitiers, France
Venue:
Pattern Recognition Letters
Year:
2011

Citing 7
Cited 2

Texture analysis

Handbook of pattern recognition & computer vision
Texture classification using wavelet transform

Pattern Recognition Letters
The Theory and Use of the Quaternion Wavelet Transform

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Quaternion wavelet phase based stereo matching for uncalibrated images

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Multiscale texture classification using dual-tree complex wavelet transform

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Bayesian texture classification and retrieval based on multiscale feature vector

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Coherent Multiscale Image Processing Using Dual-Tree Quaternion Wavelets

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Employing quaternion wavelet transform for banknote classification

Neurocomputing
New feature extraction approach for bank note classification using Quaternion Wavelets

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

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Abstract

This article proposes a study of the recent quaternionic wavelet transform (QWT) from a practical point of view through a digital image analysis application. Based on a theoretic 2D generalization of the analytic signal leading to a strong 2D signal modeling, this representation uses actual 2D analytic wavelets and yields subbands having a shift-invariant magnitude and a 3-angle phase, using the quaternion algebra. Our experiment furthers the understanding of this quite sophisticated tool, and shows its practical interest by a clear improvement of a famous wavelet application: texture classification. Thanks to coherent multiscale analysis brought by the QWT we obtain better classification results than with standard wavelets in a similar process.