Scale and Rotation Invariant Texture Features from the Dual-Tree Complex Wavelet Transform (MA-P1)

Author(s) : Edward Lo (University College, The University of New South Wales, Australia) Mark Pickering (University College, The University of New South Wales, Australia) Michael Frater (University College, The University of New South Wales, Australia) John Arnold (University College, The University of New South Wales, Australia)

Abstract : Image segmentation can be viewed as the process of classifying regions in a picture into groups with common properties (i.e. texture). A difficulty arising is that common texture can be classified differently when viewed at different scales and rotated viewpoints. This paper presents a feature vector based on the DT-CWT (dual-tree complex wavelet transform [1]) that is invariant to scale and rotation. The promising results on image segmentation (without cleaning misclassified regions) demonstrate the suitability of this feature vector in representing texture.

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