New rotationally invariant texture feature extraction methods are introduced that utilise the dual-tree complex wavelet transform (DT-CWT). The complex wavelet transform is a new technique that uses a dual tree of wavelet filters to obtain the real and imaginary parts of complex wavelet coefficients. When applied in two dimensions the DT-CWT produces shift invariant orientated subbands. Both isotropic and anisotropic rotationally invariant features can be extracted from the energies of these subbands. Using simple minimum distance classifiers, the classification performance of the proposed feature extraction methods were tested with rotated sample textures. The anisotropic features gave the best classification results for the rotated texture tests, outperforming a similar method using a real wavelet decomposition
Sponsorship: This work was supported by the Virtual Centre of
Excellence in Digital Broadcast and Multimedia
Technology. The authors acknowledge the support and
information provided by Dr N.G. Kingsbury of Cambridge
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Name of Conference: International Conference on Image Processing, 2000 (ICIP 2000)
Venue of Conference: Vancouver, BC