Multichannel texture segmentation using bamberger pyramids
Journal on Image and Video Processing
Iris feature extraction and matching based on multiscale and directional image representation
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Adaptive directional wavelet transform based on directional prefiltering
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Extracting and combining multimodal directional iris features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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Classifying textures is a problem that has been considered by many researchers. Many of the high performance methods are based on extracting features from the textures and performing classification in the feature space. In this paper, we consider the application of a new directional filter bank (DFB) to the problem of texture classification. The DFB is used to provide a compact and efficient representation in which fast classification can be performed using classical statistical methods. The resulting method is shown to yield higher performance than feature-based techniques reported previously. Furthermore, the approach has the added attraction that both the computational complexity and storage requirements are relatively low. Experimental comparisons using the Brodatz texture database are also presented.