Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor Filter Analysis for Texture Segmentation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Shape-based Invariant Texture Indexing
International Journal of Computer Vision
A segmentation quality measure based on rich descriptors and classification methods
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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An effective and efficient texture analysis method, based on a new criterion for designing Gabor filter sets, is proposed. The commonly used filter sets are usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We propose an algorithm for deriving the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range. The resulting filters differ significantly from the traditional ones. We test these maximal harmonic discrimination (MHD) filters in several texture analysis tasks: clustering, recognition, and edge detection. It turns out that the proposed filters perform much better than the traditional ones in these tasks. They can achieve performance similar to that of state-of-the-art, distribution based (texton) methods, while being simpler and more computationally efficient.