A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Texture Classification Using Windowed Fourier Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Evaluation of the effects of Gabor filter parameters on texture classification
Pattern Recognition
Fractal dimension applied to plant identification
Information Sciences: an International Journal
Statistical pattern recognition in remote sensing
Pattern Recognition
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Texture analysis and classification using deterministic tourist walk
Pattern Recognition
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Texture is an important visual attribute used to discriminate images. Although statistical features have been successful, texture descriptors do not capture the richness of details present in the images. In this paper we propose a novel approach for texture analysis based on partial differential equations (PDE) of Perona and Malik. Basically, an input image f is decomposed into two components f = u + v, where u represents the cartoon component and v represents the textural component. We show how this procedure can be employed to enhance the texture attribute. Based on the enhanced texture information, Gabor filters are applied in order to compose a feature vector. Experiments on two benchmark datasets demonstrate the superior performance of our approach with an improvement of almost 6%. The results strongly suggest that the proposed approach can be successfully combined with different methods of texture analysis.