Image decomposition with anisotropic diffusion applied to leaf-texture analysis
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Texture analysis is an important field of investigation that has received a great deal of interest from computer vision community. In this paper, we propose a novel approach for texture modeling based on partial differential equation (PDE). Each image f is decomposed into a family of derived sub-images. f is split into the u component, obtained with anisotropic diffusion, and the v component which is calculated by the difference between the original image and the u component. After enhancing the texture attribute v of the image, Gabor features are computed as descriptors. We validate the proposed approach on two texture datasets with high variability. We also evaluate our approach on an important real-world application: leaf-texture analysis. Experimental results indicate that our approach can be used to produce higher classification rates and can be successfully employed for different texture applications.
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