Topographic independent component analysis based on fractal theory and morphology applied to texture segmentation

Texture analysis and segmentation is an important area in image processing. One can employ texture segmentation for quality control in processes related to skin-leather, textile or marble/granite industries, for example. In such a context, the topographic independent component analysis (TICA) is presented as a technique for texture segmentation in which the image base is obtained from the mixture matrix of the model by implementing a bank of statistical filters, which are capable to capture the inherent properties of each texture. Indeed, using the energy as topographic criterion, the TICA filter bank exhibits results that are similar to the independent component analysis (ICA) model, as it has been already shown in the literature. In this paper, we show that using energy and morphologic fractal texture descriptors as topographic criterion those results are improved, in the sense that the segmentation error and the amount of filters are reduced, for the same textures.

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