Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
The image processing handbook (3rd ed.)
The image processing handbook (3rd ed.)
Fractal Geometry in Digital Imaging
Fractal Geometry in Digital Imaging
Independent Component Analysis of Textures
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Topographic Independent Component Analysis
Neural Computation
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Morphology-based multifractal estimation for texture segmentation
IEEE Transactions on Image Processing
Journal of Computational and Applied Mathematics
Noise tolerant local binary pattern operator for efficient texture analysis
Pattern Recognition Letters
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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.