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1 SPIN-ENSMSE - Centre Sciences des Processus Industriels et Naturels 2 PROPICE-ENSMSE - Département PROcédés Poudres, Interfaces, Cristallisation et Ecoulements 3 LGF-ENSMSE - UMR 5307 - Laboratoire Georges Friedel 4 Centre for Image Analysis

Abstract : This paper introduces a new descriptor for characterizing and classifying the pixels of texture images by means of General Adaptive Neighborhoods GANs. The GAN of a pixel is a spatial region surrounding it and fitting its local image structure. The features describing each pixel are then regionbased and intensity-based measurements of its corresponding GAN. In addition, these features are combined with the graylevel values of adaptive mathematical morphology operators using GANs as structuring elements. The classification of each pixel of images belonging to five different textures of the VisTex database has been carried out to test the performance of this descriptor. For the sake of comparison, other adaptive neighborhoods introduced in the literature have also been used to extract these features from: the Morphological Amoebas MA, adaptive geodesic neighborhoods AGN and salience adaptive structuring elements SASE. Experimental results show that the GAN-based method outperforms the others for the performed classification task, achieving an overall accuracy of 97.25% in the five-way classifications, and area under curve values close to 1 in all the five -one class vs. all classes- binary classification problems.

Keywords : Minkowski functionals morphometrical functionals adaptive mathematical morphology Pixel description adaptive neighborhoods





Author: Víctor González-Castro - Johan Debayle - Vladimir Ćurić -

Source: https://hal.archives-ouvertes.fr/



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