:Road-surface microtexture (sub-millimeter

This paper presents a self-learning system for automatic texture characterization and classification on ceramic pastes or fabrics and surfaces. The system uses Gabor filter as pre-processing methods with feature extraction possibilities. On these features it applies a linear discriminant analysis (LDA) and k-nearest neighbor classifiers (k-NN) with its best parameters. Experimental results of the recognition ceramic materials, deals on the field and in the laboratory, for different ceramic pastes and surfaces show a good accuracy and applicability of the process on this type of data.

For a transmission at 60 GHz inside the buildings, the models of propagation currently developed do not take into account the 3D roughness of surfaces under consideration. In this paper, we deal with the evaluation of the 3D roughness of surfaces in this kind of environment. An indoor environment includes different types of surfaces but the most representative of 3D roughness are walls, ceiling and floor. We propose a method to characterise the 3D roughness of these surfaces by constructing an image space made up of the original image, the image of gradient, the image of curvature and the image of the angles between the perpendicular to the grey level surface and the perpendicular to the whole image. The method we have developed is based, first, on the study of correlation variations of our image space, and second on a frequency analysis of the angle image histograms. The elaborated criteria allowed us to classify the surfaces studied.

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