Abstract:In this paper we propose a set of measures to model the concept of homogeneity in path-based image segmentation. We introduce the idea of path homogeneity as the aggregation of resemblances between consecutive pixels in the path. This resemblance is obtained from a measure of resemblance between neighbour pixels. In order to aggregate these resemblance values we propose the use of certain families of t-norms that verify a set of intuitive properties. We have studied the performance and behaviour of these functions through a set of experiments. Finally, we have applied these proposals to obtain fuzzy segmentations from real images.Keywords: image segmentation; fuzzy segmentation; path-based segmentation; fuzzy connectivity; fuzzy colour homogeneity.Reference to this paper should be made as follows: Chamorro-Martínez, J., Sánchez, D., Prados-Suárez, B. and Galán-Perales, E. He has participated and is currently member of the teams of several projects, and he has published more than 30 papers in international journals and conferences. His current main research interests are in the fields of knowledge discovery and data mining, relational databases, information retrieval, fuzzy sets theory and soft computing.

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Abslmcf-In this paper we introduce a methodology for the segmentation of colour images by means of a nested hierarchy of fuzzy partitions. Colour image segmentation attempts to divide the pixels of an image in several homogeneously-coloured and topologically connected groups, called regions. Our methodology deals with the different (hut related) aspects of imprecision that are present in this process. First, the concept of homogeneity in a colour space is imprecise, so a measure of distancdsimilarity hetween colonrs is introduced. As a direct consequence, boundaries between regions are imprecise in general, so it is convenient to define regions as fuzzy subsets of items. The proposed distance in a perceptual colour space is employed to calculate fuzzy regions and membership degrees. In addition, fuzzy segmentation can he different depending on the precision level we consider when looking for homogeneity. Starting from an initial fuzzy segmentation, a hierarchical approach, based on a similarity relation between regions, is employed to obtain a nested hierarchy of regions at different precision levels.

Abstract.In this paper we model the concept of "coarseness", typically used in texture image descriptions, by means of fuzzy sets. Specifically, we relate representative measures of this kind of texture with its presence degree. To obtain these "presence degrees", we collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using this collected data, and some statistics as reference set, the membership function corresponding to the fuzzy set "coarseness" is modelled.

Abstract. Texture is a visual feature frequently used in image analysis that has associated certain vagueness. However, the majority of the approaches found in the literature do not either consider such vagueness or they do not take into account human perception to model the related uncertainty. In this paper we model the concept of "coarseness", one of the most important textural features, by means of fuzzy sets and considering the way humans perceive this kind of texture. Specifically, we relate representative measures of coarseness with its presence degree. To obtain these "presence degrees", we collect assessments from polls filled by human subjects, performing an aggregation of such assessments. Thus, the membership function corresponding to the fuzzy set "coarseness" is modelled by using as reference set the representative measures and the aggregated data.