Perceptually-Based Functions for Coarseness Textural Feature Representation

Abstract. Coarseness is a very important textural concept that has been widely analyzed in computer vision for years. However, a model which allows to represent different perception degrees of this textural concept in the same way that humans perceive texture is needed. In this paper we propose a model that associates computational measures to human perception by learning an appropriate function. To do it, different measures representative of coarseness are chosen and subjects assessments are collected and aggregated. Finally, a function that relates these data is fitted

Fuzzy Sets for Modelling Fineness Perception in Texture Images

Abstract — Fineness is a primary texture feature frequently used for image content description. However, it is an ambiguous concept difficult to be characterized. To face this ambiguity, we propose to model the fineness by means of fuzzy sets, relating a representative fineness measure (our reference set) with the human perception of fineness. In our study, a wide variety of measures have been analyzed, defining a fuzzy set for each measure. The fineness perception has been collected from polls filled by human subjects, performing an aggregation of their assessments by means of OWA operators. For a given measure, the corresponding membership function is obtained by fitting the collected data. The performance of each fuzzy set is analyzed and checked with the human assessments, proposing a subgroup of them as the most adequate for modelling fineness perception in texture images

Using Fuzzy Sets for Coarseness Representation in Texture Images

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

Modelling Coarseness in Texture Images by Means of Fuzzy Sets

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

A Hierarchical Approach to Fuzzy Segmentation of

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 (but 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 distance/similarity between colours 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 be 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