Image segmentation using A-IFSs

The problem of segmentation in spite of all the work over the last decades, is still an important research field and also a critical preprocessing step for image processing, mostly due to the fact that finding a global optimal threshold that works well for all kind of images is in-deed a near impossible task that, proba-bly, will never be accomplished. During the past years, fuzzy logic the-ory has been successfully applied to im-age thresholding. Moreover, consider-ing that for segmentation purposes, in most cases, image pixels have an in-herent ambiguity in the predicate that they must fulfill in order to belong to an object, which results in the experts uncertainty in assigning the pixel to that object. In the context of fuzzy sets theory, Atanassov’s Intuitionistic fuzzy sets are a relevant and interest-ing extension since, uncertainty is one of the underlying ideas behind this the-ory. In this paper we describe a thresh-olding technique using Atanassov’s in-tuitionistic fuzzy sets (A-IFSs). This approach uses Atanassov’s intuitionis-tic index values for representing the un-certainty of the expert in determining that the pixel belongs to the background or that it belongs to the object. We first introduce the general framework of this approach and then its natural extension to multilevel thresholding. Segmentation experimental results and their performance evaluation for the calculation of one, two and three thresholds are presented