Atanassov’s intuitionistic fuzzy sets (A-IFSs) have been used recently to determine the optimal threshold value for gray-level
image segmentation [1]. Atanassov’s intuitionistic fuzzy index values are used for representing the unknowledge/ignorance
of an expert on determining whether a pixel of the image belongs to the background or the object of the image. This optimal
global threshold of the image is computed automatically, regardless of the actual image analysis process.
Although global optimal thresholding techniques give good results under experimental conditions, when dealing with real images
having several objects and the segmentation purpose is to point out some application-specific information, one should use
heuristic techniques in order to obtain better thresholding results.
This paper introduces an evolution of the above mentioned technique intended for use with such images. The proposed approach
takes into account the image and segmentation specificities by using a two-step procedure, with a restricted set of the image
gray-levels.
Preliminary experimental results and comparison with other methods are presented.
Keywords Fuzzy Sets Theory Applications - Atanassov’s Intuitionistic Fuzzy Sets (A-IFSs) - computer Vision - Pattern Recognition - Digital Image Processing