Abstract—Domain knowledge of real life problems are often uncertain, imprecise and inexact, therefore create difficulty in decision making while solving by conventional approaches. Among various methods of handling uncertainties, fuzzy logic has been most intensively studied almost over four decades. Fuzzy logic (FL) explores human reasoning power using linguistic terms, which are modeled as type-1 fuzzy sets and represented by membership functions (MF). However, the MF of type-1 fuzzy set is crisp and cannot tackle all kind of uncertainties. Introducing type-2 fuzzy sets, an extension of type-1 simplifies the problem where the MF is itself fuzzy with three dimension representations. Moreover, a type-2 fuzzy set maps elements of a crisp domain to type-1 fuzzy numbers bounded in the range [0, 1]. Research on type-2 fuzzy sets is still in its infancy and very recently has been applied on emerging areas that need development of more efficient and robust systems. The aim of the review paper is to describe type-2 fuzzy systems for managing uncertainties, presenting the frontier research areas where type-2 fuzzy logic has been applied and proposes algorithm on application of type-2 fuzzy sets in color image segmentation.

Index Terms—Fuzzy Logic, Membership Function, Foot-Print of Uncertainty, Interval Type-2 fuzzy sets.

Saikat Miaty is with the department of the Dr.B.C.Roy Engineering College, in the department of Information Technology, Durgapur – 6 India,phone: +919932252306 ; fax: +91343 250 3121
Jaya Sil is with Bengal Engineering Science & University, Howrah,India.She is now Head of the department in the Department of Computer Science and Engineering in B.E.S.U and also the member of IEEE.

Cite: Saikat Maity and Jaya Sil, "Color Image Segmentation using Type-2 Fuzzy Sets," International Journal of
Computer and Electrical Engineering vol. 1, no. 3, pp. 376-383, 2009.