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Image threshold computation by modelizing knowledge/unknowledge by means of Atanassov’s intuitionistic fuzzy sets. (English) Zbl 1147.68084

Bustince, Humberto (ed.) et al., Fuzzy sets and their extensions: representation, aggregation and models. Intelligent systems from decision making to data mining, web intelligence and computer vision. Berlin: Springer (ISBN 978-3-540-73722-3/hbk). Studies in Fuzziness and Soft Computing 220, 621-638 (2008).
Summary: In this chapter, a new thresholding technique using Atanassov’s Intuitionistic Fuzzy Sets (A-IFSs) and restricted dissimilarity functions is introduced. We interpret the intuitionistic fuzzy index of Atanassov as the degree of unknowledge/ignorance of an expert for determining whether a pixel of an image belongs to the background or the object of the image. Under these conditions we construct an algorithm on the basis of A-IFSs for detecting the threshold of an image. Then we present a method for selecting from a set of thresholds of an image the best one. This method is based on the concept of fuzzy similarity. Lastly, we prove that in most cases our algorithm for selecting the best threshold takes the threshold calculated with the algorithm constructed on the basis of A-IFSs.
For the entire collection see [Zbl 1130.68004].

MSC:

68U10 Computing methodologies for image processing
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