An α-cut Automatic Set based on Fuzzy Binarization Using Fuzzy Logic

Title & Authors
An α-cut Automatic Set based on Fuzzy Binarization Using Fuzzy Logic
Lee, Ho Chang; Kim, Kwang Baek; Park, Hyun Jun; Cha, Eui-Young;

Abstract
Image binarization is a process to divide the image into objects and backgrounds, widely applied to the fields of image analysis and its recognition. In the existing method of binarization, there is some uncertainty when there is insufficient brightness gap between objects and backgrounds in setting threshold. The method of fuzzy binarization has improved the features of objects efficiently. However, since this method sets $\small{{\alpha}}$-cut value statically, there remain some problems that important features of objects can be lost during binarization. Therefore, in this paper, we propose a binarization method which does not set $\small{{\alpha}}$-cut value statically. The proposed method uses fuzzy membership functions calculated by thresholds of mean, iterative, and Otsu binarization. Experiment results show the proposed method binaries various images with less loss than the existing methods.
Keywords
Image Processing;Fuzzy Binarization;Fuzzy Logic;Fuzzy Arithmetic Operation;Image Enhancement;
Language
Korean
Cited by
1.
An adaptive Fuzzy Binarization, Journal of Korean Institute of Intelligent Systems, 2016, 26, 6, 485
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