Abstract
Uncertainty is an inherent part of image segmentation in real world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional image segmentation cannot fully handle the uncertainties. Type-2 fuzzy sets and cloud model can handle such uncertainties in a better way because they provide us with more design degrees of freedom. The paper presents a comparison on the two approaches for image segmentation with uncertainty, that is, image thresholding based on type-2 fuzzy sets and cloud model. Firstly, the theoretical foundations of two methods are analyzed. Secondly, the processing of image segmentation with uncertainty is compared through two stages respectively, which is histogram analysis and optimum threshold selection. Finally, the experiments are divided in three groups, both synthetic and real images are used to investigate the performance of handling uncertainty in image segmentation, and some noisy images are also invol...
Highlights
Image segmentation is often described as the process that subdivides an image into its constituent parts and extracts those parts of interest objects, and it is the first step and one of the most critical tasks of automatic image analysis[1,2]
Interval type-2 fuzzy set is defined by lower membership function (LMF) and upper membership function (UMF)
Interval type-2 fuzzy sets represent the uncertainty of membership function through footprint of uncertainty (FOU) determined by the mathematical functions of UMF and LMF, and use two Comparative Study mathematical functions to solve problems
Summary
Image segmentation is often described as the process that subdivides an image into its constituent parts and extracts those parts of interest objects, and it is the first step and one of the most critical tasks of automatic image analysis[1,2]. Li proposed cloud model in 199513-15, which automatically produces membership grade based on probability measure space The two methods both concentrate on the essentials of uncertainty and have been applied in many fields for more than ten years[12,13,16]. Comparative with traditional methods for image segmentation, these new methods (i.e. ITT2 and ITC) can capture uncertainty and produce good results when the lack of knowledge of the expert, because the new methods introduce the theories, type-2 fuzzy sets or cloud model, into image segmentation, and analyze the histogram with uncertainty.
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