Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of line segments from fuzzy images
Pattern Recognition Letters
Some new fuzzy entropy formulas
Fuzzy Sets and Systems
Region-based fit of color homogeneity measures for fuzzy image segmentation
Fuzzy Sets and Systems
Image thresholding using type II fuzzy sets
Pattern Recognition
Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 1, Forward Problems
IEEE Transactions on Fuzzy Systems
Interval Type-2 Fuzzy Logic Systems Made Simple
IEEE Transactions on Fuzzy Systems
Fuzzy homogeneity approach to multilevel thresholding
IEEE Transactions on Image Processing
Regions adjacency graph applied to color image segmentation
IEEE Transactions on Image Processing
New geometric inference techniques for type-2 fuzzy sets
International Journal of Approximate Reasoning
Fast fuzzy connected filter implementation using max-tree updates
Fuzzy Sets and Systems
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This paper focuses on application of fuzzy sets of type 2 (FS2) in color images segmentation. The proposed approach is based on FS2 entropy application and region merging. Both local and global information of the image are employed and FS2 makes it possible to take into account the total uncertainty inherent to the segmentation operation. Fuzzy entropy is utilized as a tool to perform histogram analysis to find all major homogeneous regions at the first stage. Then a basic and fast region merging process, based on color similarity and reduction of small clusters, is carried out to avoid oversegmentation. The experimental results demonstrate that this method is suitable to find homogeneous regions for natural images, even for noisy images.