Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Pattern Recognition Letters
VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
A novel fuzzy classification entropy approach to image thresholding
Pattern Recognition Letters
Object segmentation using ant colony optimization algorithm and fuzzy entropy
Pattern Recognition Letters
A novel fuzzy classification entropy approach to image thresholding
Pattern Recognition Letters
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Multilevel image segmentation with adaptive image context based thresholding
Applied Soft Computing
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An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A novel image thresholding method based on membrane computing and fuzzy entropy
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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Thresholding is a commonly used technique in image segmentation. Selecting the correct thresholds is a critical issue. In this paper, the relationship between a probability partition (PP) and a fuzzy c-partition (FP) in thresholding is given. This relationship and the entropy approach are used to derive a thresholding technique to select the best fuzzy c-partition. The measure of the selection quality is the compatibility between the FP and the PP generated by the problem. An entropy function defined by the PP and FP is used to measure the compatibility. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient algorithm for three-level thresholding is deduced. Experiments to verify the efficiency of the proposed method and comparison to some existing techniques are also presented. The experiment results show that our proposed method gives the best performance in three-level thresholding using fuzzy c-partition