ICM Method for Multi-Level Thresholding Using Maximum Entropy Criterion

Authors:
Xiping Luo;Jie Tian
Affiliations:
-;-
Venue:
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Year:
1999

Citing 0
Cited 4

Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization

Signal Processing
A novel fuzzy classification entropy approach to image thresholding

Pattern Recognition Letters
A novel fuzzy classification entropy approach to image thresholding

Pattern Recognition Letters
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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Abstract

The maximum entropy approach is one of the most important threshold selection methods. Many authors avoid the computationally prohibitive problem when the maximum entropy criterion is applied to multi-level threshold selection. This paper deals with this problem using ICM method. Experiments show ICM can get satisfactory if not optimal results using our initialization algorithm and solve the problem of computation excellently. Also in this paper, we have modified the fuzzy maximum entropy criterion of Cheng et al. In addition, we compared our ICM method with the simulated annealing algorithm proposed by them.