A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
ICM Method for Multi-Level Thresholding Using Maximum Entropy Criterion
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Thresholding using two-dimensional histogram and fuzzy entropy principle
IEEE Transactions on Image Processing
Quantization from Bayes factors with application to multilevel thresholding
Pattern Recognition Letters
A Reinforcement Learning Framework for Parameter Control in Computer Vision Applications
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
A reinforcement agent for threshold fusion
Applied Soft Computing
An efficient iterative algorithm for image thresholding
Pattern Recognition Letters
A reinforcement agent for object segmentation in ultrasound images
Expert Systems with Applications: An International Journal
Closed-loop learning of visual control policies
Journal of Artificial Intelligence Research
International Journal of Applied Metaheuristic Computing
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Traditional maximum entropy-based thresholding methods are very popular and efficient in the case of bilevel thresholding. But they are very computationally expensive when extended to multilevel thresholding since the inevitable exhaustive search of optimal thresholds needed to maximize the posterior entropy. In this paper, a reinforcement learning (RL) approach is proposed for the maximum entropy thresholding. We show that finding the optimal thresholds using the maximum entropy criterion is equivalent to learning an optimal policy of the RL problem. Therefore, the powerful Q-learning algorithm, which is widely used in RL, can be employed to eradicate the computation burden of the maximum entropy-based thresholding methods. The experimental results show that the proposed method is suitable in the case of multilevel thresholding and the performance is better than that of the genetic algorithm-based entropy thresholding method.