Abstract
Finding an optimal threshold in order to segment digital images is a difficult task in image processing. Numerous approaches to image thresholding already exist in the literature. In this work, a reinforced threshold fusion for image binarization is introduced which aggregates existing thresholding techniques. The reinforcement agent learns the optimal weights for different thresholds and segments the image globally. A fuzzy reward function is employed to measure object similarities between the binarized image and the original gray-level image, and provide feedback to the agent. The experiments show that promising improvement can be obtained. Three well-established thresholding techniques are combined by the reinforcement agent and the results are compared using error measurements based on ground-truth images.
Original language | English (US) |
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Pages (from-to) | 174-181 |
Number of pages | 8 |
Journal | Applied Soft Computing Journal |
Volume | 8 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2008 |
Keywords
- Image thresholding
- Reinforcement learning
- Threshold fusion
ASJC Scopus subject areas
- Software