A reinforcement agent for threshold fusion

Authors:
Maryam Shokri;Hamid R. Tizhoosh
Affiliations:
Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Ont. N2L 3G1, Canada;Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Ont. N2L 3G1, Canada
Venue:
Applied Soft Computing
Year:
2008

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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.