@inproceedings{e96361cf99a147668810cad7bb32e27f,
title = "Q(λ)-based image thresholding",
abstract = "One of the problems in image processing is finding an appropriate threshold in order to convert an image to a binary one. In this paper we introduce a new method for image thresholding. We use reinforcement learning as an effective way to find the optimal threshold. Q(λ) is implemented as a learning algorithm to achieve more accurate results. The reinforcement agent uses objective rewards to explore/exploit the solution space. It means that there is not any experienced operator involved and the reward and punishment function must be defined for the agent. The results show that this method works successfully and can be trained for any particular application.",
keywords = "Image processing, Image thresholding, Q(λ), Reinforcement learning",
author = "Maryam Shokri and Tizhoosh, {Hamid R.}",
year = "2004",
doi = "10.1109/CCCRV.2004.1301490",
language = "English (US)",
isbn = "0769521274",
series = "Proceedings - 1st Canadian Conference on Computer and Robot Vision",
pages = "504--508",
booktitle = "Proceedings - 1st Canadian Conference on Computer and Robot Vision",
note = "Proceedings - 1st Canadian Conference on Computer and Robot Vision ; Conference date: 17-05-2004 Through 19-05-2004",
}