Showing papers by "Hamid R. Tizhoosh published in 2004"
17 May 2004
TL;DR: This paper introduces a new method for image thresholding using reinforcement learning as an effective way to find the optimal threshold and Q(Λ) is implemented as a learning algorithm to achieve more accurate results.
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.
16 citations