Q(λ)-based image thresholding

Maryam Shokri, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 1st Canadian Conference on Computer and Robot Vision
Pages504-508
Number of pages5
DOIs
StatePublished - 2004
EventProceedings - 1st Canadian Conference on Computer and Robot Vision - London, Ont, Canada
Duration: May 17 2004May 19 2004

Publication series

NameProceedings - 1st Canadian Conference on Computer and Robot Vision

Conference

ConferenceProceedings - 1st Canadian Conference on Computer and Robot Vision
Country/TerritoryCanada
CityLondon, Ont
Period5/17/045/19/04

Keywords

  • Image processing
  • Image thresholding
  • Q(λ)
  • Reinforcement learning

ASJC Scopus subject areas

  • Engineering(all)

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