Application of opposition-based reinforcement learning in image segmentation

Farhang Sahba, Hamid R. Tizhoosh, Magdy M.M.A. Salama

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

Abstract

In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropriate local values and segment the object. The agent uses an image and its manually segmented version and takes some actions to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal as reward/punishment. The agent uses this information to explore/exploit the solution space. The values obtained can be used as valuable knowledge to fill the Q-matrix. The results demonstrate potential for applying this new method in the field of medical image segmentation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
Pages246-251
Number of pages6
DOIs
StatePublished - 2007
Event2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007 - Honolulu, HI, United States
Duration: Apr 1 2007Apr 5 2007

Publication series

NameProceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007

Conference

Conference2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period4/1/074/5/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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