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Three-Dimension Maximum Between-Cluster Variance Image Segmentation Method Based on Chaotic Optimization

  • Jiu-Lun Fan
  • Xue-Feng Zhang
  • Feng Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4270)

Abstract

Chaotic optimization is a new optimization technique. For image segmentation, conventional chaotic sequence is not very effective to three-dimension gray histogram. In order to solve this problem, a three-dimension chaotic sequence generating method is presented. Simulation results show that the generated sequence is pseudorandom and its distribution is approximately inside a sphere whose centre is (0.5 , 0.5 , 0.5). Based on this work, we use the proposed chaotic sequence to optimize three-dimension maximum between-variance image segmentation method. Experiments results show that our method has better segmentation effect and lower computation time than that of the original three-dimension maximum between-variance image segmentation method for mixed noise disturbed image.

Keywords

Control Point Image Segmentation Chaotic System Target Class Randomness Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiu-Lun Fan
    • 1
  • Xue-Feng Zhang
    • 1
    • 2
  • Feng Zhao
    • 1
  1. 1.Department of Information and ControlXi’an Institute of Post and TelecommunicationsXi’anP.R. China
  2. 2.Department of Electronic EngineeringXidian UniversityXi’anP.R. China

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