Earticle

초록

영어

Multilevel thresholding is one of the most important techniques for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in multilevel thresholding. In this paper, a novel multilevel MET algorithm based on the hybrid of particle swarm optimization (PSO) and Genetic algorithm is presented. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed local optimal solution. To overcome this problem, we used Genetic algorithm. To obtain an optimal solution in Genetic algorithm, operation such as selection, reproduction, and mutation procedures are used to generate next generations. The capability of this hybrid PSO that called HPGT is enhanced by cloning of fitter particles instead of worst particles that is determined based on their fitness values. The performance of HPGT algorithm and PSO algorithm compared. The results show the convergence of the HPGT is very good.

목차

Abstract
 1. Introduction
 2. Multilevel Thresholding Problem Formulation
  2.1 Entropy criterion based on measure
 3. Image thresholding based on PSO and Genetic algorithm
  3.1. PSO Algorithm
  3.2. Genetic Algorithm
  3.3. Proposed method
 4. Experimental Results
 5. Conclusion
 References

키워드

저자

• Elham Akbari Baniani [ Department of Computer Engineering, Razi University ]
• Abdolah Chalechale [ Department of Computer Engineering, Razi University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605