Earticle

초록

영어

The particle swarm optimization (PSO) is a new swarm intelligence technique inspired by
social behavior of bird flocking. In this paper, the optimal multi-objective particle swarm
optimization (OMOPSO) is presented. Since the parameters determine the optimization
performance of the algorithm, the uniform design is introduced to obtain the optimal
combination of the parameters. Additionally, a new crowding operator is used to improve the
distribution of nondominated solutions, and ε-dominance is used to fix the size of the set of
final solutions. OMOPSO is applied to optimize the parameters of blind color image fusion.
First the model of blind color image fusion in YUV color space is established, and then the
proper evaluation metrics without the reference image are given, in which a new metrics of
conditional mutual information is proposed. Experimental results indicate that the method of
blind color image fusion based on OMOPSO realizes the Pareto optimal blind color image
fusion.

목차

Abstract
 1. Introduction
 2. OMOPSO Algorithm
  2.1. OMOPSO Flow
  2.2. Repository Control
  2.3. ε Dominance
  2.4. Adaptive Inertia Weight
  2.5. Crowding Distance
  2.6. Adaptive Mutation
  2.7. Uniform Design of Parameters
 3. Blind Color Image Fusion in YUV Space
 4. Evaluation of Blind Color Image Fusion
  4.1. Gradient
  4.2. Entropy
  4.3. Conditional Mutual Information
 5. Experiments
  5.1. Uniform Design of the Parameters
  5.2. Comparison among Different Fusion Schemes
 6. Conclusions
 7. References

키워드

저자

• Lincheng Shen [ College of Mechatronic Engineering and Automation National University of Defense Technology ]
• Yifeng Niu [ College of Mechatronic Engineering and Automation National University of Defense Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605