An Improved Particle Swarm Optimization Algorithm for Image Matching

Author

Ru, An ; Chunye, Chen ; Huilin, Wang

Author_Institution

Dept. of Geogr. Inf. Sci., Hohai Univ., Nanjing, China

Volume

1

fYear

2009

fDate

25-27 Dec. 2009

Firstpage

7

Lastpage

10

Abstract

Image matching is widely applied in the areas of pattern recognition, computer vision, medicine, remote sensing, aircraft navigation and movement tracking. In this paper, an improved particle swarm optimization algorithm based on variable swarm population size and mutual information as similarity measure function is proposed for image matching. The aim is to enhance the overall performance of image matching. The proposed scheme adjusts the population size in terms of the diversity of the population. The algorithm presented is compared with the exhaustive search based on mutual information, and standard PSO. Remote sensing images captured by different sensors with different resolutions are as testing data. It is proved that the algorithm the paper suggested is effective for image matching.

Keywords

image matching; particle swarm optimisation; image matching; mutual information; particle swarm optimization; population diversity; remote sensing image; similarity measure function; variable swarm population size; Aircraft navigation; Biomedical imaging; Computer vision; Image matching; Mutual information; Particle measurements; Particle swarm optimization; Pattern recognition; Remote sensing; Tracking; image matching; image registration; mutual information; particle swarm optimization; variable swarm population size;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on

Conference_Location

Chongqing

Print_ISBN

978-0-7695-3930-0

Electronic_ISBN

978-1-4244-5423-5

Type

conf

DOI

10.1109/IFCSTA.2009.8

Filename

5385147