DocumentCode
2373779
Title
Image segmentation using quantum genetic algorithms
Author
Benatchba, Karima ; Koudil, Mouloud ; Boukir, Yacine ; Benkhelat, Nadjib
Author_Institution
Institut Nat. de Formation en Inf.
fYear
2006
fDate
6-10 Nov. 2006
Firstpage
3556
Lastpage
3563
Abstract
On one hand, image segmentation is a low-level processing task which consists in partitioning an image into homogeneous regions. It can be seen as being a combinatorial optimization problem. In fact, considering the huge amount of information that an image carries, it is impossible to find the best segmentation. On the other hand, quantum genetic algorithms are characterized by their high diversity, and by a good balance between global and local search. In this paper, we present a quantum genetic algorithm for image segmentation
Keywords
combinatorial mathematics; genetic algorithms; image segmentation; combinatorial optimization problem; image segmentation; quantum genetic algorithms; Biomedical imaging; Computer vision; Genetic algorithms; History; Image analysis; Image segmentation; Object recognition; Optimization methods; Pixel; Testing; Image segmentation; Optimization problem; quantum genetic algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on
Conference_Location
Paris
ISSN
1553-572X
Print_ISBN
1-4244-0390-1
Type
conf
DOI
10.1109/IECON.2006.347758
Filename
4153487
Link To Document