Maximum Variance Combined with Adaptive Genetic Algorithm for Infrared Image Segmentation

DOI

10.2991/lemcs-15.2015.124How to use a DOI?

Keywords

Image segmentation; Otsu; Adaptive Genetic Algorithm; Infrared image; Optimal threshold

Abstract

Maximum Variance Image Segmentation method (Otsu) is a popular non-parametric method in image segmentation. However, it is large amount of computation and poor real-time quality have limited its further application. To solve these problems, a new approach based on an adaptive genetic algorithm (AGA) and Otsu are proposed, which using between-class variance as fitness function, automatically adjusts the optimal threshold. The adaptive genetic algorithm selects crossover probability and mutation probability according to the fitness values, reduces the convergence time and improves the precision of genetic algorithm, insuring the accuracy of parameter selection. The experimental results show that the proposed method is better than the original Otsu, the AGA-Otsu can provide better effectiveness on experiments of infrared image segmentation, decrease processing time.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huixuan Fu
AU  - Yuchao Wang
AU  - Liangliang Han
PY  - 2015/07
DA  - 2015/07
TI  - Maximum Variance Combined with Adaptive Genetic Algorithm for Infrared Image Segmentation
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 641
EP  - 646
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.124
DO  - 10.2991/lemcs-15.2015.124
ID  - Fu2015/07
ER  -