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/).
Cite this article
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 -