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Proceedings Paper

Fast infrared image segmentation method based on 2D OTSU and particle swarm optimization
Author(s): Song-Tao Liu; Zhan Wang; Zhen Wang
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Paper Abstract

The image segmentation method based on 1D histogram and the optimal objective function is an important threshold segmentation method, but if it is applied to the infrared image segmentation directly, its ability for the suppression of the background noise is weak. In this paper, the 2D Maximum inter-class variance method is applied to infrared image segmentation, which improves the image segmentation effect obviously, but it takes a long time to calculate. Therefore, an improved Particle Swarm Optimization (PSO) algorithm is introduced to speed up the algorithm, which improves the real-time performance of the algorithm. The experimental results show that the new method has not only good segmentation effect, but also high computational efficiency, and it is a fast infrared image segmentation method.

Paper Details

Date Published: 26 July 2018
PDF: 7 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280F (26 July 2018); doi: 10.1117/12.2501870
Show Author Affiliations
Song-Tao Liu, Dalian Naval Academy (China)
Zhan Wang, Dalian Naval Academy (China)
Zhen Wang, Dalian Naval Academy (China)

Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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