Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm

• Published in: Measurement science review 2012-12, Vol.12 (6), p.296-301
• Main Authors: Guofeng, Jin, Wei, Zhang, Zhengwei, Yang, Zhiyong, Huang, Yuanjia, Song, Dongdong, Wang, Gan, Tian
• Format: Article
• Language: English
• Subjects: fuzzy C-Mean clustering algorithm; Image segmentation; particle swarm optimization; thermal wave inspection
• ISSN: 1335-8871; 1335-8871
• DOI: 10.2478/v10048-012-0041-6

The Fuzzy C-Mean clustering (FCM) algorithm is an effective image segmentation algorithm which combines the clustering of non-supervised and the idea of the blurry aggregate, it is widely applied to image segmentation, but it has many problems, such as great amount of calculation, being sensitive to initial data values and noise in images, and being vulnerable to fall into the shortcoming of local optimization. To conquer the problems of FCM, the algorithm of fuzzy clustering based on Particle Swarm Optimization (PSO) was proposed, this article first uses the PSO algorithm of a powerful global search capability to optimize FCM centers, and then uses this center to partition the images, the speed of the image segmentation was boosted and the segmentation accuracy was improved. The results of the experiments show that the PSO-FCM algorithm can effectively avoid the disadvantage of FCM, boost the speed and get a better image segmentation result.