Multiple Particles Optimization Used in Image Segmentation
碩士 === 國立中興大學 === 應用數學系所 === 102 === This paper presents a Multiple Particles Optimization (MPO) algorithm. The algorithm uses the variances between groups in Otsu method as the fitness function of the particle swarm algorithm for solving the threshold selection problem of the image segmentation....
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| Format: | Others |
| Language: | zh-TW |
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2014
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| Online Access: | http://ndltd.ncl.edu.tw/handle/64p59t |
| Summary: | 碩士 === 國立中興大學 === 應用數學系所 === 102 === This paper presents a Multiple Particles Optimization (MPO) algorithm. The algorithm uses the variances between groups in Otsu method as the fitness function of the particle swarm algorithm for solving the threshold selection problem of the image segmentation. The multiple particle optimization (MPO) algorithm is to obtain multiple thresholds of an image histogram used in a thresholding method. The performance of the multiple particle optimization (MPO) algorithm is better than the particle swarm optimization (PSO) algorithm in computing speed.
The experiments results show image segmentation for synthetic images and real images. The proposed method has the best performance in speed for comparing the results of the MPO algorithm and the other thresholding algorithms, Otsu and LEA methods in computing speed.
Keywords：Multiple Particles Optimization ;Particle Swarm Optimization; Otsu Method; Thresholding Method
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