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Particle Swarm Optimization Algorithm and It’s Application on Image Segmentation

Author: WuYan
Tutor: ZhangBing
School: Jiangsu University of Science and Technology
Course: Signal and Information Processing
Keywords: Particle Swarm Optimization Image Segmentation Variability model Artificial Immune Multi - threshold
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 61
Quote: 0
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Abstract


Particle Swarm Optimization algorithm derived from the behavior of the flock group sports a population search strategy based adaptive stochastic optimization algorithms. As a typical representative of swarm intelligence, particle swarm optimization algorithm has been proven to be an effective global optimization method, has been put forward on the subject to the attention of researchers around the world, great importance has been widely applied to image segmentation, the objective function optimization, neural network training, fuzzy control systems and many other fields, and have achieved good results. Image segmentation is an important step in target detection and identification process, and its purpose is the area of ??interest from the image segmentation, which provide the basis for the subsequent processing of computer vision. There are many methods of image segmentation threshold and become an effective method for its realization simple image segmentation method. Search for the best combination of multi-threshold image segmentation, it's high time consuming unable to meet the requirements of real-time, accurately determine threshold is the key to effective image segmentation, however, in the complex image histogram showed a multimodal distribution . Therefore, fast and accurate search to the combination of multi-threshold image segmentation is the difficulty of the problem. To quickly and accurately determine the best combination of multi-threshold complex image segmentation results and meet the requirements of real-time, it is necessary to seek an efficient algorithm to solve the problem of image segmentation based on multi-threshold method. On the basis of previous work on particle swarm optimization algorithm and its application in image segmentation: (1) In order to improve the convergence rate of the particle swarm algorithm and at the same time improve the global search performance, the paper focuses on the two novel improved particle swarm optimization. (A) an improved algorithm uses relative the base initialization particle populations in order to obtain a better initial solution. In order to further improve the convergence speed and accuracy, the algorithm groups into a local optimum corresponding variation particles compare their fitness, to select high fitness particles continue to optimize the process. Different test functions simulation experiments show that the algorithm significantly improves the speed and accuracy of the convergence of particle swarm algorithm. (B) the second improved algorithm combining particle swarm algorithm and immune algorithm, simulated annealing mechanism to restrict the location of the particle, and verify the effectiveness of the algorithm in combinatorial optimization and the traveling salesman problem. (2) will be improved in this paper two algorithms is applied based on the trial of multi-threshold image segmentation method, the experimental results show that the algorithm can quickly and accurately find the best combination of the segmentation threshold: the two improved obtain good segmentation results and is suitable for multi- The peak histogram complex image.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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