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首页> 外文会议>Proceedings of the 2007 International Conference on Machine Learning and Cybernetics >MULTI-THRESHOLD INFRARED IMAGE SEGMENTATION BASED ON THE MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM
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MULTI-THRESHOLD INFRARED IMAGE SEGMENTATION BASED ON THE MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM

机译：基于改进粒子群优化算法的多阈值红外图像分割

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摘要

Threshold extraction is the fundamental step in multi-threshold image segmentation.This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction.But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature.This can cause image segmentation failure.This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction.Simulation results show that the MPSO has better performance and quicker speed.The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.
机译：阈值提取是多阈值图像分割的基本步骤。本文介绍了粒子群优化算法（PSO）进行阈值提取。但是，当处理最大熵的峰值高维函数用于多阈值图像分割时，传统的PSO提出了一种改进的粒子群优化方法（MPSO），可以提高收敛速度和搜索能力，避免在阈值提取中使用时出现过早现象。实验结果表明，改进的PSO作为阈值提取方法，可以很好地分割图像，提高了分割速度。

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