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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Dual-threshold Segmentation Using Arimoto Entropy Based on Chaotic Bee Colony Optimization
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Dual-threshold Segmentation Using Arimoto Entropy Based on Chaotic Bee Colony Optimization

机译：基于混沌蜂群优化的有位熵双阈值分割

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摘要

In order to extract target from complex background more quickly and accurately, and to further improve the detection effect of defects, a method of dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization was proposed. Firstly, the method of single-threshold selection based on Arimoto entropy was extended to dual-threshold selection in order to separate the target from the background more accurately. Then intermediate variables in formulae of Arimoto entropy dual-threshold selection was calculated by recursion to eliminate redundant computation effectively and to reduce the amount of calculation. Finally, the local search phase of artificial bee colony algorithm was improved by chaotic sequence based on tent mapping. The fast search for two optimal thresholds was achieved using the improved bee colony optimization algorithm, thus the search could be accelerated obviously. A large number of experimental results show that, compared with the existing segmentation methods such as multi-threshold segmentation method using maximum Shannon entropy, two-dimensional Shannon entropy segmentation method, two-dimensional Tsallis gray entropy segmentation method and multi-threshold segmentation method using reciprocal gray entropy, the proposed method can segment target more quickly and accurately with superior segmentation effect. It proves to be an instant and effective method for image segmentation.
机译：为了更快速，准确地从复杂背景中提取目标，并进一步提高缺陷的检测效果，提出了一种基于混沌蜂群优化的有元熵双阈值分割方法。首先，将基于有元熵的单阈值选择方法扩展到双阈值选择，以更准确地将目标与背景分离。然后通过递归计算有向熵双阈值选择公式中的中间变量，以有效地消除冗余计算并减少计算量。最后，通过基于帐篷映射的混沌序列改进了人工蜂群算法的局部搜索阶段。利用改进的蜂群优化算法实现了两个最优阈值的快速搜索，可以明显加快搜索速度。大量的实验结果表明，与现有的分割方法相比，例如采用最大香农熵的多阈值分割方法，二维香农熵分割方法，二维Tsallis灰度熵分割方法和使用多阈值分割的分割方法。互为灰色熵，该方法可以更快速，准确地进行目标分割，并具有较好的分割效果。它被证明是一种即时有效的图像分割方法。

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