| 潘峰,孙红霞.基于蝗虫算法的图像多阈值分割方法[J].电子测量与仪器学报,2019,33(1):149-155 |
| 基于蝗虫算法的图像多阈值分割方法 |
| Algorithm for image segmentation based on grasshopper optimization algorithm |
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| DOI： |
| 中文关键词: 图像分割 Otsu Kapur最大熵 蝗虫算法 粒子群 人工蜂群 |
| 英文关键词:image segmentation otsu Kapur's entropy grasshopper algorithm particle swarm artificial bee colony |
| 基金项目:四川省教育厅科研自然科学基金(18ZB0592)资助项目 |
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| 摘要点击次数: 336 |
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| 中文摘要: |
| 针对图像分割中的阈值选择问题，提出了一种基于蝗虫算法的图像多阈值分割算法。该算法综合考虑Otsu法和最大熵法的分割特性，以Otsu算法的最大类间方差和Kapur最大熵法的最大熵值构造适应度函数，利用蝗虫算法进行寻优求解最佳阈值，最后利用最佳阈值对图像进行分割。将该算法与传统的Otsu算法、最大熵法、基于粒子群的图像分割方法、基于人工蜂群的图像分割方法进行比较，实验结果表明，相对其他算法，分割所得的峰值信噪比更大，分割效果更好，在阈值个数为4和5时，该算法所得的峰值信噪比（PSNR）值相比粒子群算法、人工蜂群算法提高了约3%和15%，算法的运行时间相比粒子群算法和人工蜂群算法，快了约9%和5%。 |
| 英文摘要: |
| Aiming at the problem of threshold selection in image segmentation, a multi threshold segmentation algorithm based on grasshopper algorithm is proposed in this paper. In this algorithm the otsu method and Kapur’s entropy are considered. The fitness function which used are the maximum between class variance criterion (Otsu) and the Kapur’s Entropy. This method uses the grasshopper optimization algorithm to optimize threshold. In the end, the image is segmented with the best threshold. The algorithm is compared with the traditional Otsu algorithm, the maximum entropy method, the image segmentation method based on particle swarm, and the image segmentation method based on artificial bee colony. Experimental results show that the algorithm is better than other algorithms. When the number of thresholds is 4 and 5, the PSNR of the proposed algorithm is about 3% and 15% higher than that of particle swarm optimization algorithm and artificial bee colony algorithm. The running time of this algorithm is about 9% and 5% faster than that of particle swarm optimization algorithm and artificial bee colony algorithm. |
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