改进人工蜂群算法在二维Otsu图像分割中的应用

为了迅速准确的分割图像，通过对传统蜂群算法选择蜜源方式和缺陷蜜源的调整，提出了一种基于改进的人工蜂群算法分割二维Otsu图像的新方法. 此方法把图像阈值由人工蜂群算法中的蜜蜂表示，通过引领蜂、侦查蜂和跟随蜂之间的信息共享和分工协作来求出最佳阈值，成功解决了传统二维Otsu图像分割计算量大、运行时间长的缺陷. 实验结果表明，所提出的算法不仅能得到理想分割结果，而且分割速率快.

Improved Artificial Colony Algorithm in the Application of Two-Dimensional Otsu Image Segmentation

In order to segment images exactly and quickly, based on the traditional adjustment of colony algorithm selection strategy and defect honey, a new method based on a improved Artificial Bee Colony algorithm segmenting two dimensional Otsu images is proposed. This method looked on the image threshold value as artificial colony algorithm of the bees. The best threshold is approached in parallel via the division of labor, cooperation and information sharing of employed bees, onlookers and scouts. Effectively solved the problem of the traditional two dimensional Otsu image segmentation calculation defects, long operation time. Experimental results show that the proposed algorithm not only can get the ideal segmentation results, but only improved the segmentation speed.