中文摘要：

为解决基于模糊C-均值聚类（FCM）的图像分割算法需要预先给定初始聚类数目和聚类中心,易使得算法陷入局部最优的问题,提出一种改进的人工蜂群优化模糊聚类的图像分割方法。该方法在传统的人工蜂群的基础上进行优化,以FCM算法中目标函数为基础改进人工蜂群的适应度函数,运用蜂群行为中的采蜜蜂、跟随蜂和侦察蜂的分工合作来快速求解图像中的最优初始聚类中心,将求出的最优聚类中心输入给FCM进行处理,根据最大隶属度原则对果实图像进行分割。以300幅不同光照情况下拍摄的夏黑葡萄果进行分割试验,试验结果表明,改进的图像分割方法能更快地将水果从自然环境中分割识别出来,单幅图像平均分割时间为0.219 3 s,正确分割率达到90.33%,能满足采摘机器人及水果分级系统对目标图像的实时性要求。

英文摘要：

The image segmentation algorithm based on the fuzzy C-average clustering （FCM） needs initial cluster number and cluster center in advance, which make the algorithm easy to fall into local optimum. An image segmentation method based on improved artificial swarm optimization fuzzy clustering was proposed. The optimization of proposed method was conducted on the basis of the traditional artificial colony. The fitness function of artificial colony was improved by using objective function of FCM algorithm. With the collaboration of bee colony, follow bees and computerized bee, the optimal initial clustering center could be solved quickly. Then the optimal initial clustering center was input into FCM and image segmentation was finally realized by using maximum membership principle. The fruit segmentation experiment was carried out with 300 ＇summer black＇ grape photos taken under frontlight, backlight and normal light illumination conditions. The experiment proves that the proposed method can identify fruit from the natural environment quickly. The average time for segmentation was 0. 219 3 s per photo and accuracy was 90. 33%. The time consuming was shorter and the accuracy was higher than OTSU and traditional FCM algorithm. It can meet the real-time requirement of picking robot and fruit grading system.