中文摘要：

准确分割水果图像是采摘机器人实现视觉定位的关键技术。该文针对传统模糊聚类对初始聚类中心敏感、计算量大和易出现图像过分割等问题，结合机器人的视觉特性，提出了一种基于多尺度视觉显著性改进的水果图像模糊聚类分割算法。首先，选择适当的颜色模型把彩色水果图像转换为灰度图像；然后对灰度图像做不同尺度的高斯滤波处理，基于视觉显著性的特点，融合了多个不同尺度的高斯滤波图像，形成图像聚类空间；最后，用直方图和模拟退火粒子群算法对图像的传统模糊聚类分割算法进行了改进，用改进的算法分别对采集到的100张成熟荔枝和柑橘图像，各随机选取50张，进行图像分割试验。试验结果表明：该方法对成熟荔枝和柑橘的图像平均果实分割率分别为95.56%和93.68%，平均运行时间分别为0.724和0.790 s，解决了水果图像过分割等问题，满足实际作业中采摘机器人对果实图像分割率和实时性的要求，为图像分割及其实时获取提供了一种新的基础算法，为视觉精确定位提供了有效的试验数据。

英文摘要：

The vision location system of the picking robot, which is an important part of the robot, is mainly used to detect the spatial position of the fruit and provide the motion control system of the robot with position information. Extracting the fruit waited for picking in a complex background by selecting an appropriate image segmentation technology provides us with the full assurance to obtain the position information of the fruit. So, aiming at the problems that the traditional fuzzy clustering is sensitive to the initial clustering centers and has large amounts of calculation and image over-segmentation, combining with the picking robot visual characteristics, an improved fuzzy clustering segmentation algorithm based on the multi-scale visual saliency for fruit image was put forward in this paper. First, a color model of the litchi and citrus image was discussed respectively, their diagrams of the R-I color model was expatiated, the fruit color image was converted into gray image by selecting a R-I color model; the gray image was processed with different scale Gaussian filters and the image clustering segmentation space was formed by blending all the different scale Gaussian filtering images according to the visual saliency, effect chart of the multi-scale visual saliency image algorithm was given based on R-I, and the over-segmentation problem most of the fruit image fuzzy clustering segmentation algorithms was solved. Second, the high dimensional clustering segmentation space based on pixels was changed into the low dimensional clustering segmentation space based on the histogram and the gray level by using the histogram method and the specific steps of image segmentation algorithm was given; the calculation of the fuzzy clustering image segmentation algorithm was greatly decreased and the fuzzy clustering image segmentation speed was improved. Furthermore, in the light of the problems that the fuzzy clustering algorithm easily fell into the local extreme value, the clustering center was optimized with the part