基于稀疏场水平集的荔枝图像分割算法

为了给采摘机器人提供完整的荔枝果实轮廓，该文选择HSV彩色空间中色调H分量的旋转分量作为图像分割的特征；然后，通过模糊聚类算法自动获取合适的初始演化曲线轮廓，再利用稀疏场水平集方法对目标区域轮廓进行精确提取；最后，对分割的区域进行标记，并利用图像标记来恢复分割区域的原始图像。结果表明，该算法不仅很好地克服随机噪声的影响，而且很好地保持果实区域的完整性，使成熟荔枝分割的正确率达到了84.1%。

Litchi image segmentation algorithm based on sparse field level set

In order to provide picking robot with complete contour of litchi, hue component of HSV color space was selected, and the rotation of hue was used as the feature for image segmentation. Then, fuzzy clustering algorithm was utilized to obtain the appropriate initial evolution curve contour automatically, the sparse field level set method was adopted to extract the target region precisely, and the segmentation regions were labeled. The label image could restore the original image of the segmented regions. Finally, the results show that the proposed algorithm can not only overcome the impact of random noise interference, but also maintain the integrity of the segmentation area, and the correct segmentation rate is up to 84.1%.