Abstract: Concerning the problem that the segmentation of plant leaf images in natural scenes is not effective enough, it is difficult to extract the complete leaf region from the images containing many leaves. Aiming at this problem, a fast multi-threshold extraction method of leaf region is proposed. Firstly, the artificial bee colony algorithm is used to optimize the Otsu multi-threshold selection process. The optimal multi-threshold is obtained by using the between-class variance as the fitness function. The suitable number of segmentation thresholds for the leaf image is determined adaptively in a iterative manner in the process of obtaining the optimal multi-threshold. Then the edge detection, logical operation and morphological methods are used to remove the remaining background elements and extract the leaf region from the results of the multi-threshold segmentation. The experimental results show that the method can obtain a more complete and accurate leaf region when the plant leaf image with one or more leaves are operated.

Key words: image segmentation, artificial bee colony, multi-threshold, leaves, edge detection

摘要： 针对自然场景中植物叶片图像分割效果不佳，难以从含有多个叶片的图像中提取出完整叶片区域的问题，提出了一种叶片区域的快速多阈值提取方法。首先，使用人工蜂群算法优化Otsu多阈值选取的过程，以类间方差为适应度函数获取最优的多个阈值，在获取最优多阈值的过程中以迭代的方式自适应地确定出适合于叶片图像的分割阈值数目，然后使用边缘检测，逻辑运算和形态学操作等从多阈值分割结果中去除背景元素，提取独立、完整的叶片区域。实验结果表明，当对包含一个和多个叶片的自然场景图像进行处理时，该方法能够较为快速地得到更为完整、准确的叶片区域。

关键词: 图像分割, 人工蜂群, 多阈值, 叶片, 边缘检测