基于模糊C均值聚类的作物病害图像分割方法研究

摘要

Image segmentation is to separate the target of interest from the background, and the result directly influences the latest image analysis and recognition.Based on the segmentation technology, crop disease images are to segment disease spot from the disease images, in order to provide the convenience for the following diagnosis and recognition.Fuzzy C-Means clustering algorithm(FCM) is a powerful unsupervised method for the analysis of data and construction of models.For improvement on segmentation precision of crop disease images, an adaptive segmentation method of crop disease images is proposed based on FCM, according to the properties of crop disease images.And compared with the K-Mean clustering algorithm, the result shows that the proposed algorithm works well.%图像分割是指将人们感兴趣的目标从背景中分割出来,分割结果的好坏直接影响后期的图像分析和识别.基于作物病害图像的分割技术就是将病斑从病害图像中分割出来,以便于后期病害的诊断和识别.模糊C均值聚类是一种重要数据分析和建模的无监督方法,为提高作物病害图像的分割效果,根据作物病害图像的特点,提出一种基于模糊C均值聚类算法的作物病害图像自适应分割方法,并与K均值聚类算法进行比较,结果显示本文算法在进行图像分割方面表现出明显优势.