基于同态滤波和改进K-means的苹果分级算法研究  

Research on apple classification algorithm based on homomorphic filtering and improved K-means algorithm

作　　者：王阳阳 黄勋[1] 陈浩[1] 黄伦 雷扬博 WANG Yang-yang;HUANG Xun;CHEN Hao;HUANG Lun;LEI Yang-bo(Shaanxi University of Science and Technology of Shaanxi Province,Xian,Shaanxi 710021,China)

机构地区：[1]陕西科技大学

出　　处：《食品与机械》2019年第12期47-51,112,共6页Food and Machinery

摘　　要：针对苹果在分级的过程中,光线不均所导致的表面反光和阴影问题,利用同态滤波和改进的K-means算法予以解决。同态滤波前,将苹果图像由RGB空间转换到HSV空间,再对HSV空间的V分量进行同态滤波增强,最大限度地削弱光线不均带来的影响;对传统K-means聚类算法,新增加距离度量方法、确定聚类数目和初始中心点,能较好地去除苹果阴影对图像分割的影响。从大小、果形、质量、颜色、缺陷5个方面对陕北富县的秦冠苹果进行分级,分级成功率达到97%。利用同态滤波算法结合改进的K-means算法来对苹果图像进行处理,能够大大提高苹果分级的准确性。Homomorphic filtering and improved k-means algorithm were used to solve the problem of apple surface reflection and apple shadow caused by uneven light during apple grading.Before homomorphic filtering,the apple image was converted from RGB space to HSV space.Then the V component of HSV space was enhanced by homomorphic filtering to minimize the impact of uneven light.For the traditional K-means clustering algorithm,distance measurement method,determination of clustering number and initial center point were newly added,which can better remove the influence of apple shadow on image segmentation.The Qin Guan apples in Fu Xian county of northern Shaanxi were classified from five aspects,such as size,shape,quality,color and defect.Compared with the artificial and mechanical classification,the classification success rate reached 97%.Using homomorphic filtering algorithm and improved k-means algorithm to process apple images can greatly improve the accuracy of apple classification.

关 键 词：苹果 分级 同态滤波 改进K-MEANS算法 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]