农机化研究Issue(9)：222-226,5.
基于 Lab 空间和 K-Means 聚类的叶片分割算法研究
Segmentation Algorithm Based on Blade Lab Space and K-Means Clustering
摘要
Abstract
By classifying plant leaves has important significance in the study of plant species identification , classification in plant leaves , leaves of accurate segmentation is a necessary prerequisite to classify .This paper analyzes the contrast between the traditional threshold segmentation of the largest class clustering two variance method and K-Means segmenta-tion algorithm , to achieve segmentation leaves and RGB space conversion to Lab space , and then use two algorithms were split .The results show that the traditional threshold segmentation and K -Means clustering segmentation can not be the target image accurately segmented;in Lab space for a component of threshold segmentation can remove the shadow part , but the segmentation results for binary image;while in Lab space K-Means clustering segmentation , not only can effec-tively eliminate the shaded area in the captured image generated by the process , and after the image segmentation for col-or images , the extraction of texture and color features more convenient and improve the classification accuracy .关键词
植物种类鉴别/阈值分割/K-Means 聚类分割/Lab 空间Key words
plant species identification/threshold segmentation/K-Means clustering segmentation/lab space分类
农业科技引用本文复制引用
邹秋霞,杨林楠,彭琳,郑强..基于 Lab 空间和 K-Means 聚类的叶片分割算法研究[J].农机化研究,2015,(9):222-226,5.基金项目
云南省科技创新强省计划项目（2014AB019）；国家自然科学基金项目 （）
