基于多边形近似的叶缘缺刻位置自动检测  

POLYGONAL APPROXIMATION-BASED AUTOMATIC DETECTION OF LEAF MARGIN INDENTATION LOCATION

作　　者：王晓洁[1] 张晓煜[2] 郑小东[2] 薄树奎[2] 

机构地区：[1]郑州大学机械工程学院,河南郑州450002 [2]郑州航空工业管理学院计算机科学与应用系,河南郑州450015

出　　处：《计算机应用与软件》2014年第9期157-161,共5页Computer Applications and Software

基　　金：国家自然科学基金项目(41001235)

摘　　要：叶缘特征是植物叶重要的形态特征,在许多植物相关工作中有重要作用。叶缘缺刻位置检测是获取叶缘特征的重要环节。为实现叶缘缺刻位置自动检测,提出基于多边形近似的检测方法:首先进行图像预处理,获得植物叶片二值图像,然后提取叶片轮廓,进行多边形近似;最后通过对近似多边形顶点进行凹凸性分析、筛选,获得叶缘缺刻位置。在Matlab平台下对该方法进行仿真,使用多种形态的植物叶进行测试。结果表明:该方法可以检测出图像中人眼可觉察的缺刻位置,该方法可应用于叶缘特征自动获取工作。The calibration method affects the tracking accuracy and user experience directly,so it is a key link in gaze tracking technolo-gy.Current calibration method used by the head-mounted tracking system requires multiple calibration points to accomplish this process.Inorder to calibrate faster and more convenient,we present a method which only requires one calibration point for extracting sufficient calibrationinformation to complete the calibration process.This method can be applied to a variety of mapping methods used at present,such as the DLTmethod,the polynomial method,and the neural network method,etc.The calibration time takes only 10 s,and the precision reaches 1＆#176;.Compared with multi-point calibration,it significantly improves the efficiency with no noticeable difference in precision.In addition,we usea new neural network model,the ELM （extreme learning machine）,to realise the neural network calibration.ELM’s fast learningperformance remarkably shortens the training time of the neural network.

关 键 词：叶缘 凹凸性 特征提取 多边形近似 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S126[自动化与计算机技术—计算机科学与技术]