Support Vector Machine-based Image Segmentation Approach for Automatic Agriculture Vehicle
Support Vector Machine,a statistic procedure,is robust and good performance when applied to farmland image segmentation.It can effectively identify the crop rows,even though they have some intersection at some points because of weeds of the leaves growth of the crops.However,the Support Vector Machine has relatively high time complexity and can not meet the requirements of real-time processing.Based on Pyramid algorithm,we can obtain a low resolution representation of the images being processed.Then training and testing are applied in the low resolution images only.Through this method,the time consumption is significantly lower than the original Support Vector Machine Procedure.The objects in farmland images are large and there are only two major types of structure in it,so the examination accuracy of proposed method is changed little.At the same time based on spatial structure and color distribution of farmland image,the kernel and main parameters of Support Vector Machine are selected.
Guidance Support Vector Machine image segmentation Pyramid algorithm
Yonghua Han Yarning Wang Yun Zhao
School of Biosystem Engineering and Food Science,Zhejiang University,Hangzhou,China The College of E The College of Electronics and Informatics,Zhejiang Sci-Tech University,Hangzhou,china School of Biosystem Engineering and Food Science,Zhejiang University,Hangzhou,China The Mechanical C
国际会议
太原
英文
385-389
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）