Feature Extraction Based on Learning For Feature List Object Matching

摘要：

The appropriate choice of feature extraction offers possibilities for reducing calculation complexity in machine visionapplications,which also has a strong influence on the results of the feature list object matching.But the requirements for reasonable feature extraction are sophisticated and depend on different applications.Based on machine learning,an approach to gradient feature extraction using double thresholds is provided for feature list object matching in this paper.By training,the double thresholds adapted to the special application can be automatically estimated,where an unsupervised learning means is used.Then,the estimated double thresholds are used to the extraction of gradient feature points for the features list matching. The proposed method has been verified by the experiments.

关键词： Double thresholds clustering feature list machine learning matching

作者: Zhijun Pei Jianhua Tao Haiyan Ren

作者单位: School of Mechanical Engineering Tianjin University Nankai,Tianjin,China Department of Machine Vision Tianjin Puda Software Technique Co.ltd TEDA,Tianjin,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 402-406

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）