机械制造与自动化Issue(4)：132-134,139,4.
混合KPCA和SVM的机械零件形状识别方法研究
Research on Shape Recognition of Mechanical Parts Based on Hybrid KPCA and SVM
摘要
Abstract
Shape recognition of mechanical parts is regarded as an important area in the reseach work of intel igent machine vision. The contour information of shapes of mechanical parts is extracted by Fourier descriptors, and a series of high dimensional vectors are created. These vectors are processed through the method of vector dimension reduction based on KPCA. The method of pattern recognition based on SVM is used with these vectors to classify the some simple mechanical parts ( rings, nuts and bolts) . Experi-ments show the methods above have very high recognition rate for non-overlapping and ful shape mechanical parts. The research results provide the important reference value for mechanical intel igent sorting, assembly and other tasks.关键词
形状识别/傅里叶描绘子/核主分量分析/支持向量机/机械零件Key words
shape recognition/Fourier descriptor/KPCA/SVM/mechanical part分类
信息技术与安全科学引用本文复制引用
冯长建,吴斌,罗跃纲..混合KPCA和SVM的机械零件形状识别方法研究[J].机械制造与自动化,2016,(4):132-134,139,4.基金项目
中央高校基本科研业务费专项资金资助项目( DC120101013) （ DC120101013）
