基于支持向量机的人脸姿态判定

对于多姿态人脸检测中的姿态判定问题,提出了一种基于支持向量机(SVM)的人脸姿态判定算法。将人脸姿态划分成6个类别,从一个多姿态人脸库中手工标定出1800幅人脸图像作为训练样本集,分别训练基于支持向量分类(SVC)和基于支持向量回归(SVR)2种姿态分类器。另外标定出300幅人脸图像作为测试样本。SVC方法和SVR方法分别取得了1.67%和3.33%的分类错误率。其中SVC方法的分类效果明显优于在传统方法中效果最好的人工神经元网络(ANN)方法(分类错误率为3.33%)。对比实验结果表明,SVM方法对于解决姿态判定问题是很有效的。     

A New Approach to Implement Synchronous Demultiplexers in High-Speed ISDN Switching Network

Introduction　　In ISDN switching networks,synchronousmultiplexers convert a certain number of serialsynchronous inputs into parallel synchronousmultiplexing data which is stored in memory.Thedata is then switched through the synchronousswitching network,and then synchronousdemultiplexers reconvert the data into severalserial synchronous outputs. The speed andcapacity of the multiplexers and demultiplexers areimportant factors in very large synchronousswitching networks[1] . So synchronousmu…