心率是脉搏波的最基本信息,是人体健康的一项重要生理指标。传统的心率检测多与人体有直接接触,而长时间的接触会造成被试者的不适,因而不适合长时间的心率测量。因此本文提出了利用摄像头实现非接触式对被测者的光电容积脉搏波(RPPG: r...心率是脉搏波的最基本信息,是人体健康的一项重要生理指标。传统的心率检测多与人体有直接接触,而长时间的接触会造成被试者的不适,因而不适合长时间的心率测量。因此本文提出了利用摄像头实现非接触式对被测者的光电容积脉搏波(RPPG: remote Photo Plethysmography)的提取和分析的方法。即基于人脸识别技术,实现从摄像头中自动分割被测试者的面部信息,选取感兴趣区域(ROI: Region of Interest),使用计算机视觉技术和独立成分分析算法(ICA: Independent ComponentAnalysis),从摄像头视频中提取被测者的光电容积脉搏波信号。本文设计实现了非接触式测量心率,提供了一种较为准确的心率测量方法。展开更多
Sparse signal recovery is a topic of considerable interest,and the literature in this field is already quite immense.Many problems that arise in sparse signal recovery can be generalized as a convex programming with l...Sparse signal recovery is a topic of considerable interest,and the literature in this field is already quite immense.Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints.In this paper,we present a new proximal point algorithm(PPA) termed as relaxed-PPA(RPPA) contraction method,for solving this common convex programming.More precisely,we first reformulate the convex programming into an equivalent variational inequality(VI),and then efficiently explore its inner structure.In each step,our method relaxes the VI-subproblem to a tractable one,which can be solved much more efficiently than the original VI.Under mild conditions,the convergence of the proposed method is proved.Experiments with l1 analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.展开更多
文摘心率是脉搏波的最基本信息,是人体健康的一项重要生理指标。传统的心率检测多与人体有直接接触,而长时间的接触会造成被试者的不适,因而不适合长时间的心率测量。因此本文提出了利用摄像头实现非接触式对被测者的光电容积脉搏波(RPPG: remote Photo Plethysmography)的提取和分析的方法。即基于人脸识别技术,实现从摄像头中自动分割被测试者的面部信息,选取感兴趣区域(ROI: Region of Interest),使用计算机视觉技术和独立成分分析算法(ICA: Independent ComponentAnalysis),从摄像头视频中提取被测者的光电容积脉搏波信号。本文设计实现了非接触式测量心率,提供了一种较为准确的心率测量方法。
基金the National Natural Science Foundation of China(No.70901018)
文摘Sparse signal recovery is a topic of considerable interest,and the literature in this field is already quite immense.Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints.In this paper,we present a new proximal point algorithm(PPA) termed as relaxed-PPA(RPPA) contraction method,for solving this common convex programming.More precisely,we first reformulate the convex programming into an equivalent variational inequality(VI),and then efficiently explore its inner structure.In each step,our method relaxes the VI-subproblem to a tractable one,which can be solved much more efficiently than the original VI.Under mild conditions,the convergence of the proposed method is proved.Experiments with l1 analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.
