基于TMS320VC54X的实时卫星信号跟踪系统

介绍了一种在移动载体上实现卫星信号实时跟踪的系统设计方案，该方法以TMS320VC54X为核心信号处理芯片，利用姿态敏感元件稳定运动中的卫星接收天线，以使其始终对准所要跟踪的同步卫星，文章给出了这种方法的硬件原理和主要的软件设计思路。     

基于HMM方法的动态手势轨迹训练性能研究

基于HMM(Hidden Markov Model,隐形马尔可夫模型)对动态手势轨迹的训练是手势识别的关键技术之一。本文对HMM的模型训练采用Baum.Welch算法,并分别从迭代次数,样本个数选取,以及模型初值选取等方面对动态手势轨迹的训练性能进行了研究。实验结果表明HMM方法对具有时空特性的动态手势轨迹识别是非常有效的。     

From children's hands to adults' ears: Gesture's role in the learning process.

Children can express thoughts in gesture that they do not express in speech--they produce gesture-speech mismatches. Moreover, children who produce mismatches on a given task are particularly ready to learn that task. Gesture, then, is a tool that researchers can use to predict who will profit from instruction. But is gesture also useful to adults who must decide how to instruct a particular child? We asked 8 adults to instruct 38 third- and fourth-grade children individually in a math problem. We found that the adults offered more variable instruction to children who produced mismatches than to children who produced no mismatches--more different types of instructional strategies and more instructions that contained two different strategies, one in speech and the other in gesture. The children thus appeared to be shaping their own learning environments just by moving their hands. Gesture not only reflects a child's understanding but can play a role in eliciting input that could shape that understanding. As such, it may be part of the mechanism of cognitive change. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于动态时间规整的手势加速度信号识别

为了提高基于加速度传感器的动态手势识别算法的性能,本文采用了动态时间规整(DTW)识别算法。通过该算法计算测试模板和参考模板的相似度,从而得出识别结果。为了验证该方法,建立了一套手势加速度无线采集系统,并采集了41个志愿者的手势信息。实验结果表明,该方法手势平均识别率在97%以上。与HMM识别算法相比,DTW识别算法在识别的准确率上比HMM识别算法更具优势。     

HEGM: A hierarchical elastic graph matching for hand gesture recognition

A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.     

基于体感技术的手势追踪与识别

体感技术使人们更直接的通过自己的肢体动作与电脑设备产生互动,减小了鼠标、键盘等传统输入设备带来的束缚。使用Kinect 体感设备,对体感关键技术及 Kalman滤波器算法进行了深入的研究,并在此基础上提出了一种基于骨骼坐标的手势识别算法。最后详细阐明了基于 Kinect 的手势追踪与识别系统的具体设计与实现方法。     

基于手势识别的智能输入

针对在人机交互中键盘远远不能满足人们的要求,提出了一种手势识别算法,并结合键盘的按键实现智能输入．该算法采用肤色分割提取出手部区域,根据手形检测出大拇指和伸出的手指数目及位置,把手势转化为计算机所能接受的二进制语言．根据二进制数,结合键盘的按键,把不同手势定义为不同的按键,实现对计算机的智能输入．实验结果表明该算法具有一定的鲁棒性,并能达到较高的成功率．     

基于深度相机的手腕识别与掌心估测算法(HHME2013-ID51)

当手和手臂都进入深度相机所设定的有效深度范围时,它们将被作为一个整体来提取,若处理时也把它们作为一个整体,这可能会影响手势交互的一些重要算法,如掌心估测,手朝向估测,手的跟踪等。本文分析了手腕的运动特征和手的轮廓特点,并利用内切矩形的几何特征,提出了手腕识别算法来分割手与手臂;为了提高掌心估测算法的性能,本文分析了锐角三角形和最大内切圆的几何特征,并结合手势交互的特点,提出了新的掌心估测算法。经实验证明,手腕识别算法能较好地分割出手与手臂,新的掌心估测算法较之原算法在性能上有显著提高。     

基于信道状态特征的手势动作识别技术研究

手势识别作为人和机器之间重要的交互手段,在日常生活中具有广泛的应用场景。基于无线信号特别是WiFi的手势识别由于其无接触、成本低等优点成为当前热门的方式。为解决传统基于无线信号手势识别算法没有充分利用信号相位特征的缺点,本文提出利用WiFi信道状态信息幅值和相位结合的方式进行手势识别。通过子载波降维和动作曲线提取对接收的WiFi信号进行处理,并将信号的幅值和相位结合,利用机器学习算法对数据进行训练和分类,实现了单手向前、单手向后、单手向左、单手向右、单手向上和单手向下六种手势的识别。实验结果显示,本文算法在近距离和远距离下的精确度分别为96%和92%。     

在线半监督Kohonen网络的预抓取手势识别

为实现智能仿生手的抓取,提高模式识别的实时性和灵敏性,提出一种在线半监督Kohonen网络。该网络针对表面肌电信号(s EMG)的特性,在有监督Kohonen网络基础上,将有监督和无监督网络的优势进行结合,应用数据剪辑方法处理训练集更新识别网络,在线识别侧边抓取、球形抓取、三指精确抓取和圆柱形抓取4种预抓取手势。实验表明,与不同Kohonen网络相比,此识别方法具有很好的在线识别能力和正确率。