飞行目标3维姿态测量的新方法

针对传统光测方法通常用于测量目标的点运动轨迹,而难以获得目标的3维姿态参数的情况,提出用数字图像处理手段,对目标图像建立特征点,结合特定的算法,得出目标的俯仰角、偏航角、滚动角、漂移量等3维参数,从而获取更多的目标姿态参数的新方法。     

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。     

基于视觉的多特征手势识别

手势是一种自然直观的交互方式,基于视觉的手势识别是实现新一代人机交互的关键技术。本文在已有的手势识别技术基础上,从手势分割及手势表示两方面着手,提出了一种单目视觉下的手势识别方法。利用颜色特征检测肤色区域,成功分割出人手;利用人手的轮廓及凸缺陷检测指尖,再利用指尖的数目和方位来表示一个手势,进而结合轮廓长度和面积等几何特征完成手势识别。传统的指尖检测方法需要遍历并扫描手掌外轮廓,计算量大,本文通过凸缺陷检测指尖,减少了计算量,提高了指尖检测的速度。实验结果表明,本文的方法具有很好的鲁棒性及实时性,能适应环境的变化。     

融合Hu矩与BoF-SURF支持向量机的手势识别

基于尺度不变特征变换的特征包（BoF-SIFT）支持向量机的分类方法具有较好的手势识别效果, 但是计算复杂度高、实时性较差。为此, 提出了融合Hu矩与基于快速鲁棒特征的特征包（BoF-SURF）支持向量机（SVM）的手势识别方法。特征包模型中用快速鲁棒性特征（SURF）算法替换尺度不变特征变换（SIFT）算法提取特征, 提高了实时性, 并引入Hu矩描述手势全局特征, 进一步提高识别率。实验结果表明, 算法无论是实时性还是识别率都要高于BoF-SIFT支持向量机方法。     

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．