On an algorithm for Vision-based hand gesture recognition

A vision-based static hand gesture recognition method which consists of preprocessing, feature extraction, feature selection and classification stages is presented in this work. The preprocessing stage involves image enhancement, segmentation, rotation and filtering. This work proposes an image rotation technique that makes segmented image rotation invariant and explores a combined feature set, using localized contour sequences and block-based features for better representation of static hand gesture. Genetic algorithm is used here to select optimized feature subset from the combined feature set. This work also proposes an improved version of radial basis function (RBF) neural network to classify hand gesture images using selected combined features. In the proposed RBF neural network, the centers are automatically selected using k-means algorithm and estimated weight matrix is recursively updated, utilizing least-mean-square algorithm for better recognition of hand gesture images. The comparative performances are tested on two indigenously developed databases of 24 American sign language hand alphabet.     

用于人机交互的静态手势识别系统

提出并实现一个用于人机交互的静态手势识别系统。基于皮肤颜色模型进行手势分割，并用傅里叶描述子描述轮廓。采用针对小样本特别有效且范化误差有界的支持向量机方法：最小二乘支持向量机（LS－SVM）作为分类器。提出了LS－SVM的增量训练方式，避免了费时的矩阵求逆操作。为实现多类手势识别，利用DAG（Directed Acyclic Graph)将多个两类LS－SVM结合起来。对26个字母手势进行识别，与多层感知器、径向基函数网络等方法比较，LS－SVM的识别率最高，为93．62％。     

A cognitive architecture for Robotic hand posture learning

This paper deals with the design and implementation of a visual control of a robotic system composed of a dexterous hand and video camera. The aim of the proposed system is to reproduce the movements of a human hand in order to learn complex manipulation tasks or to interact with the user. A novel algorithm for robust and fast fingertips localization and tracking is presented. A suitable kinematic hand model is adopted to achieve a fast and acceptable solution to an inverse kinematics problem. The system is part of a cognitive architecture for posture learning that integrates the perceptions by a high-level representation of the scene and of the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces, and to perform complex interactions with the human operator.     

Gesture-based interaction and communication: automatedclassification of hand gesture contours

The accurate classification of hand gestures is crucial in the development of novel hand gesture-based systems designed for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC). A complete vision-based system, consisting of hand gesture acquisition, segmentation, filtering, representation and classification, is developed to robustly classify hand gestures. The algorithms in the subsystems are formulated or selected to optimality classify hand gestures. The gray-scale image of a hand gesture is segmented using a histogram thresholding algorithm. A morphological filtering approach is designed to effectively remove background and object noise in the segmented image. The contour of a gesture is represented by a localized contour sequence whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. Gesture similarity is determined by measuring the similarity between the localized contour sequences of the gestures. Linear alignment and nonlinear alignment are developed to measure the similarity between the localized contour sequences. Experiments and evaluations on a subset of American Sign Language (ASL) hand gestures show that, by using nonlinear alignment, no gestures are misclassified by the system. Additionally, it is also estimated that real-time gesture classification is possible through the use of a high-speed PC, high-speed digital signal processing chips and code optimization     

基于红外传感器和隐马尔可夫模型的动态手势识别

非接触式手势控制交互识别作为一种人机交互的新型技术,摆脱了传统的人机设备限制,更符合人际交流习惯。从其实现原理来看,非接触式手势控制交互识别有多种实现,有基于摄像头的识别,也有基于体感遥控的手势识别。本文采用了基于红外传感器的动态手势识别,其基本实现原理是利用四个定向二极管来感知反射的红外线能量,然后将该数据转换为四个方向的距离信息。对于如何从距离信息识别手势,一般多采用动态时间规整、人工神经网络以及隐马尔可夫模型(HMM, Hidden Markov Model)等模式匹配算法。HMM是一种随时间变化的信号模型,具有自动分割和分类能力,适合进行动态手势识别。本文通过HMM对传感器输出的四个方向距离信息进行训练识别,经过多次试验及调整,使得对于5种手势的识别率平均都达到了75%以上,并且随着手势训练数据的增加,识别率会随之提高。     

人手姿态检测系统的设计与实现

为了增强人机交互的交互性以及遥控操作的可控性,设计并实现了一款基于磁场传感器与微加速度计的人手姿态检测系统。利用坐标变换理论,通过磁场传感器数据解算姿态的翻滚角和俯仰角,然后结合磁场传感器数据解算方位角,并对传感器的噪声、温度漂移以及磁场传感器受外界磁场的干扰进行了补偿。通过实验证明,该系统能准确测量人手姿态。     

引入运动残差与手势连续特性的人手自动定位方法

手势识别在人机交互领域应用广泛,而手势的自动定位始终是一个难点.通常手势的背景中会出现其他运动物体,所以理论上仅基于运动的定位方法不够准确.由于手势属于非刚性运动,其区域内的匹配决具有更大的残差值,因此在块金字塔匹配算法的基础上引入了运动残差理论来确定单帧中相对精确的待定手势区域.接而,为了进一步排除错误的待定区域,在相邻图像序列中运用了基于手势连续性的算法来确定手势的正确运动轨迹,进而确定正确的人手区域.实验分为单帧中定位手势待定区域,以及在图像序列中确定手势轨迹2个环节,并分别获得91.05%和83.0%的正确率,其结果较相关论文的定位方法提高了40%到50%.     

Performance evaluation of sensing fabrics for monitoring physiological and biomechanical variables.

In the last few years, the smart textile area has become increasingly widespread, leading to developments in new wearable sensing systems. Truly wearable instrumented garments capable of recording behavioral and vital signals are crucial for several fields of application. Here we report on results of a careful characterization of the performance of innovative fabric sensors and electrodes able to acquire vital biomechanical and physiological signals, respectively. The sensing function of the fabric sensors relies upon newly developed strain sensors, based on rubber-carbon-coated threads, and mainly depends on the weaving topology, and the composition and deposition process of the conducting rubber-carbon mixture. Fabric sensors are used to acquire the respitrace (RT) and movement sensors (MS). Sensing features of electrodes, instead rely upon metal-based conductive threads, which are instrumental in detecting bioelectrical signals, such as electrocardiogram (ECG) and electromyogram (EMG). Fabric sensors have been tested during some specific tasks of breathing and movement activity, and results have been compared with the responses of a commercial piezoelectric sensor and an electrogoniometer, respectively. The performance of fabric electrodes has been investigated and compared with standard clinical electrodes.     

一种基于肤色特征提取的手势检测识别方法

针对手势识别的准确率和识别速度等问题,提出一种基于肤色特征提取的手势检测识别方法。采用OpenCV自有的Haar分类器自动提取面部区域并建立肤色模型,再以肤色模型检测肤色点集合,去除与面部区域重合点集获得手部轮廓,通过综合计算掌心位置,手掌轮廓凸包和凸性缺陷实现手势识别。经验证,该方法能够稳定地定位手掌区域,识别准确度高并能够在保证识别率的基础上实现对手势的快速识别。     

Chronological pattern indexing: An efficient feature extraction method for hand gesture recognition with Leap Motion

Recently, Hand-Gesture-Recognition (HGR) systems has appreciably change the way of interaction between humans and computers thanks to advanced sensor technologies like the Leap-Motion-Controller (LMC). Despite the success achieved by many state-of-the-art methods, they have not worked on the rich temporal information existing in the sequential hand gesture data and characterizing the discriminative representation of different hand gesture classes. In this paper, we suggest a novel Chronological-Pattern-Indexing (CPI) approach which encodes the temporal orders of patterns for hand gesture time series data acquired by the LMC sensor. We extract a set of temporal patterns from different optimized projections. Then, we compare their temporal order and we encode the whole sequence with the index of the first coming pattern. We repeat these steps until we generate an efficient feature vector modeling the chronological dynamics of the hand gesture. The experiments demonstrate the potential of the proposed CPI approach for HGR systems.     

民族体育教学中的特殊姿态校对视觉监控平台设计

为了发展民族体育、弘扬民族文化,现设计一款民族体育教学中的特殊姿态校对视觉监控平台。该平台由视觉监控模块与特殊姿态校对模块构成。视觉监控模块负责进行特殊姿态数据的采集、量化与传导,讨论了该模块中的MFC监控架构、数据传导通信指令结构、主要硬件连接模式以及数据的高阶低通滤波方法。特殊姿态校对模块用于接收视觉监控模块传导出的特殊姿态量化数据,其利用Processing语言建立GUI优化数据结算空间,通过四元数结算方法完成特殊姿态量化数据向欧拉角的转化,根据欧拉角判定规则确定特殊姿态是否需要校对,并标记校对点。实验结果表明,所设计平台的滤波效果好,监控误差小。     

Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes

In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.     

CH_4监测的反射式光纤传感方法研究

根据CH4的光谱吸收特性,研究了一种基于比尔-朗伯定律的光纤CH4气体传感方法。采用1665nm的分布反馈式激光器(DFB-LD)光源,设计了一种带有环形器的反射式气室结构,实现了传感器小型化。实验结果表明,该方法可探测的最小CH4浓度约为10ppm,系统灵敏度较高且稳定性好。     

遥感卫星接收系统伺服监控软件设计

伺服系统是遥感卫星地面接收系统的重要组成部分,可靠性和安全性要求高。采用单进程、多线程、并行多模块的结构,设计伺服系统监控软件,实现了天线控制、设备监视、信息显示、数据处理、通信及伺服性能指标自动化测试等功能,具有实时性强,自动化程度高,容错性、可靠性及扩展性好等特点。该设计已用于实际工程项目中,实际使用结果表明该设计是合理可行的。     

关于遥感图像的水质监测评价系统设计

本文通过关于遥感图像的水质监测评价系统（RWQMES）的设计,实现了水质遥感图像的分析、处理,水质的预测、评价三部分功能的统一管理。为水污染防治、水资源可持续开发利用及水资源管理的规范化和科学化,提供了有效的信息技术支持。     

光纤传感在电力传输安全监测中的应用研究

传感器、信息等新技术在电力行业的应用,是智能电网实现的重要基础。智能电网需要建立从发电、输电网到配网的电网实时监控系统。文中研究光纤传感技术,利用传感器对输电设备电缆的温度参数进行采集,及时发现升温现象,确保电力传输和生产安全。     

基于ZigBee的可自充电式无线温度传感监测系统

考虑到传统温度监测系统能耗高、成本高、体积大、线路复杂等问题,设计一种基于ZigBee的无线温度监测系统。该系统在正常情况下由锂电池直接供电,在振动环境中通过压电微能源收集环境振动机械能转化为电能给锂电池充电。利用低功耗芯片CC2530和外围器件搭建硬件电路,使用IAR Embedded Workbench软件并借助TI公司的Z-Stack编译软件,通过Lab VIEW软件进行上位机的设计,借助小体积倒F天线(IFA)进行温度数据收发。经测试,该系统在空旷区域传输距离80 m,能实时通过上位机读取需要监测区域的温度,具有一定的可靠性。     

激光雷达监测海面溢油遥感理论

超连续谱激光在保持激光原有的高能量密度和良好的方向性的基础上,还具有较宽测量谱段的特点,因此与传统的被动式遥感方式相比,超连续谱激光雷达具有很多独特的优势。在海面溢油探测领域,对溢油事故的发生进行及时而准确的监测是十分重要的,当前采用的遥感探测技术均存在各种缺陷,而超连续谱激光雷达能够在夜晚和白天持续工作,且由于探测信号为回波信号,因此能够实现较大的探测距离和较高的遥感精度。本文主要从海面溢油监测现状、大气对激光传输的影响以及海面溢油高光谱遥感原理入手,对激光雷达海面溢油遥感理论进行了介绍。