Three‐dimensional interaction and autostereoscopic display system using gesture recognition

We propose a 3D interaction and autostereoscopic display system that use gesture recognition, which can manipulate virtual objects in the scene directly by hand gestures and can display objects in 3D stereoscopy. The system consists of a gesture recognition and manipulation part as well as an autostereoscopic display as an interactive display part. To manipulate the 3D virtual scene, a gesture recognition algorithm is proposed, which use spatial‐temporal sequences of feature vectors to match predefined gestures. To get smooth 3D visualization, we utilize the programmable graphics pipeline in graphic processing unit to accelerate data processing. We develop a prototype system for 3D virtual exhibition. The prototype system reaches frame rates of 60 fps and operates efficiently with a mean recognition accuracy of 90%.     

基于表观特征分析的手势识别及其应用

针对复杂背景下的手势识别容易受到环境干扰造成的识别困难问题,通过分析手势的表观特征,提出并实现了一种可用于自然人机交互的手势识别算法。该算法基于Kinect深度图像实现手势区域分割,然后提取手势手指弧度、指间弧度、手指数目等具有旋转缩放不变性的表观特征,运用最小距离法实现快速分类。并将该算法成功运用于实验室三指灵巧手平台,达到了理想的控制效果。实验表明该算法具有良好的鲁棒性,针对九种常用手势,平均识别率达到94.3%。     

A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition

The role of gesture recognition is significant in areas like human‐computer interaction, sign language, virtual reality, machine vision, etc. Among various gestures of the human body, hand gestures play a major role to communicate nonverbally with the computer. As the hand gesture is a continuous pattern with respect to time, the hidden Markov model (HMM) is found to be the most suitable pattern recognition tool, which can be modeled using the hand gesture parameters. The HMM considers the speeded up robust feature features of hand gesture and uses them to train and test the system. Conventionally, the Viterbi algorithm has been used for training process in HMM by discovering the shortest decoded path in the state diagram. The recursiveness of the Viterbi algorithm leads to computational complexity during the execution process. In order to reduce the complexity, the state sequence analysis approach is proposed for training the hand gesture model, which provides a better recognition rate and accuracy than that of the Viterbi algorithm. The performance of the proposed approach is explored in the context of pattern recognition with the Cambridge hand gesture data set.     

基于深度信息的手势识别算法研究

针对在复杂背景中传统手势识别算法的识别率低问题,利用Kinect的深度摄像头获取深度图像,分割出手势区域后进行预处理;提取手势的几何特征,并提出深度信息的同心圆分布直方图特征,融合手势的几何特征和深度信息的同心圆分布直方图特征;学习训练随机森林分类器进行手势识别.文中通过在复杂背景条件下对常见的“石头”、“剪刀”、“布”3种手势进行测试,实验结果表明:文中所提方法具有很好的平移,旋转和缩放不变性,能适应复杂环境的变化.     

融合关节旋转特征和指尖距离特征的手势识别

作为人机交互的重要方式,手势交互和识别由于其具有的高自由度而成为计算机图形学、虚拟现实与人机交互等领域的研究热点.传统直接提取手势轮廓或手部关节点位置信息的手势识别方法,其提取的特征通常难以准确表示手势之间的区别.针对手势识别中不同手势具有的高自由度以及由于手势图像分辨率低、背景杂乱、手被遮挡、手指形状尺寸不同、个体差异性导致手势特征表示不准确等问题,本文提出了一种新的融合关节旋转特征和指尖距离特征的手势特征表示与手势识别方法.首先从手势深度图中利用手部模板并将手部看成链段结构提取手部20个关节点的3D位置信息;然后利用手部关节点位置信息提取四元数关节旋转特征和指尖距离特征,该表示构成了手势特征的内在表示;最后利用一对一支持向量机对手势进行有效识别分类.本文不仅提出了一种新的手势特征表示与提取方法,该表示融合了关节旋转信息和指尖距离特征;而且从理论上证明了该特征表示能唯一地表征手势关节点的位置信息;同时提出了基于一对一SVM多分类策略进行手势分类与识别.对ASTAR静态手势深度图数据集中8类中国数字手势和21类美国字母手势数据集分别进行了实验验证,其分类识别准确率分别为99.71%和85.24%.实验结果表明,本文提出的基于关节旋转特征和指尖距离特征的融合特征能很好地表示不同手势的几何特征,能准确地表征静态手势并进行手势识别.     

Hand gesture recognition based on dynamic Bayesian network framework

In this paper, we propose a new method for recognizing hand gestures in a continuous video stream using a dynamic Bayesian network or DBN model. The proposed method of DBN-based inference is preceded by steps of skin extraction and modelling, and motion tracking. Then we develop a gesture model for one- or two-hand gestures. They are used to define a cyclic gesture network for modeling continuous gesture stream. We have also developed a DP-based real-time decoding algorithm for continuous gesture recognition. In our experiments with 10 isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. In the case of recognizing continuous stream of gestures, it recorded 84% with the precision of 80.77% for the spotted gestures. The proposed DBN-based hand gesture model and the design of a gesture network model are believed to have a strong potential for successful applications to other related problems such as sign language recognition although it is a bit more complicated requiring analysis of hand shapes.     

Hand gesture recognition based on concentric circular scan lines and weighted K-nearest neighbor algorithm

Human-computer interactions based on hand gestures are of the most popular natural interactive modes, which severely depends on real-time hand gesture recognition approaches. In this paper, a simple but effective hand feature extraction method is described, and the corresponding hand gesture recognition method is proposed. First, based on a simple tortoise model, we segment the human hand images by skin color features and tags on the wrist, and normalize them to create the training dataset. Second, feature vectors are computed by drawing concentric circular scan lines (CCSL) according to the center of the palm, and linear discriminant analysis (LDA) algorithm is used to deal with those vectors. Last, a weighted k-nearest neighbor (W-KNN) algorithm is presented to achieve real-time hand gesture classification and recognition. Besides the efficiency and effectiveness, we make sure that the whole gesture recognition system can be easily implemented and extended. Experimental results with a user-defined hand gesture dataset and multi-projector display system show the effectiveness and efficiency of the new approach.     

一种基于嵌入式系统实时交互的手势识别方法*

提出了一种在单摄像头条件下基于嵌入式系统的手势识别方法。通过拟合手势图的外接多边形,找出其所对应的手势缺陷图,并建立手势与手势缺陷图的一一映射,利用手势缺陷图的特征来匹配和识别不同的手势。算法还将手势的跟踪与识别有机地统一起来,通过预测下一帧中手势出现的粗略位置大大降低识别步骤的计算量。该算法在实际应用的嵌入式平台下,能快速、准确地实现手势的识别,能够满足实时人机交互的要求。     

复杂环境中多信息融合的手势识别

针对复杂环境中的手势识别问题,提出了一种融合深度信息和红外信息的手势识别方法。首先利用Kinect摄像头的深度信息进行动态实时手势分割,然后融合红外图像复原手势区域。解决了实时手势分割和利用手势的空间分布特征进行手势识别时由于分割的手势区域有缺损或有人脸干扰时识别率低的问题。经实验验证,提出的方法不仅不受环境光线的影响,而且可以识别区分度较小的手势,对旋转、缩放、平移的手势识别也具有鲁棒性。对于区分度较大的手势,识别率高达100%。     

A novel approach for dynamic hand gesture recognition using contour-based similarity images

A novel approach is proposed for the recognition of moving hand gestures based on the representation of hand motions as contour-based similarity images (CBSIs). The CBSI was constructed by calculating the similarity between hand contours in different frames. The input CBSI was then matched with CBSIs in the database to recognize the hand gesture. The proposed continuous hand gesture recognition algorithm can simultaneously divide the continuous gestures into disjointed gestures and recognize them. No restrictive assumptions were considered for the motion of the hand between the disjointed gestures. The proposed algorithm was tested using hand gestures from American Sign Language and the results showed a recognition rate of 91.3% for disjointed gestures and 90.4% for continuous gestures. The experimental results illustrate the efficiency of the algorithm for noisy videos.     

基于RGBD数据的静态手势识别

提出一种基于RGBD数据的手势识别方法,首先采用融合深度信息和彩色信息的手势分割算法分割出手势区域;其次提取静态手势轮廓的圆形度、凸包点及凸缺陷点、7Hu矩特征组成特征向量;最后采用SVM进行静态手势识别。实验结果表明,该方法能有效地识别预定义的5种静态手势,且对环境的适应性比较强。     

基于自适应子空间在线PCA的手势识别

基于视觉的手势识别系统的学习一般是离线的,导致系统对新手势的正确识别需要重新离线学习,因此系统实时性、可扩展性和鲁棒性较差,不适合认知发育的智能框架。文中提出了基于自适应子空间在线PCA的手势识别方法。该方法通过计算样本投影系数向量的PCA来实现子空间在线更新,并根据新样本与已学习样本的差异程度,调整子空间更新策略,使算法自适应于不同情况,减少计算和存储开销,实现增量的在线学习和识别手势的目的。实验表明,本文方法能处理未知手势问题,实现手势在线积累和更新,逐渐增强系统识别能力。     

动态手势识别及在PPT播放当中的应用研究

本文主要研究了动态手势识别在PPT播放当中的应用,即在播放过程中能够通过动态手势控制PPT的翻页.首先在动态手势检测的研究中,通过采用背景差分和基于HSV的肤色建模相结合的方法,分割出了较好的动态手势图像.其次,通过对几种动态手势的跟踪算法的分析与比较,采用了主流的非线性跟踪算法W勇瞬’惴？通过对得到的手势图像中的V分量值的人为设置,消除了在跟踪过程中脸部及其他的类肤色干扰,从而避免了手势跟踪过程中的遮挡问题.最后通过采用隐马尔可夫模型与阈值Max相结合的方法,实现了对PPT播放的实时控制.实验结果表明动态手势的识别能够应用到PPT播放当中,并最终实现了利用动态手势来实时控制PPT的翻页.     

基于Kinect深度图像的指尖检测与手势识别

针对基于普通摄像头的手势识别系统在不同光照条件和复杂环境下易受影响的问题,提出一种基于kinect深度图像进行指尖检测和手势识别的算法. 首先利用Kinect传感器获取深度图像,再利用OpenNI手部跟踪器检测出手部的位置,根据手部位置对手势进行深度阈值分割. 提出一种结合凸包和曲率检测指尖的算法,检测出指尖数目和位置后,计算出包括指尖和手掌水平方向的夹角、相邻两个指尖夹角以及指尖与掌心的距离的特征向量,最后利用支持向量机（SVM）对预定的9种数字手势进行识别. 实验邀请5位实验者在复杂环境下每个手势做30次,每次的手势角度不同,实验结果表明该方法能够准确检测出指尖的数目和位置,9种数字手势平均识别率达到97.1%,该方法使用特征简单,实时性好,有较好的鲁棒性.     

基于Kinect的指尖检测与手势识别方法

针对基于视频的弯曲指尖点识别难、识别率不高的问题,提出一种基于深度信息、骨骼信息和彩色信息的手势识别方法。该方法首先利用Kinect相机的深度信息和骨骼信息初步快速判定手势在彩色图像中所在的区域,在该区域运用YCrCb肤色模型分割出手势区域;然后计算手势轮廓点到掌心点的距离并生成距离曲线,设定曲线波峰与波谷的比值参数来判定指尖点;最后结合弯曲指尖点特征和最大内轮廓面积特征识别出常用的12个手势。实验结果验证阶段邀请了6位实验者在相对稳定的光照环境条件下来验证提出的方法,每个手势被实验120次,12种手势的平均识别率达到了97.92%。实验结果表明,该方法能快速定位手势并准确地识别出常用的12种手势,且识别率较高。     

基于深度信息的实时手势识别和虚拟书写系统

鉴于无接触体感交互技术在人机交互领域的成功应用,提出了一种基于Kinect深度相机的实时隔空虚拟书写方法。结合颜色和深度数据检测和分割出手掌区域;进一步,通过修改的圆扫描转换算法获得手指的个数,以识别不同的手势指令;根据指尖检测从指尖的运动轨迹分割出独立的字符或汉字运动轨迹,并采用随机森林算法识别该字符或汉字。这种基于深度信息的手势检测和虚拟书写方法可以克服光照和肤色重叠的影响,可靠实时地检测和识别手势和隔空书写的文字,其识别率达到93.25%,识别速度达到25 frame/s。     

Static hand gesture recognition using neural networks

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.     

Gesture recognition using Bezier curves for visualization navigation from registered 3-D data

This paper presents a gesture recognition system for visualization navigation. Scientists are interested in developing interactive settings for exploring large data sets in an intuitive environment. The input consists of registered 3-D data. A geometric method using Bezier curves is used for the trajectory analysis and classification of gestures. The hand gesture speed is incorporated into the algorithm to enable correct recognition from trajectories with variations in hand speed. The method is robust and reliable: correct hand identification rate is 99.9% (from 1641 frames), modes of hand movements are correct 95.6% of the time, recognition rate (given the right mode) is 97.9%. An application to gesture-controlled visualization of 3D bioinformatics data is also presented.     

图像识别的静态手势识别与动态跟踪系统设计

本文研究了图像手势识别和增强现实技术,设计了可以进行静态手势识别和动态跟踪的系统,通过提前录入不同手势,利用皮肤颜色对图像进行OSTU自适应阈值划分,建立二值化图像,与已知的手势进行匹配,以得到手势结果.实验结果表明,准确率达到96.8％,识别速度达到0.55 s.动态跟踪利用检测每帧图像中手部的位置进行定位和捕捉,图...