基于Bag of Features的手势识别

基于视觉的手势识别中,手势的识别效果易受手势旋转,光照亮度的影响,针对该问题,借鉴了目标识别和图像检索领域的Bag of Features(特征袋)算法,将Bag of Features算法应用到手势识别领域.通过SURF(加速鲁棒性特征)算法提取手势图像的特征描述符,使手势对尺度、旋转、光照具有很强的适应力,再应用Bag of Features算法把SURF特征描述符映射到一个统一维度的向量,即Bag of Features特征向量,再用支持向量机对图像得到的特征向量进行训练分类.实验结果表示,该方法不仅具有较高的时间效率,满足手势识别的实时性,而且即使在很大角度的旋转以及亮度的变化下,仍能达到较高的识别率.     

基于单目摄像头的3D动态手势交互

传统的手势交互都需要借助于Leap Motion或Kinect等专用交互设备。以图像通道转换、二值化等图像处理方式提取手势,以手势平面坐标值的变化获取手势的平面移动信息,以手势面积的变化解决了手势深度的问题。通过绘制手势轮廓结合自创的图像匹配算法计算不同图像的匹配率,用最高匹配率选择相对应的手势运动信息。通过摄像头坐标系到3D场景坐标系之间的转换,结合三维图形的几何变换计算变换矩阵,实现手的空间移动与旋转。在不借助专用的手势交互设备的情况下,实现单目摄像头的动态手势交互。     

Generating 3D architectural models based on hand motion and gesture

This paper presents a novel method for rapidly generating 3D architectural models based on hand motion and design gestures captured by a motion capture system. A set of sign language-based gestures, architectural hand signs (AHS), has been developed. AHS is performed on the left hand to define various “components of architecture”, while “location, size and shape” information is defined by the motion of Marker-Pen on the right hand. The hand gestures and motions are recognized by the system and then transferred into 3D curves and surfaces correspondingly. This paper demonstrates the hand gesture-aided architectural modeling method with some case studies.     

使用三维智能剪刀算法的PACS设计

为了提高PACS的医学影像处理的效率和准确性,在三维智能剪刀技术的基础上,结合三维图形处理和医学影像处理技术,设计和开发了智能PACS.不仅可以管理医学影像资料,还可以高效自动地辅助医生对脏器和肿瘤进行分割标识,医生可以在这些数据的基础上对肿瘤的疗效和预后进行准确的评估.该系统不仅在算法层面有所创新,而且把最新的学术成果应用到医疗应用第一线,有重要的理论价值和强烈的应用需求,对公共卫生起到明显促进作用,可以产生巨大的社会效益.     

基于多个关键点对应性的手部动态重建

提出了一种新的基于单视图深度序列的手部运动跟踪和表面重建方法。在假设任 意一对关键点的对应性在所有手部姿态上均一致基础上,使用一个平滑的手部网格模板来提供 形状和拓扑先验,引入多个能量函数构造输入扫描与模板之间三维关键点到关键点的对应性, 并将其整合到一个可用的非刚性配准管线中,以实现精确的表面拟合。通过最小化手部模板和 输入深度图像序列之间的误差来捕获非刚性的手部运动。采用迭代求解的方法,通过显式的关 键点到关键点之间的对应性,逐步细化手部关节区域的变形,从而达到快速收敛和合理变形的 目的。在含有噪声的手部深度图像序列上的大量实验表明,该方法能够重建精确的手部运动, 并且对较大的变形和遮挡具有鲁棒性。     

Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization

This paper presents an unsupervised algorithm for co-segmentation of a set of 3D shapes of the same family. Taking the over-segmentation results as input, our approach clusters the primitive patches to generate an initial guess. Then, it iteratively builds a statistical model to describe each cluster of parts from the previous estimation, and employs the multi-label optimization to improve the co-segmentation results. In contrast to the existing “one-shot” algorithms, our method is superior in that it can improve the co-segmentation results automatically. The experimental results on the Princeton Segmentation Benchmark demonstrate that our approach is able to co-segment 3D shapes with significant variability and achieves comparable performance to the existing supervised algorithms and better performance than the unsupervised ones.     

A multimedia presentation system using a 3D gesture interface in museums

Multimedia presentations have become an indispensable feature of museum exhibits in recent years. Advances in technology have increased the relevance of studying digital communication using computational devices. Devices, such as multi-touch screens and cameras, are essential for natural communication, and obvious applications involve entertainment to attract users. This study focused on the use of cameras to support natural interaction of visitors during museum presentations. We first outlined a platform called the “U-Garden,” comprising a set of tools to assist application designers in developing movement-based projects that employ camera tracking. We then established a rationale with which to base the design of such presentation tools. This system supplies interactive power to natural interaction based on depth image streams, and provides tracking results to designers for producing numerous fascinating applications that appeal to more diverse interactive imaginations.     

View-invariant gesture recognition using 3D optical flow and harmonic motion context

This paper presents an approach for view-invariant gesture recognition. The approach is based on 3D data captured by a SwissRanger SR4000 camera. This camera produces both a depth map as well as an intensity image of a scene. Since the two information types are aligned, we can use the intensity image to define a region of interest for the relevant 3D data. This data fusion improves the quality of the motion detection and hence results in better recognition. The gesture recognition is based on finding motion primitives (temporal instances) in the 3D data. Motion is detected by a 3D version of optical flow and results in velocity annotated point clouds. The 3D motion primitives are represented efficiently by introducing motion context. The motion context is transformed into a view-invariant representation using spherical harmonic basis functions, yielding a harmonic motion context representation. A probabilistic Edit Distance classifier is applied to identify which gesture best describes a string of primitives. The approach is trained on data from one viewpoint and tested on data from a very different viewpoint. The recognition rate is 94.4% which is similar to the recognition rate when training and testing on gestures from the same viewpoint, hence the approach is indeed view-invariant.     

基于CCD的三维形面测量系统研究

首先介绍了根据激光三角测距原理并采用片光照明、面阵CCD接收的三维形面检测原理及对映函数法原理,系统只用一个CCD测量头即可完成对物体进行360°的二维扫描,使测量系统结构简单、便于控制。其次分析了影响其测量准确度的几个主要因素,针对其各自的特点给出了相应的解决途径。实验证明系统设计方法是可行的。     

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%。     

基于特征包支持向量机的手势识别

针对类肤色信息或复杂背景的影响,难以通过手势分割得到精确手势轮廓而对后期手势识别率与实时交互的影响,提出了一种基于特征包支持向量机(BOF-SVM)的手势识别方法。采用SIFT算法提取手势图像局部不变性特征点,将手势局部特征向量(尺度不变特征变换(SIFT)描述子)进行K-means聚类生成视觉码书,并通过视觉码书量化每一幅手势图像的视觉码字集合,以此获得手势图像的固定维数的表征向量来训练支持向量机(SVM)多类分类器。该方法只需框定手势所在区域,无需精确地分割人手。实验表明,该方法对9种交互手势的平均识别率达到92.1%,并具有很好的鲁棒性及实时性,能适应环境的变化。     

一种基于手机加速度传感器的三维手势身份认证方法

针对手机用户安全问题,提出一种基于手机加速度传感器的手势身份认证方法。采用均值—方差归一化方式对三维手势数据进行归一化处理;采用门限值方法截取手势动作,去除干扰数据;认证算法采用模板匹配的方式,通过设计的均值—动态时间归整(A-DTW)算法对参考模板和测试模板进行比较,判断用户的真实性。仿真结果显示:该算法方便可行,具有较高的识别率。     

An efficient algorithm for 3D hand gesture recognition using combined neural classifiers

Gestures are the dynamic movements of hands within a certain time interval, which are of practical importance in many areas, such as human–computer interaction, computer vision, and computer graphics. The human hand gesture can provide a free and natural alternative to today’s cumbersome interface devices so as to improve the efficiency and effectiveness of human–computer interaction. This paper presents a neural-based combined classifier for 3D gesture recognition. The combined classifier is based on varying the parameters related to both the design and training of neural network classifier. The boosting algorithm is used to make perturbation of the training set employing the Multi-Layer Perceptron as base classifier. The final decision of the ensemble of classifiers is based on the majority voting rule. Experiments performed on 3D gesture database show the robustness of the proposed technique.     

基于伪三维卷积神经网络的手势姿态估计

大多数现有的基于深度学习的手势姿态估计方法都使用标准三维卷积神经网络提取三维特征,估计手部关节坐标。该方法提取的特征缺乏手部的多尺度信息,限制了手势姿态估计的精度。另外,由于三维卷积神经网络巨大的计算成本和内存需求,这些方法常难以满足实时性要求。为了克服这些缺点,提出以空间滤波器和深度滤波器级联的方式模拟三维卷积,减少网络参数量。同时,在各个尺度上提取手势姿态特征并加以整合,充分利用手势的三维信息。实验表明,该方法能有效提高手势姿态估计精度,减小模型尺寸,且在具有单块GPU的计算机上能以超过119 fps的速度运行。     

3-Draw: a tool for designing 3D shapes

A fundamentally new type of CAD system for designing shape that is intuitive, easy to use, and powerful is presented. It is based on a paradigm that can be described as designing directly in 3-D. By virtue of two hand-held sensors, designers using 3-Draw to sketch their ideas in the air feel as if they're actually holding and working on objects. Current design practice and related work are reviewed, and current work on 3-Draw is summarized. To capture the flavor of 3-Draw, construction of a sample model of a 12-m yacht is described. 3-Draw's features and data structures are discussed     

一种在3D手势交互中模拟鼠标的方法

针对当下3D手势交互在执行2D交互任务时用户体验不佳,提出了一种模拟鼠标2D交互方式的3D手势交互技术。根据ISO9241-9标准,设计多方向点选实验,用于比较该3D手势交互技术、一般3D手势交互技术和日常鼠标这三种交互技术在执行典型2D交互任务时的标准吞吐量、指向-选择时间和错误率。以调查问卷的方式评估该交互技术的主观舒适性。结果显示,与一般的手势交互相比,在不降低吞吐率的情况下,该交互技术在执行2D交互任务时可以获得更高的准确率以及更好的舒适度体验。