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.     

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.     

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。     

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.     

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.     

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     

Real-time one-shot learning gesture recognition based on lightweight 3D Inception-ResNet with separable convolutions

Pattern Analysis and Applications - Gesture recognition is a popular research field in computer vision and the application of deep neural networks greatly improves its performance. However, the...     

On visual complexity of 3D shapes

We present an approach to compute the perceived complexity of a given 3D shape using the similarity between its views. Previous studies on 3D shape complexity relied on geometric and/or topological properties of the shape and are not appropriate for incorporating results from human shape perception which claim that humans perceive 3D shapes as organizations of 2D views. Therefore, we base our approach to computing 3D shape complexity on the (dis)similarity matrix of the shape's 2D views. To illustrate the application of our approach, we note that simple shapes lead to similar views whereas complex ones result in different, dissimilar views. This reflected in the View Similarity Graph (VSG) of a shape as tight clusters of points if the shape is simple and increasingly dispersed points as it gets more complex. To get a visual intuition of the VSG, we project it to 2D using Multi-Dimensional Scaling (MDS) and introduce measures to compute shape complexity through point dispersion in the resulting MDS plot. Experiments show that results obtained using our measures alleviate some of the drawbacks present in previous approaches.     

Semantics-driven best view of 3D shapes

The problem of automatically selecting the pose of a 3D object that corresponds to the most informative and intuitive view of the shape is known as the best view problem. In this paper we address the selection of the best view driven by the meaningful features of the shape, in order to maximize the visibility of salient components from the context or from the application point of view. Meaningful features can be automatically detected by means of semantic-oriented segmentations: we tested several approaches with very pleasant results in the automatic generation of thumbnails for large 3D model databases.     

Symmetry discovery and retrieval of nonrigid 3D shapes using geodesic skeleton paths

Multimedia Tools and Applications - In this paper, we propose a skeleton path based approach for symmetry discovery and retrieval of nonrigid 3D shapes. The main idea is to match skeleton graphs by...     

SymmSketch: Creating symmetric 3D free-form shapes from 2D sketches

Computational Visual Media - This paper presents SymmSketch—a system for creating symmetric 3D free-form shapes from 2D sketches. The reconstruction task usually separates a 3D symmetric...     

Geometry curves: A compact representation for 3D shapes

We propose a novel compact surface representation, namely geometry curves, which record the essence of shape geometry and topology. The geometry curves mainly contain two parts: the interior and boundary lines. The interior lines, which correspond to the feature lines, record the geometry information of the 3D shapes; the boundary lines, which correspond to the boundary or fundamental polygons, record the topology information of the 3D shapes. As a vector representation, geometry curves can depict highly complex geometry details. The concept of geometry curves can be utilized in many potential applications, e.g., mesh compression, shape modeling and editing, animation, and level of details. Furthermore, we develop a procedure for automatically constructing geometry curves which obtain an excellent approximation to the original mesh.