基于深度扫描仪的高辨识度三维人体模型重建方法

3D 照相打印馆人像的打印质量取决于3D 扫描获得的三维人体模型的辨识度。然 而,由于现有3D 人体扫描仪价格昂贵、操作复杂等原因,使得3D 人像打印成本高、耗时长和 打印精度较低。针对这些缺点提出一种基于深度扫描仪重建高辨识度三维人体模型方法。利用 多组深度扫描仪分工协作、优势互补,分别获取高辨识度的人体面部五官点云数据,上半身与 全身表面轮廓点云数据。然后,通过引入特征点和改进的最近点迭代法将采集到的三组点云数 据进行对齐、替换、拼接,将拼接后的无拓扑关系的点云数据进行曲面重构即可获得高辨识度 的三维人体模型。该方法的扫描时间较短,以较低的成本构建了具有高辨识度的三维人像模型。     

使用白色结构光编码的三维几何发型重建

为了实现对头发区域三维模型的快速精确重建,对发型建模提供可行技术方案,利用白色结构光技术搭建三维发型采集系统,并提出一种高频二值条纹的结构光编码算法.该系统使用了4台自制的白色结构光扫描仪,通过预先配置空间位置实现对头部区域的有效覆盖,并利用高频二值条纹编码算法在1s内采集头部数据,提高结构光条纹边缘可辨识度,实现亚像素级数据精度,之后快速重建出包括发型在内的三维头部网格模型.实验结果表明,重建后的三维发型具有高几何精度,达到了当前最优的毫米级别,并验证了采集系统特别是编码算法的有效性和鲁棒性.     

基于傅里叶变换轮廓术的高速齿科三维扫描仪实现

齿科三维扫描仪是数字化齿科修复的重要组成部分,在种植、正畸、嵌体等齿科修复中发挥着数据获取的关键作用.为了实现三维口腔模型的高速采集,提出了一种基于傅里叶变换轮廓测量法的三维重建算法的牙科三维扫描系统.投射光栅图形到牙齿印模上,采集变形光栅图像,对相机捕获的变形条纹图像信息进行预处理,相位解包裹、三维点云重构和获取三维模型,实现了傅里叶变换轮廓术的高速齿科三维扫描仪.经测试,该扫描仪可在9秒内完成牙齿印模扫描,能达到10微米内的扫描精度,性能表现突出.     

三维视觉测量技术中的双目纹理映射技术

设计了基于双目摄像机的三维纹理映射系统,单色和彩色摄像机分别负责三维坐标和颜色信息的采集;建立了双目摄像机的像素匹配模型,并设计了简单的标定方法,确定模型中的内部和结构参数,建立高精度的像素对应关系,配合实现三维数据的纹理映射。还对双目纹理映射方法的测量过程进行了详细描述。经过实验验证,该方法不仅简单易用,还具有高于0.1个像素的匹配精度。     

基于Unity3D平台的三维虚拟城市研究与应用

针对三维虚拟城市系统在高逼真度、强交互性和高精度等方面的不足,提出基于Unity3D平台的三维虚拟城市的实现方法。首先通过三维地形的数据采集和坐标数据统一,实现三维模型的建立和导入,纹理数据的建立和导入。在此基础上结合三维引擎Unity3D平台,实现三维模型数据与二维空间数据整合、三维虚拟漫游、三维物理效应仿真等。该系统采用了真实的数据进行实验,证明了采用该三维虚拟技术很好地满足三维虚拟城市系统的各项指标,并且实现效率高。     

1秒采集的三维头像摄影系统

为了实现高精度、快速度、低成本的三维头像建模,提出一个人体头部的三维摄影系统.该系统能够1 s采集用户的三维信息,3 min内建立照片真实感的三维模型.通过配置多目RGB-Depth相机摄影系统,保证1 s内完成对人体头部的全方位数据采集,进而对所得到的点云数据进行数据增强、配准、表面重建和着色,最终生成高逼真的三维头像模型.     

基于摄影原理的三维纹理重建技术研究

讨论的是基于摄影原理的三维物体表面纹理的重建技术.该技术从国内外研究现状和主要不足出发,对三维物体表面纹理的采集和映射方法进行研究和改进,提出了较为合理的算法.纹理采集过程中分析了回转工作台的结构、纹理采集的原则并建立相关的坐标系;根据拍照和投影变换的原理建立三个坐标系对应点间的关系、推导出坐标变换关系式和转换矩阵;对模型表面微小面片法向量进行均匀化处理;通过计算面片的法向量与纹理采集方向向量的位置关系确定其最佳纹理,最后通过多角度映射达到三维物体表面纹理重建的目的.     

CNN和三维Gabor滤波器的高光谱图像分类

卷积神经网络(CNN)具有强大的特征提取能力,能够有效地提高高光谱图像的分类精度.然而CNN模型训练需要大量的训练样本参与,以防止过拟合,Gabor滤波器以非监督的方式提取图像的边缘和纹理等空间信息,能够减轻CNN模型对训练样本的依赖度及特征提取的压力.为了充分利用CNN和Gabor滤波器的优势,提出了一种双通道CNN和三维Gabor滤波器相结合的高光谱图像分类方法Gabor-DC-CNN.首先利用二维卷积神经网络(2D-CNN)模型处理原始高光谱图像数据,提取图像的深层空间特征;同时利用一维卷积神经网络(1D-CNN)模型处理三维Gabor特征数据,进一步提取图像的深层光谱-纹理特征.连接2个CNN模型的全连接层实现特征融合,并将融合特征输入到分类层中完成分类.实验结果表明,该方法能够有效地提高分类精度,在Indian Pines,Pavia University和Kennedy Space Center 3组数据上分别达到98.95%,99.56%和99.67%.     

利用反射率和距离信息的三维场景数据分割方法

0引言 利用激光三维扫描仪和高分辨率彩色CCD摄像机产生完整的包括三维几何和纹理信息的真实场景模型,对虚拟现实、远程浏览、数字考古及数字城市等具有重要意义,是计算机视觉和机器人领域最大的挑战和热点问题,受到国内外学者的普遍关注[1～3].真实场景三维建模主要包括如下三步:数据分割、几何建模及纹理映射.其中数据分割是后续工作的基础,它将三维点云数据按场景结构分成不同的封闭区域,以去除冗余数据,加快处理速度.并为多视点三维数据配准、几何建模和纹理映射提供准确的基元特征.因此本文主要研究数据分割.     

基于IGWO-XGBoost融合模型的沥青路面抗滑性能评估

为研究沥青路面抗滑性能影响因素,精确预测路面抗滑性能,本文使用Gocator 3110三维智能传感器采集沥青混合料试件表面纹理并使用摆式摩擦仪测试试件表面摩擦系数.针对三维纹理点云数据中的异常数据,提出基于径向基函数(RBF)的邻域插值算法进行数据质量提升.根据修复后的三维纹理点云数据计算出具有代表性的10类宏观纹理特征参数,并采用Pearson系数相关性分析法去除冗余因子,改进模型的输入特征,并构建基于改进灰狼优化算法(IGWO)与XGBoost融合的沥青路面抗滑性能预测模型,预测沥青路面的摩擦系数.结果表明,提出模型的预测精度优于多元线性回归模型、支持向量机回归模型以及基于网格化搜索的XGBoost模型.     

Fast capture of textured full-body avatar with RGB-D cameras

We present a practical system which can provide a textured full-body avatar within 3 s. It uses sixteen RGB-depth (RGB-D) cameras, ten of which are arranged to capture the body, while six target the important head region. The configuration of the multiple cameras is formulated as a constraint-based minimum set space-covering problem, which is approximately solved by a heuristic algorithm. The camera layout determined can cover the full-body surface of an adult, with geometric errors of less than 5 mm. After arranging the cameras, they are calibrated using a mannequin before scanning real humans. The 16 RGB-D images are all captured within 1 s, which both avoids the need for the subject to attempt to remain still for an uncomfortable period, and helps to keep pose changes between different cameras small. All scans are combined and processed to reconstruct the photorealistic textured mesh in 2 s. During both system calibration and working capture of a real subject, the high-quality RGB information is exploited to assist geometric reconstruction and texture stitching optimization.     

3D foot prediction method for low cost scanning

With the rapid development of CAD/CAM technology and information technology, it is becoming possible to satisfy the quality, fit and comfort requirements of footwear design and manufacturing. In the footwear industry, although there are availability of design and manufacture technologies to fulfill the desired requirements, the current methods are very expensive. Cheap and accurate scanners are needed at the retail shop to acquire 3D foot shape information. This paper proposes a prediction method to model foot shapes through scaling a standard foot by using limited parameters. The accuracy of different number of parameters have been evaluated. Given that commercial expensive scanner accuracy range from 0.5 to 1 mm, in order to predict 3D foot shape to an accuracy of around 0.75 mm, foot outline, foot profile, two foot sections and standard foot model were required. The mean modeling errors were 0.76 mm and 0.75 mm of the right foot and left foot respectively. Results indicate that if more sections are used the modeling error decreases but this will increase the cost of the scanner and the computational complexity. This method provides a cost effective method to substitute expensive 3D foot scanners that usually use laser-based technology.Relevance to industry: This method provides the core algorithm for the development of low cost 3D foot scanners for footwear mass-customization. CCD cameras can be used to capture foot profile and outline, while fixed line laser can be used to obtain two key sections. This method reduces the need for expensive linear gears and optical systems.     

Real-time EEG-based emotion monitoring using stable features

In human–computer interaction (HCI), electroencephalogram (EEG) signals can be added as an additional input to computer. An integration of real-time EEG-based human emotion recognition algorithms in human–computer interfaces can make the users experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the applications. Currently, the most accurate EEG-based emotion recognition algorithms are subject-dependent, and a training session is needed for the user each time right before running the application. In this paper, we propose a novel real-time subject-dependent algorithm with the most stable features that gives a better accuracy than other available algorithms when it is crucial to have only one training session for the user and no re-training is allowed subsequently. The proposed algorithm is tested on an affective EEG database that contains five subjects. For each subject, four emotions (pleasant, happy, frightened and angry) are induced, and the affective EEG is recorded for two sessions per day in eight consecutive days. Testing results show that the novel algorithm can be used in real-time emotion recognition applications without re-training with the adequate accuracy. The proposed algorithm is integrated with real-time applications “Emotional Avatar” and “Twin Girls” to monitor the users emotions in real time.     

基于SSVEP与运动想象的实时脑控阿凡达系统

针对当前在线脑机接口交互系统识别正确率比较低、模式单一、算法复杂度高等问题,设计基于SSVEP和运动想象的实时脑控实时算法并应用于脑控阿凡达系统。通过对6名受试者进行离线训练和在线测试,实验结果表明该系统较好地反映受试者的控制意图,所有受试者均可以基本准确地控制机器人,可实现多指令的控制,识别准确率高,可进一步推动脑机接口在实际领域的应用和发展。     

基于图像内容和噪音特征的扫描仪源辨识

以扫描仪获得的数字扫描图像为研究对象,提出一种基于图像内容和噪音特征的扫描仪源辨识方法,提取数字扫描图像中的颜色特征、质量特征和邻或预测特征,生成一个72维的特征向量以辨识扫描仪的来源,并借助支持向量机确定扫描仪的品牌或型号.实验结果表明,该方法具有较高的分类精度,并且在数字扫描图像被压缩或剪切的情况下均具有较好的鲁棒...     

What is the effective key length for a block cipher: an attack on every practical block cipher

Recently,several important block ciphers are considered to be broken by the brute-force-like cryptanalysis,with a time complexity faster than the exhaustive key search by going over the entire key space but performing less than a full encryption for each possible key.Motivated by this observation,we describe a meetin-the-middle attack that can always be successfully mounted against any practical block ciphers with success probability one.The data complexity of this attack is the smallest according to the unicity distance.The time complexity can be written as 2k(1-),where>0 for all practical block ciphers.Previously,the security bound that is commonly accepted is the length k of the given master key.From our result we point out that actually this k-bit security is always overestimated and can never be reached because of the inevitable loss of the key bits.No amount of clever design can prevent it,but increments of the number of rounds can reduce this key loss as much as possible.We give more insight into the problem of the upper bound of effective key bits in block ciphers,and show a more accurate bound.A suggestion about the relationship between the key size and block size is given.That is,when the number of rounds is fixed,it is better to take a key size equal to the block size.Also,effective key bits of many well-known block ciphers are calculated and analyzed,which also confirms their lower security margins than thought before.The results in this article motivate us to reconsider the real complexity that a valid attack should compare to.     

Backward walking simulation of humans using optimization

The objective of this study is to formulate, simulate and study the backward walking motion of a full-body skeletal digital human model using an optimization approach. Predictive dynamics is used to simulate the task in which joint angle profiles are treated as primary unknowns in the formulation. The joint torques are treated as dependent variables that are evaluated directly from the equations of motion. For the performance measure, the normalized dynamic effort represented by the integral of the squares of all the normalized joint torques is minimized subject to the associated physical constraints. Backward walking at different speeds is simulated and analyzed. The backward walking is validated with motion capture data and the available data in the literature. The results of the backward walking motion are compared to those of the forward walking motion in order to study the differences between the two walking patterns. It is seen that the joint torque profiles for hip and knee of backward walk are quite similar to those of forward walk with reverse sequence, but with different time duration of flexion and extension activations. These findings can impact many fields, such as improvement of human performance, rehabilitation from injuries, and others.     

建立Avatar感知能力与自主行为选择模型的新方法

在人脸重构系统与智能虚拟环境系统的研究基础上，提出了一种基于Agent的建立虚拟人感知能力与行为选择模型的新方法。该方法把虚拟人(Avatar)当成一个Agent，它在虚拟场景中能够识别三维物体以及利用传感器计算物体的运动方向。Avatar的行走采用骨架模型来进行仿真，并且其行走行为由七个基本动作行为组成。实验结果显示新方法是具有较好的效果。     

视觉检测中的光照强度优化研究

为了实现自适应控制光照强度,保障视觉检测系统获得高质量原始图像,研究了彩色图像成像感知模型,建立了图像灰度值与光源光照强度的关系,提出基于HIS彩色空间,单独使用I分量进行光照强度评价,给出了基于数字图像的清晰度和高频信息的光照强度评价函数,并设计了光照强度调控实验系统,通过实验分析了不同测度函数对光照强度变化的评价结果,取得了与人类主观评价一致的结果。实验结果表明,通过提出的方法控制光源强度,可以使视觉系统获得高质量的原始图像,保证检测的质量和精度。     

Multilevel active registration for kinect human body scans: from low quality to high quality

Registration of 3D human body has been a challenging research topic for over decades. Most of the traditional human body registration methods require manual assistance, or other auxiliary information such as texture and markers. The majority of these methods are tailored for high-quality scans from expensive scanners. Following the introduction of the low-quality scans from cost-effective devices such as Kinect, the 3D data capturing of human body becomes more convenient and easier. However, due to the inevitable holes, noises and outliers in the low-quality scan, the registration of human body becomes even more challenging. To address this problem, we propose a fully automatic active registration method which deforms a high-resolution template mesh to match the low-quality human body scans. Our registration method operates on two levels of statistical shape models: (1) the first level is a holistic body shape model that defines the basic figure of human; (2) the second level includes a set of shape models for every body part, aiming at capturing more body details. Our fitting procedure follows a coarse-to-fine approach that is robust and efficient. Experiments show that our method is comparable with the state-of-the-art methods for high-quality meshes in terms of accuracy and it outperforms them in the case of low-quality scans where noises, holes and obscure parts are prevalent.