基于编码结构光和外极线约束的自由曲面三维轮廓术

提出了一种新的基于编码结构光和外极线约束的自由曲面立体视觉测量方法,这种方法有效解决了立体视觉中的图像匹配问题;外极线约束将可能的候选点限制在直线分布,引入编码光用来确定光栅条纹的级次;这种方法极大地减小了图像匹配的运算量,同时减小了错误匹配的概率;实验表明,采用编码光栅投影视觉测量能够高效准确地测量自由曲面的三维轮廓.     

基于结构光技术的动物内脏三维重建研究

针对内窥镜手术监控中二维图像三维重建的需求,对结构光三维重建中的标定和匹配中的关键技术进行改进,使用改进后的标定方法和编码策略有效重建了动物内脏目标。为减少错检,在格雷码条纹之间加入一个像素宽的红色,来判断条纹边界像素点的存在与否。使用灰度重心法可精确实现条纹边界定位。针对三步相移法标定投影仪过程中相位去包络的不稳定性问题,使用双频投影栅线法,避免了复杂易产生噪声的去包络过程。实验结果表明,使用文中提出的策略可以有效重建动物内脏目标。     

基于极线几何的改进多约束图像立体匹配

针对计算机视觉和模式识别领域基本而重要的问题--立体匹配,提出了一种基于极线几何、结合特征匹配与区域匹配、视差梯度约束等多约束立体匹配算法,实现图像快速准确匹配.该算法将现有的基于特征和基于窗口匹配两种方法相结合,并加入视差梯度等约束条件,有效弥补了单一匹配算法的不足,同时增强了算法适应性.实验表明,该算法具有更高的求解质量和求解效率,可以满足双目立体视觉系统的需要.     

基于外极线约束的快速精确立体匹配算法

针对三维运动捕捉系统标记点匹配中存在标记点交叠等情况和速度要求,提出利用平行相机结构,使用统计匹配像素点的平均高度差计算相机间的高度差,并将其与外极线约束相结合将匹配标记点搜索范围限制在一个特定区域内,与传统的利用外极线约束需要在整条直线上进行搜索相比,将搜索范围限制在一个估算点周围,减小了搜索区域,提高了匹配速度。对于标记点存在交叠的情况,使用聚类的方法和加权距离最小值得到正确的匹配标记点,从而提高了立体匹配的精度。实验结果验证了算法的有效性。     

低成本微型结构光动态三维重建系统研究

基于激光散斑投射的结构光技术只需单张图像即可实现三维重建,是重要的动态测量手段之 一。然而,散斑结构光技术在实际中的测量精度较低,无法满足精确测量需求。该研究开发了一套低 成本微型结构光系统,提出一种高精度的双目摄像机标定优化方法以及基于两步旋转的立体图像校正 方法,提高了系统参数的标定和测量精度。其中,测量系统由一个微型的光学发射器和两个摄像头组 成。通过将带有伪随机斑点的单张图案投影到目标对象的表面上,并通过双目相机成像,最终利用特 征匹配和三角原理恢复目标的深度和轮廓信息。经测试,所提出的微型结构光系统在使用优化的标定 和校正参数的条件下,对圆球、圆柱等标准体的测量精度相比传统标定方法显著提升。其中,球体重 建误差为 1.05 mm,精度提升 52.38%;圆柱重建误差为 1.61 mm,精度提高了 63.98%。     

基于极线约束的ORB特征匹配算法

图像匹配是机器视觉领域的基础核心课题,针对当前ORB (Oriented FAST and Rotated BRIEF)图像特征匹配算法虽然执行速度快,但是匹配质量不高的问题,提出一种通过极线约束来改进ORB匹配的算法。通过合理设计Hamming阈值大小来提高初始匹配点数量,采用RANSAC和8点改进法计算基本矩阵,应用极线约束剔除误匹配保留大量优质匹配点。仿真实验结果证明,算法改进后的优质匹配点数量可达原始算法的2-3倍,同时极大地提高了匹配点的质量,证明了算法的有效性。     

线结构光传感器结构参数标定的新方法

本文提出了一种简单、快速、高精度的线结构光传感器结构参数的标定方法．为求得光平面和摄像机间的位置关系，设计了一种简单、实用的梯形靶标，以测量光平面上点的坐标，并采用罚函数约束求解参数．该方法对可见光和不可见光的场合均适用．实验证明，这种方法具有较高精度．     

基于彩色结构光的实时三维重建

提出一种新颖的基于彩色结构光的实时三维重建方法RRCS（Real-time 3D Reconstruction based on Color-coded Structured light）,利用投影在物体表面的彩色结构光的彩色信息,快速准确地还原相位,计算出物体的高度,最终重建物体的三维模型。针对RRCS的特殊性,设计了基于拓扑排序的解相位算法,以及针对点线的快速三角网化算法。实验结果表明,RRCS对设备的要求低,效果较好,具备基本的实时性能。     

基于极线几何约束的非标定图像的立体匹配

立体匹配是计算机视觉领域的一个关键问题，是计算机三维重建中的一个核心问题。基于极线几何约束是特征匹配中最强有力的约束，独立于场景结构，因此将特征匹配与极线几何有机结合起来，实现了一个鲁棒的匹配算法在非标定图像中的应用。其中，对基础矩阵估计及其引导匹配均采用点到极线距离最小约束准则。对真实图像的实验表明，该算法具有较好的实用性。     

A low cost 3D scanner based on structured light

Automatic 3D acquisition devices (often called 3D scanners) allow to build highly accurate models of real 3D objects in a cost- and time-effective manner. We have experimented this technology in a particular application context: the acquisition of Cultural Heritage artefacts. Specific needs of this domain are: medium-high accuracy, easy of use, affordable cost of the scanning device, self-registered acquisition of shape and color data, and finally operational safety for both the operator and the scanned artefacts. According to these requirements, we designed a low-cost 3D scanner based on structured light which adopts a new, versatile colored stripe pattern approach. We present the scanner architecture, the software technologies adopted, and the first results of its use in a project regarding the 3D acquisition of an archeological statue.     

3D coordinates measurement based on structured light sensor

This paper mainly discusses several challenges in exploiting a structured light sensor to realize 3D coordinates measurement: a practical and flexible sensor structure is proposed with accuracy analysis, a very robust and accurate line extraction method is adopted to localize the center of the light stripe under considerable disturbances, the invariance of double cross-ratio based method is used to obtain enough accurate calibration points and a novel planar target based calibration method is presented. An application system of the structured light sensor for tyre inspection is set up, and the real online measurement results show that the application system has good repeatability and accuracy.     

引入极线约束的SURF特征匹配算法

目的 特征点匹配算法是当今计算机图像处理领域的研究热点,但是大多数现存的方法不能同时获得数量多和质量优的匹配。鉴于此,基于SURF （speeded-up robust features）算法,通过引入极线约束来提高特征匹配效果。方法 首先使用SURF算法检测和描述图像特征点,然后使用RANSAC （random sampling consensus）方法计算匹配图像之间的基础矩阵,通过该基础矩阵计算所有特征点的极线。再引入极线约束过滤掉错误匹配,最终获得数量与质量显著提高的匹配集合。结果 实验结果表明,该方法获得的匹配具有高准确度,匹配数目与原约束条件相比可高达2～8倍。结论 本文方法实现过程简单,不仅匹配准确度高且能够大大提高正确的特征匹配数,适用于处理不同类型的图像数据。     

基于极线校正的最小二乘立体匹配

本文减少了最小二乘转换参数,通过三个几何转换参数和两个辐射转换参数建立对应关系,采用经极线校正的立体像对,使对应点的搜索在相同扫描行上进行,减小了搜索空间,提高了匹配速度,且把匹配方法嵌入到多尺度空间中以提高匹配速度,通过视差后处理进一步提高匹配精度。采用自适应窗口技术解决由于存在矩阵不可逆情况而导致大量不可匹配点和在地形平坦、灰度变化不明显的区域不匹配或误匹配率高的缺点。试验结果表明了,本算法精度高,匹配率高的优点,有相当的使用价值。     

一种基于单相机三维重建的简单方法

提出了一种基于单相机三维重建的简单方法.在待重建场景中放置一块平面标定模板,用内参数已知的摄像机拍摄不同角度的二幅图像,利用场景中的标定模板精确地求解出拍摄时摄像机的外参数,并以此为基础进行立体标定、配准和匹配,得到视差图进而实现欧式几何意义下的三维重建.实验结果表明,提出的算法实现简单、配置灵活,具有较强的实用性.     

Towards a real-time 3D shape reconstruction using a structured light system

This paper deals with 3D shape reconstruction using a structured light system (SLS) which projects a matrix of laser rays onto the scene to be analyzed. The intrinsic problem of such a system is the correspondence problem solving, which in this particular case amounts to matching up the imaged spots and the originating laser rays. In this paper, we propose a method for automatically obtaining configurations of the system (COS) (i.e. the relative positions of the camera, laser projector, and measuring scene) that permit to achieve a direct and unambiguous correspondence. After, we propose a splitting cell algorithm, which efficiently performs a real-time correspondence procedure. Experimental results obtained from both simulated and real data demonstrate that our method provides our SLS with possibilities for real-time applications.     

Accurate reconstruction of 3D model of a human face using structured light

An automated system for the reconstruction of textured 3D models of human faces has been developed. 3D information is read using the structured lighting used in calibrated projector-camera system. The accuracy of 3D reconstruction is studied experimentally. De Wansa Wickramarante Viktor Klementovich. Born 1983. Graduated from the Moscow State Institute of Radioengineering, Electronics, and Automation (Technical University). Post graduate student of MSIREA. Scientific interests: pattern recognition, biometry, 3D scanners. Author of 5 papers. Awarded the 3 degree diploma at the All-Russian Conference MMRO-13; the incentive diploma at the International Conference ROAI-8-2007. Vladimir Vasilievich Ryazanov. Vladimir V. Ryazanov gratuated from the Moscow Institute of Physical Technology in 1973 and the post-graduate courses at the Computer Center, Academy of Sciences, USSR, in 1976. Received Ph.D. in 1977 (“Computer Science”) and Professor degree in 1994 (“Applications of Mathematical Methods, Mathematical Modeling and Computers in Scientific Investigations”). At the Dorodnicin Computer Center of the Russian Academy of Sciences since 1976. Head of the Situations Recognition Sector in the Department of Recognition Problems and Combinatorial Analysis. Author of approximately 150 papers. Scientific interests: data mining, mathematical models of pattern recognition, classification and forecasting, optimization of recognition and classification models, learning, synthesis of optimal collective solutions in the classification problem, creation of intelligent program systems for data analysis and recognition, and practical applications in technology, medicine, industry. Alexander Vinogradov. Born in 1951. Graduate from Moscow Institute of Physics and Technology in 1974. Kandidat degree in mathematical cybernetics, 1978. Spere of main interests—geometric and algebraic methods in data analysis and image processing. Author of about 50 scientific publications.     

基于序列图像的鲁棒三维重建方法

提出了基于序列图像的鲁棒三维重建方法。首先利用两幅图像的最优参数估计,然后添加新图像并采用稀疏调整,减少图像坐标测量值的最小几何误差。通过对三维结构和摄像机参数进行全局优化处理,以提高重建的鲁棒性。实验结果表明,该方法提高了重建的精度和鲁棒性,并真实地再现了物体的三维模型。