利用光度立体法生成真实物体的三维模型

在计算机三维动画制作中，要制作一个具体的真实物体的三维模型，是一项复杂而困难的事情。本文根据由阴影恢复形状的视觉理论，利用光度立体视觉技术，给出了由３幅光移图象计算物体表面方向和深度信息，并利用贝塞尔曲线拟合获得物体表面的结构模型的方法。     

五自由度立体视觉平台--目标物体的识别与定位

从立体视觉与机器人控制集成的角度出发,建立了一个主动立体视觉跟踪和定位系统,用于柔性装配线中装配零件的运动跟踪和装配工位的精确定位。讨论了使用立体视觉进行快速目标识别和定位的方法,该方法将颜色分割与基于形状特征相结合,提出采用基于局部图像的HSV阈值分割和形状识别相结合的图像处理方法,该方法经过HSV阈值和形状判据,准确地识别出物体,得到物体的边界和质心。能快速准确地识别出物体并进行定位,满足柔性生产线小规模定制产品装配的要求。最后给出的实验结果验证了该方法的有效性。     

五自由度立体视觉仿真平台设计与建模

从立体视觉与机器人控制集成的角度出发，建立了一个主动立体视觉跟踪和定位系统，用于柔性装配线中装配零件的运动跟踪和装配工位的精确定位。该系统具有五个自由度，其中三个是平移自由度，两个是转动自由度，该文针对平台的结构进行了运动学建模和分析，采用D—H方法建立了五自由度立体视觉平台的运动学模型，得到运动学的正解。在该运动学方程基础上开发了系统仿真平台，介绍了’系统仿真平台的设计原理和操作特点。该仿真平台验证了机构设计的正确性，验证了五自由度平台运动学分析的正确性。该仿真平台采用虚拟现实建模方法，建立了五自由度平台的三维立体模型和立体视觉跟踪运动物体的实验模型，具有良好的交互性和开放性。     

多方法相融合的复杂物体深度信息的恢复

在恢复图象深度信息的方法之中,利用立体视觉的偏差来精确地定位物体的深度,是行之有效的,但只能适用于可匹配的特征点,如何建立左右图象中对应点的匹配是该方法的主要障碍;Ｓｈａｐｅ Ｆｒｏｍ Ｓｈａｄｉｎｇ方法是利用单幅图象的灰度信息获取物体表面的形状信息（表面的方向）,而不能获得其深度信息,其约束条件是表面的光滑性。在此用神经网络方法将二者融合起来,形成优势互补,用来获取物体的深度信息,通过对合成图象     

一种鱼眼镜头成像立体视觉系统的标定方法

鱼眼镜头成像立体视觉系统在微小型机器人视觉导航和近距离大视场物体识别与定位中有着广泛的应用 .尽管鱼眼镜头摄像机具有很大的视场角 (接近 180°) ,但同时也引入严重的图像变形 ,常规的摄像机标定方法无法使用 .该文提出一种标定鱼眼镜头摄像机立体视觉系统的方法 .在鱼眼镜头变形模型的基础上 ,通过考虑鱼眼镜头成像的径向变形、偏心变形和薄棱镜变形 ,建立了鱼眼镜头成像的精确成像模型 ;然后 ,利用非线性迭代算法 ,精确求解摄像机外部参数、内部参数 .实验表明 ,使用该方法得到的立体视觉系统参数满足精确恢复大场景稠密深度图的要求 .     

基于双目立体视觉的视差测量

双目立体视觉是计算机视觉研究最重要的组成部分之一.该文旨在构建一个双目视觉系统用来测量相机与物体的距离.通过双目相机进行图像采集,使用Matlab Calibration Toolbox工具对相机标定,然后进行左右图像校正,最后对校正的图像进行立体匹配,得到被测物体的视差图及其物体的3D坐标.实验表明:这个方案具有可行...     

基于立体视觉的高光物体恢复深度图的方法

基于立体视觉的物体深度图提取,在工业检测等许多方面均有较高的应用价值,但对于工程领域里的高光物体带来的误差,研究不多。为了较好地恢复具有高光反射的物体表面的三维深度信息,论文提出一种与立体视觉结合的多光源法去除高光的技术,使得后续的立体匹配更加可靠,深度计算更加准确。实验首先采用多光源法获取高光反射物体的多组图像,再经过图像预处理、摄像机标定、高光去除、立体匹配等步骤,结果证明这种方法有效地恢复了具有相对大面积的高光区域表面的物体的三维深度信息。     

基于立体视觉的立体拍摄参数自动获取方法

现有的3D摄像机在立体拍摄过程中参数调整困难,难以保证左右2台摄像机动作的协调性和一致性。针对该问题,提出一种基于立体视觉的立体拍摄参数自动获取方法。由4台全方位视觉传感器构成的全景3D摄像机获取全景立体图像,通过立体图像处理技术自动获取立体拍摄所需要的汇聚点位置、拍摄距离、焦距以及光圈系数等参数。实验结果表明,该方法能有效保证焦距、拍摄方向、拍摄角度和3D深度等3D拍摄参数的一致性,解决3D拍摄过程中2台摄像机在拍摄动作以及立体景深参数调整等协调难的问题,在拍摄3D全景视频图像的同时进行3D特写视频图像的拍摄,实现在显示器上全景点控的自动3D特写视频图像拍摄。     

一种鲁棒的基于光度立体视觉的表面重建方法

提出一种基于先进的凸优化技术的光度立体视觉重建框架. 首先通过鲁棒的主成分分析(Robust principle component analysis, RPCA)祛除图像噪声, 得到低秩矩阵和物体表面向量场, 然后再通过表面重建算法从向量场来恢复物体形状. 相对于先前的一些使用最小二乘或者一些启发式鲁棒技术的方法, 该方法使用了所有可用的信息, 可以同时修复数据中的丢失和噪声数据, 显示出了较高的计算效率以及对于大的稀疏噪声的鲁棒性. 实验结果表明, 本文提出的框架大大提高了在噪声存在情况下物体表面的重建精度.     

由单目图像获得表面高度算法的分析和实现

由单目图像获得物体表面高度（由阴影恢复形状）是计算机视觉中的一个翻来覆去研究领域,迭代计算算法的准确度在合成图像的表面恢复上得到了验证,在遥感图像的表面高度提取中也得到了应用,文中对一种新的由单目图像忧愁物体表面高度和梯度的算法进行了详细分析,并提出了实现算法的具体步骤和计算方法,对合成图像进行迭代计算,区 准确的表面高度,对实际焊点图像也验证了该算法的可行性。     

动态变形表面的四维测量

提出一种在双目视觉中利用随机三角形纹理进行动态变形表面四维测量的方法。生成随机的三角形纹理,将纹理转印到纸、布等需要进行测量的对象表面上;用标定的两个同步相机拍摄表面的动态变形过程,获得两个同步的图像序列。使用提出的方法,检测每个图像上的三角形;使用提出的三角形描述符和三角形极线约束方法,匹配第一帧图像上的三角形,并根据匹配的结果,测量物体表面在第一帧中的三维信息;根据所测的信息生成每个三角形的局部三维拓扑结构;在两个同步的图像序列中追踪三角形,得到四维测量的结果,并且利用局部拓扑结构检测并修复出现的错误。模拟实验、实际数据实验,验证了该方法的有效性和可行性。     

Object-centered surface reconstruction: Combining multi-image stereo and shading

Our goal is to reconstruct both the shape and reflectance properties of surfaces from multiple images. We argue that an object-centered representation is most appropriate for this purpose because it naturally accommodates multiple sources of data, multiple images (including motion sequences of a rigid object), and self-occlusions. We then present a specific object-centered reconstruction method and its implementation. The method begins with an initial estimate of surface shape provided, for example, by triangulating the result of conventional stereo. The surface shape and reflectance properties are then iteratively adjusted to minimize an objective function that combines information from multiple input images. The objective function is a weighted sum of stereo, shading, and smoothness components, where the weight varies over the surface. For example, the stereo component is weighted more strongly where the surface projects onto highly textured areas in the images, and less strongly otherwise. Thus, each component has its greatest influence where its accuracy is likely to be greatest. Experimental results on both synthetic and real images are presented.     

3-D recovery of human motion by mobile stereo cameras

A method is described which recovers the 3-D shape of deformable objects, particularly human motions, from mobile stereo images. In the proposed technique, camera calibration is not required when taking images. Existing optical 3-D modeling systems must employ calibrated cameras that are set at fixed positions. This inevitably puts constraints on the range of the movement of an object. In the proposed method, multiple mobile cameras take images of a deformable object moving freely, and its 3-D model is reconstructed from the video image streams obtained. The advantages of the proposed method include the fact that the cameras employed are calibration-free, and that the image-taking cameras can move freely. The theory is described, and the performance is shown by an experiment on 3-D human motion modeling in an outdoor environment. The accuracy of the 3-D model obtained is evaluated and a discussion is given. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Object tracking using deformable templates

We propose a method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the frame-to-frame deviations of the object shape and the fidelity of the modeled shape to the input image. The deformable template model utilizes the prior shape information which is extracted from the previous frames along with a systematic shape deformation scheme to model the object shape in a new frame. The following image information is used in the tracking process: 1) edge and gradient information: the object boundary consists of pixels with large image gradient, 2) region consistency: the same object region possesses consistent color and texture throughout the sequence, and 3) interframe motion: the boundary of a moving object is characterized by large interframe motion. The tracking proceeds by optimizing an objective function which combines both the shape deformation and the fidelity of the modeled shape to the current image (in terms of gradient, texture, and interframe motion). The inherent structure in the deformable template, together with region, motion, and image gradient cues, makes the proposed algorithm relatively insensitive to the adverse effects of weak image features and moderate amounts of occlusion     

Reconstruction of non-rigid 3D shapes from stereo-motion

Several non-rigid structure from motion methods have been proposed so far in order to recover both the motion and the non-rigid structure of an object. However, these monocular algorithms fail to give reliable 3D shape estimates when the overall rigid motion of the sequence is small. Aiming to overcome this limitation, in this paper we propose a novel approach for the 3D Euclidean reconstruction of deformable objects observed by an uncalibrated stereo rig. Using a stereo setup drastically improves the 3D model estimation when the observed 3D shape is mostly deforming without undergoing strong rigid motion. Our approach is based on the following steps. Firstly, the stereo system is automatically calibrated and used to compute metric rigid structures from pairs of views. Afterwards, these 3D shapes are aligned to a reference view using a RANSAC method in order to compute the mean shape of the object and to select the subset of points which have remained rigid throughout the sequence. The selected rigid points are then used to compute frame-wise shape registration and to robustly extract the motion parameters from frame to frame. Finally, all this information is used as initial estimates of a non-linear optimization which allows us to refine the initial solution and also to recover the non-rigid 3D model. Exhaustive results on synthetic and real data prove the performance of our proposal estimating motion, non-rigid models and stereo camera parameters even when there is no rigid motion in the original sequence.     

立体视频对象分割及其三维重建算法研究*

为更加有效分析立体视频对象,本文提出了一种基于离散冗余小波变换的立体视频对象分割算法,首先采用离散冗余小波变换提取特征点结合DT网格技术的视差估计方法,获得了可靠的视差场,再利用视差信息对立体视频中静止对象进行分割。对于立体视频序列中的运动对象,采用离散冗余小波提取运动区域的方法进行分割。实验结果表明,本算法对有重叠的多视频对象具有较好的分割效果,可同时分割静止物体和运动物体,具有较好的精确性和鲁棒性。对于分割出的立体视频对象,结合深度信息对其进行三维重建,得到较好的三维效果。     

Deformable model for estimating clothed and naked human shapes from a single image

Estimation of human shape from images has numerous applications ranging from graphics to surveillance. A single image provides insufficient constraints (e.g. clothing), making human shape estimation more challenging. We propose a method to simultaneously estimate a person’s clothed and naked shapes from a single image of that person wearing clothing. The key component of our method is a deformable model of clothed human shape. We learn our deformable model, which spans variations in pose, body, and clothes, from a training dataset. These variations are derived by the non-rigid surface deformation, and encoded in various low-dimension parameters. Our deformable model can be used to produce clothed 3D meshes for different people in different poses, which neither appears in the training dataset. Afterward, given an input image, our deformable model is initialized with a few user-specified 2D joints and contours of the person. We optimize the parameters of the deformable model by pose fitting and body fitting in an iterative way. Then the clothed and naked 3D shapes of the person can be obtained simultaneously. We illustrate our method for texture mapping and animation. The experimental results on real images demonstrate the effectiveness of our method.     

基于量子力学目标轮廓提取方法

提出了一种新的目标轮廓提取方法-量子轮廓模型。在分析了可变形模型(DeformableModels)的物理背景的基础上,将量子力学中关于粒子运动的观念及规律引入到目标轮廓的提取中,提出了一种基于量子力学目标轮廓提取方法。从理论上论证了此方法的合理性,说明了此方法能够克服可变形模型的一些无法克服的缺点。给出了粒子从一点在下一时刻到达另一点的概率(相对概率)的一种估计方法。实验表明,不管是人工图像还是真实图像,该方法有较好的效果、较快的速度。     

Non-Rigid Stereo Factorization

In this paper we address the problem of recovering 3D non-rigid structure from a sequence of images taken with a stereo pair. We have extended existing non-rigid factorization algorithms to the stereo camera case and presented an algorithm to decompose the measurement matrix into the motion of the left and right cameras and the 3D shape, represented as a linear combination of basis-shapes. The added constraints in the stereo camera case are that both cameras are viewing the same structure and that the relative orientation between both cameras is fixed. Our focus in this paper is on the recovery of flexible 3D shape rather than on the correspondence problem. We propose a method to compute reliable 3D models of deformable structure from stereo images. Our experiments with real data show that improved reconstructions can be achieved using this method. The algorithm includes a non-linear optimization step that minimizes image reprojection error and imposes the correct structure to the motion matrix by choosing an appropriate parameterization. We show that 3D shape and motion estimates can be successfully disambiguated after bundle adjustment and demonstrate this on synthetic and real image sequences. While this optimization step is proposed for the stereo camera case, it can be readily applied to the case of non-rigid structure recovery using a monocular video sequence. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.