Reconstruction of a Scene with Multiple Linearly Moving Objects

In this paper we describe an algorithm to recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously given a monocular image sequence. The number of the moving objects is automatically detected without prior motion segmentation. Assuming that the objects are moving linearly with constant speeds, we propose a unified geometrical representation of the static scene and the moving objects. This representation enables the embedding of the motion constraints into the scene structure, which leads to a factorization-based algorithm. We also discuss solutions to the degenerate cases which can be automatically detected by the algorithm. Extension of the algorithm to weak perspective projections is presented as well. Experimental results on synthetic and real images show that the algorithm is reliable under noise.     

Reconstruction of General Curves,Using Factorization and Bundle Adjustment

In this paper, we extend the notion of affine shape, introduced by Sparr, from finite point sets to curves. The extension makes it possible to reconstruct 3D-curves up to projective transformations, from a number of their 2D-projections. We also extend the bundle adjustment technique from point features to curves.The first step of the curve reconstruction algorithm is based on affine shape. It is independent of choice of coordinates, is robust, does not rely on any preselected parameters and works for an arbitrary number of images. In particular this means that, except for a small set of curves (e.g. a moving line), a solution is given to the aperture problem of finding point correspondences between curves. The second step takes advantage of any knowledge of measurement errors in the images. This is possible by extending the bundle adjustment technique to curves.Finally, experiments are performed on both synthetic and real data to show the performance and applicability of the algorithm.     

三维物体识别研究进展

出于工业和医疗等领域大量现实应用的需要,如今三维物体识别已成为一个很活跃的研究领域。一般来说,三维物体识别系统可以通过两个阶段的处理来完成三维物体的识别和定位,首先用传感器获取的场景输入数据来得到场景的表达;然后将它与数据库中存储的物体表达相匹配。为了推动该领域研究进一步发展,因而对近１０ａ年中该识别过程中必须解决的感传器类型、三维物体表达方法和匹配策略等３个方面问题的研究成果进行了综述,对主要方法进行分类和总结;并提出了一些三维视觉系统中还需要深入研究的问题,包括对所研究物体形状的限制、复杂背景的影响和表达以及识别中的“整体和局部”的矛盾等。     

一种新颖的海上运动目标实时检测方法

提出了一种基于可见光成像序列的海上运动目标的快速检测方法。该方法先在静态图像中分割出感兴趣区域,继而仅在感兴趣区域中应用变形的时间差分法来检测运动目标。在感兴趣区域分割阶段,引入视觉注意力机制,通过构造金字塔结构以及迭代的小尺寸倒三角低通模板运算成功地滤除了海面波浪噪声。在此基础上,检测并分析外边缘,在单帧图像中快速实现了感兴趣区域的分割。大量实验表明,该方法具有实时性好、鲁棒性强等特点。     

主轴方法和矩方法相融合的三维物体归一化的识别

针对主轴方法和矩方法各自的特点,提出将这两种方法相融合的物体归一化和识别的新思想.首先推导出主轴唯一性判别准则,有效地解决了物体取向的归一化问题,然后推导出了对物体平移、取向和比例变化归一化的3-D不变矩.最后提出由二阶、三阶3-D不变矩组成的判别向量和识别判据.对12个三维物体进行归一化和识别的实验结果验证了本文方法的有效性.     

A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences

Given an image sequence of a scene consisting of multiple rigidly moving objects, multi-body structure-and-motion (MSaM) is the task to segment the image feature tracks into the different rigid objects and compute the multiple-view geometry of each object. We present a framework for multibody structure-and-motion based on model selection. In a recover-and-select procedure, a redundant set of hypothetical scene motions is generated. Each subset of this pool of motion candidates is regarded as a possible explanation of the image feature tracks, and the most likely explanation is selected with model selection. The framework is generic and can be used with any parametric camera model, or with a combination of different models. It can deal with sets of correspondences, which change over time, and it is robust to realistic amounts of outliers. The framework is demonstrated for different camera and scene models. Most of the presented research was carried out while all three authors were at Monash University.     

双视三维重建的高精度运动参数估计方法

从两幅透视图像恢复被摄目标的三维结构是计算机视觉最基本的任务之一,其中,运动估计算法的性能决定了最终的三维重建精度。首先讨论了双视成像的基本数学模型,并介绍了几种现有运动参数估计方法的基本原理和不足。随后,基于投影误差最小判决函数,提出了用于双像运动估计的改进非线性迭代优化方法。数值仿真结果表明,在大平移小旋转角及小平移大旋转角2种运动条件下,采用文中提出的方法,运动估计精度均有所提高。此外,根据运动参数的估计值对真实目标进行三维重建实验,结果表明尺度重建误差小于2%且角度误差在3°以内。     

三维运动目标的多尺度智能递推识别新方法

在实际成像条件下,运动中的三维目标,其投影形状(Silhouette)是变化的,因而其可识别性也处于变动中．为了应对这类困难情况,本文定义了模式的动态特征空间和模式的动态可识别性等概念．讨论了建立多尺度三维目标特性视图特征模型的必要性,以及将目标运动特性一般约束用于目标序列图像识别的合理性．据此,提出了处理三维目标运动图像序列的多尺度智能递推识别方法(MUSIRR)．构造了一种混合神经网络和逻辑决策模块的智能识别器,BP神经网和RBF网用作识别器的基本构成单元．在训练阶段,该识别器使用目标的多尺度二值特性视图模型的规则矩不变量为样本特征向量．在识别阶段,算法在递推识别序列目标图像过程中,充分利用了目标姿态不会突变以及有关成像过程的合理约束,达到了提高识别率目的．与文献中的基于单尺度特性视图的三维目标识别方法相比,本文的方法训练过程简单,只需较少的目标特性视图模型样本,不仅能处理单帧图像,更能有效处理序列图像．对几类飞机目标的大规模模拟实验结果证实了本文方法的合理性和有效性．     

运动目标三维轨迹可视化与关联分析方法

随着治安监控系统的普及,越来越多的监控摄像头被安装在各个交通道路和公共场所中,每天都产生大量的监控视频.如今,监控视频分析工作主要是采用人工观看的方式来排查异常,以这种方式来分析视频内容耗费大量的人力和时间.目前,关于视频分析方面的研究大多是针对目标个体的异常行为检测和追踪,缺乏针对对象之间的关联关系的分析,对视频中的一些对象和场景之间的关联关系等还没有较为有效的表示和分析方法.针对这一现状,提出一种基于运动目标三维轨迹的关联视频可视分析方法来辅助人工分析视频,首先对视频资料进行预处理,获取各个目标对象的运动轨迹信息,由于二维轨迹难以处理轨迹的自相交、循环运动和停留等现象,并且没有时间信息就难以对同一空间内多个对象轨迹进行的关联性分析,于是结合时间维度对轨迹进行三维化扩展.该方法支持草图交互方式来操作,在分析过程中进行添加草图注释来辅助分析.可结合场景和对象的时空关系对轨迹进行关联性计算,得出对象及场景之间的关联模型,通过对对象在各个场景出现状况的统计,结合人工预先设定的规则,可实现对异常行为报警,辅助用户决策.     

物体基视频编码中的运动物体分割

针对物体基视频编码应用,提出了一种基于运动的图像序列分割方案,它同时结合了亮度和运动信息,使用重构式开－闭形态滤波器等形态变换工具。此外,还提出了一种区域生长型的分水岭算法,它直接对要分割的图像而不是其形态梯度信号进行分割,克服了传统形态分割方法使用形态梯度信号而导致轮廓信息丢失的缺点。在具体实现算法时,文中采用了一种基于分级队列结构进行图像扫描的巧妙方法,大大提高了算法的效率。模拟结果表明,该方     

一种基于立体视觉的多视点建模方法

论述了一种基于立体视觉的建模方法。该方法利用立体视觉系统在不同视点对景物观测所获得的局部三维几何模型，通过空间特征点匹配和坐标变换将局部模型融合，从而建立景物的完整描述。文章重点介绍了一种基于空间向量的坐标变换求解方法。     

A Geometric Approach for the Theory and Applications of 3D Projective Invariants

A central task of computer vision is to automatically recognize objects in real-world scenes. The parameters defining image and object spaces can vary due to lighting conditions, camera calibration and viewing position. It is therefore desirable to look for geometric properties of the object which remain invariant under such changes in the observation parameters. The study of such geometric invariance is a field of active research. This paper presents the theory and computation of projective invariants formed from points and lines using the geometric algebra framework. This work shows that geometric algebra is a very elegant language for expressing projective invariants using n views. The paper compares projective invariants involving two and three cameras using simulated and real images. Illustrations of the application of such projective invariants in visual guided grasping, camera self-localization and reconstruction of shape and motion complement the experimental part.     

基于特征点跟踪的运动目标接触时间估计方法研究

解决估计运动目标和静止观测者之间的接触时间(time-to-contact)的问题.首先定义了广义接触时间的概念,并提出了基于特征点跟踪的估计匀速运动目标接触时间的理论依据和利用特征线段估计接触时间的解决思路.随后,提出了一个结合Kalman滤波器的估计匀速运动目标和静止观测者之间接触时间的特征点跟踪方案,并讨论了特征点的选择准则、运动分割的方法、以及所采用的特征点跟踪的方法.最后,针对标定TTC的运动目标序列图像进行接触时间的估计实验,实验的结果是令人满意的.     

照明方向及反射特性参数的鲁棒估计

由图象明暗度提取物体表面三维形状需要预知照明方向及表面反射特性参数，但这些参数在实际应用中往往难于得到，文中提出了一种新的方法，该方法只需图象存在奇点，就可直接由灰度图象估计照明方向和反射特性参数，实验证明，该方法具有计算量少，误差小，鲁棒性好等优点。     

一种受雨滴影响的运动目标检测方法

提出一种在户外受雨滴影响的视频场景中检测运动目标的方法.在R,G,B空间构建雨滴在视频中的成像模型,该模型可以计算受雨滴影响像素的亮度变化值.能够有效克服现有模型只能针对某些特定类型雨滴进行辨识的局限性.在使用基于颜色信息的雨滴成像模型基础上,提出运动目标检测函数,此函数可以有效抑制雨滴产生的干扰.实验结果表明,提出的雨滴成像模型和相应的检测函数与现有模型比较,能够适用于多种不同受雨滴影响的图像序列采样环境,对于运动目标具有更好的分辨能力,并有更强的鲁棒性.     

Machine vision for industry: Tasks,tools and techniques

The ‘why’, ‘how’ and ‘what’ of industrial machine vision systems are surveyed-why vision is important, how it is accomplished and what sorts of tasks it is being applied to. Examples are given of vision techniques and applications from Japan, France, the GDR and the USA.     

EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling

Aligning shapes is essential in many computer vision problems and generalized Procrustes analysis (GPA) is one of the most popular algorithms to align shapes. However, if some of the shape data are missing, GPA cannot be applied. In this paper, we propose EM-GPA, which extends GPA to handle shapes with hidden (missing) variables by using the expectation-maximization (EM) algorithm. For example, 2D shapes can be considered as 3D shapes with missing depth information due to the projection of 3D shapes into the image plane. For a set of 2D shapes, EM-GPA finds scales, rotations and 3D shapes along with their mean and covariance matrix for 3D shape modeling. A distinctive characteristic of EM-GPA is that it does not enforce any rank constraint often appeared in other work and instead uses GPA constraints to resolve the ambiguity in finding scales, rotations, and 3D shapes. The experimental results show that EM-GPA can recover depth information accurately even when the noise level is high and there are a large number of missing variables. By using the images from the FRGC database, we show that EM-GPA can successfully align 2D shapes by taking the missing information into consideration. We also demonstrate that the 3D mean shape and its covariance matrix are accurately estimated. As an application of EM-GPA, we construct a 2D + 3D AAM (active appearance model) using the 3D shapes obtained by EM-GPA, and it gives a similar success rate in model fitting compared to the method using real 3D shapes. EM-GPA is not limited to the case of missing depth information, but it can be easily extended to more general cases.     

三维物体的形态图表达方法

三维物体的表达方法是计算机视觉中的关键问题之一，现有的各种三维物体表达方法虽然在各自的识别中得到应用，但都存在各自的局限性，用形态图表达三维物体是一种以视点为中心的表达方法，由于它列举了一个物体所有可能的“定性”形象，即它可使用最少的二维投影线图（特征视图）来表达一个完整的三维物体，因此使三维物体识别转化为2D与2D的匹配，该文首先定义了二维线图拓扑结构等价的判别准则，然后给出了构造透明物体形态图的方法，最后根据拓扑结构等价准则来得到不透明物体的形态图和特征图，并用圆锥与圆柱相交的实例进行了验证。     

Application of Self-Organisation Neural Network for Direct Shape from Shading

In this paper, a supervised self-organisation Neural Network (NN) for direct shape from shading is developed. The structure of the NN for the inclined light source model is derived based on the maximum uphill direct shape from shading approach. The major advantage of the NN model presented is the parallel learning or weight evolution for the direct shading. Here the proved convergent learning rule, the rate of convergence and a zero initialisation condition are shown. To increase the rate of convergence, the momentum factor is introduced. Further-more, the application of the network on IC (Integrated Circuit) component shape reconstruction is presented.     

基于SFM算法的三维人脸模型重建

提出了一种根据两幅正面人脸图像和一幅侧面图像重建人脸三维模型的算法，该算法主要包括4个步骤：寻找匹配点；采用SFM算法计算出特征点的三维坐标，并组成稀疏的三维网格结构；采用分步紧支撑径向基函数进行三维插值，得到三维模型；最后根据多分辨图像拼接算法生成纹理图像并将其映射到三维模型上，从而增强真实感，与其它算法相比，该算法最大的不同之处在于匹配点的寻找，匹配点的准确与否直接影响SFM算法结果的正确性，许多寻找匹配点的算法如角点匹配算法，在处理人脸图像时得到的结果并不稳定，这是因为人脸图像上包含了许多低纹理和重复纹理区域，大多数算法将代表人脸结构基本特征的基准模型运用在重建过程的最后一步，通过三维逼近运算，得到最终的重建模型，而该算法将反映人脸共性特征的几何对称性和规律性运用到匹配点的寻找中，能够快速准确地找出SFM算法需要的匹配点，用户使用普通照相机拍摄到的图像经本算法的处理后就可以得到相应的三维人脸结构。