基于环视图象的数字相机内参数自定标方法

描述了一种适用于IBR系统的数字相机内参数自定标方法。该方法基于跟踪机机旋转得到的图象系列的特征匹配点以,而不需要标定物。认定在相机旋转过程中,其光学中心是稳定不变的,也即图象中心是固定的,可以事先定标;但容许相机的焦距在各幅图象间有变化,利用真实图象序列进行了实验验证,表明该方法能鲁棒地估算相机内参数。     

Epipolar Geometry for Central Catadioptric Cameras

Central catadioptric cameras are cameras which combine lenses and mirrors to capture a very wide field of view with a central projection. In this paper we extend the classical epipolar geometry of perspective cameras to all central catadioptric cameras. Epipolar geometry is formulated as the geometry of corresponding rays in a three-dimensional space. Using the model of image formation of central catadioptric cameras, the constraint on corresponding image points is then derived. It is shown that the corresponding points lie on epipolar conics. In addition, the shape of the conics for all types of central catadioptric cameras is classified. Finally, the theory is verified by experiments with real central catadioptric cameras.     

Photogeometric Direct Visual Tracking for Central Omnidirectional Cameras

The fundamental task of visual tracking is considered in this work as a direct image registration problem. Direct methods refer to those that exploit the pixel intensities without intermediate steps, e.g., no extraction of image features. This article proposes new photogeometric transformation models and nonlinear optimization methods for directly registering central omnidirectional images. The proposed models ensure robustness to arbitrary illumination changes, as well as encompass all classes of projective deformations of planar objects within those images. Experimental results show that visual tracking can indeed be highly robust and accurate even for this type of vision systems.     

一种反射折射摄像机的简易标定方法

Central catadioptric cameras are widely used in virtual reality and robot navigation,and the camera calibration is a prerequisite for these applications.In this paper,we propose an easy calibration method for central catadioptric cameras with a 2D calibration pattern.Firstly,the bounding ellipse of the catadioptric image and field of view (FOV) are used to obtain the initial estimation of the intrinsic parameters.Then,the explicit relationship between the central catadioptric and the pinhole model is used to initialize the extrinsic parameters.Finally,the intrinsic and extrinsic parameters are refined by nonlinear optimization.The proposed method does not need any fitting of partial visible conic,and the projected images of 2D calibration pattern can easily cover the whole image,so our method is easy and robust.Experiments with simulated data as well as real images show the satisfactory performance of our proposed calibration method.     

A Generic and Provably Convergent Shape-from-Shading Method for Orthographic and Pinhole Cameras

We describe a mathematical and algorithmic study of the Lambertian “Shape-From-Shading” problem for orthographic and pinhole cameras. Our approach is based upon the notion of viscosity solutions of Hamilton-Jacobi equations. This approach provides a mathematical framework in which we can show that the problem is well-posed (we prove the existence of a solution and we characterize all the solutions). Our contribution is threefold. First, we model the camera both as orthographic and as perspective (pinhole), whereas most authors assume an orthographic projection (see Horn and Brooks (1989) for a survey of the SFS problem up to 1989 and Zhang et al. (1999), Kozera (1998), Durou et al. (2004) for more recent ones); thus we extend the applicability of shape from shading methods to more realistic acquisition models. In particular it extends the work of Prados et al. (2002a) and Rouy and Tourin (1992). We provide some novel mathematical formulations of this problem yielding new partial differential equations. Results about the existence and uniqueness of their solutions are also obtained. Second, by introducing a “generic” Hamiltonian, we define a general framework allowing to deal with both models (orthographic and perspective), thereby simplifying the formalization of the problem. Thanks to this unification, each algorithm we propose can compute numerical solutions corresponding to all the modeling. Third, our work allows us to come up with two new generic algorithms for computing numerical approximations of the “continuous solution of the “Shape-From-Shading” problem as well as a proof of their convergence toward that solution. Moreover, our two generic algorithms are able to deal with discontinuous images as well as images containing black shadows. First online version published in October, 2005     

基于双摄像机的车辆超高检测

随着现代城市交通的迅速发展,桥梁和隧道日益增多,由超高车辆引发的交通安全事故频繁发生,车辆超高检测技术已经成为城市交通管理研究中的一个热点;这里提出了一种通过双摄像机(分别侧面和正面放置)联合检测并定位超高车辆的新技术,其中提出了一种车辆像素高度归一化方法并且将hough变换、角点检测应用于车辆超高检测;在北京某城市道路上的实验结果显示,该方法不但可以精确地进行车辆超高检测,而且可以有效定位超高车辆所在车道,提高了车辆超高预警系统的可靠性.     

Calibration of Central Catadioptric Cameras Using a DLT-Like Approach

In this study, we present a calibration technique that is valid for all single-viewpoint catadioptric cameras. We are able to represent the projection of 3D points on a catadioptric image linearly with a 6×10 projection matrix, which uses lifted coordinates for image and 3D points. This projection matrix can be computed from 3D–2D correspondences (minimum 20 points distributed in three different planes). We show how to decompose it to obtain intrinsic and extrinsic parameters. Moreover, we use this parameter estimation followed by a non-linear optimization to calibrate various types of cameras. Our results are based on the sphere camera model which considers that every central catadioptric system can be modeled using two projections, one from 3D points to a unitary sphere and then a perspective projection from the sphere to the image plane. We test our method both with simulations and real images, and we analyze the results performing a 3D reconstruction from two omnidirectional images.     

基于双摄像机的视频特征跟踪算法研究

在视频动画中,特征跟踪结果决定了三维重建的运动信息是否逼真。提出一种基于双摄像机的视频特征跟踪算法,它采用卡尔曼滤波预测特征点的位置,然后利用子块模型进行特征匹配,最后利用极线方程指导跟踪和剔除跟踪误差大的特征点。该算法已经用于我们开发的VBHA系统中,实验结果表明,其速度和效率能够满足视频动画的要求,并且能够部分解决自遮挡的问题。     

Self-calibration of an affine camera from multiple views

A key limitation of all existing algorithms for shape and motion from image sequences under orthographic, weak perspective and para-perspective projection is that they require the calibration parameters of the camera. We present in this paper a new approach that allows the shape and motion to be computed from image sequences without having to know the calibration parameters. This approach is derived with the affine camera model, introduced by Mundy and Zisserman (1992), which is a more general class of projections including orthographic, weak perspective and para-perspective projection models. The concept of self-calibration, introduced by Maybank and Faugeras (1992) for the perspective camera and by Hartley (1994) for the rotating camera, is then applied for the affine camera.This paper introduces the 3 intrinsic parameters that the affine camera can have at most. The intrinsic parameters of the affine camera are closely related to the usual intrinsic parameters of the pin-hole perspective camera, but are different in the general case. Based on the invariance of the intrinsic parameters, methods of self-calibration of the affine camera are proposed. It is shown that with at least four views, an affine camera may be self-calibrated up to a scaling factor, leading to Euclidean (similarity) shape réconstruction up to a global scaling factor. Another consequence of the introduction of intrinsic and extrinsic parameters of the affine camera is that all existing algorithms using calibrated affine cameras can be assembled into the same framework and some of them can be easily extented to a batch solution.Experimental results are presented and compared with other methods using calibrated affine cameras.     

C语言中两个常见问题的通用算法

通过解析第4届全国ITAT教育工程就业技能大赛C程序设计的复赛试题,给出其中一个编程题目的具体实现,并从中总结出C语言中两个常见问题的通用算法。     

Self-calibration from one circular motion sequence and two images

This paper describes a new method for self-calibration of camera with constant internal parameters under circular motion, using one sequence and two images captured with different camera orientations. Unlike the previous method, in which three circular motion sequences are needed with known motion, the new method computes the rotation angles and the projective reconstructions of the sequence and the images with circular constraint enforced, which is called a circular projective reconstruction, using a factorization-based method. It is then shown that the images of the circular points of each circular projective reconstruction can be readily obtained. Subsequently, the image of the absolute conic and the calibration matrix of the camera can be determined. Experiments on both synthetic and real image sequence are given, showing the accuracy and robustness of the new algorithm.     

Self-calibration of a space robot

We present a neural-network method to recalibrate automatically a commercial robot after undergoing wear or damage, which works on top of the nominal inverse kinematics embedded in its controller. Our starting point has been the work of Ritter et al. (1989, 1992) on the use of extended self-organizing maps to learn the whole inverse kinematics mapping from scratch. Besides adapting their approach to learning only the deviations from the nominal kinematics, we have introduced several modifications to improve the cooperation between neurons. These modifications not only speed up learning by two orders of magnitude, but also produce some desirable side effects, like parameter stability. After extensive experimentation through simulation, the recalibration system has been installed in the REIS robot included in the space-station mock-up at Daimler-Benz Aerospace. Tests performed in this set-up have been constrained by the need to preserve robot integrity, but the results have been concordant with those predicted through simulation.     

Generic model abstraction from examples

The recognition community has typically avoided bridging the representational gap between traditional, low-level image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models using simple scenes containing idealized, textureless objects or by bringing the models closer to the images using 3D CAD model templates or 2D appearance model templates. In this paper, we attempt to bridge the representational gap for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2D view-based class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graph-theoretical formulation of the problem in which we search for the lowest common abstraction among a set of lattices, each representing the space of all possible region groupings in a region adjacency graph representation of an input image. The problem is intractable and we present a shortest path-based approximation algorithm to yield an efficient solution. We demonstrate the approach on real imagery.     

Self-calibration of stationary non-rotating zooming cameras

This paper proposes a new method for self-calibrating a set of stationary non-rotating zooming cameras. This is a realistic configuration, usually encountered in surveillance systems, in which each zooming camera is physically attached to a static structure (wall, ceiling, robot, or tripod). In particular, a linear, yet effective method to recover the affine structure of the observed scene from two or more such stationary zooming cameras is presented. The proposed method solely relies on point correspondences across images and no knowledge about the scene is required. Our method exploits the mostly translational displacement of the so-called principal plane of each zooming camera to estimate the location of the plane at infinity. The principal plane of a camera, at any given setting of its zoom, is encoded in its corresponding perspective projection matrix from which it can be easily extracted. As a displacement of the principal plane of a camera under the effect of zooming allows the identification of a pair of parallel planes, each zooming camera can be used to locate a line on the plane at infinity. Hence, two or more such zooming cameras in general positions allow the obtainment of an estimate of the plane at infinity making it possible, under the assumption of zero-skew and/or known aspect ratio, to linearly calculate the camera's parameters. Finally, the parameters of the camera and the coordinates of the plane at infinity are refined through a nonlinear least-squares optimization procedure. The results of our extensive experiments using both simulated and real data are also reported in this paper.     

Self-calibration of a vision-based sensor network

When a network of vision-based sensors is emplaced in an environment for applications such as surveillance or monitoring the spatial relationships between the sensing units must be inferred or computed for self-calibration purposes. In this paper we describe a technique to solve one aspect of this self-calibration problem: automatically determining the topology and connectivity information of a network of cameras based on a statistical analysis of observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data for systems containing up to 10 agents and verified with experiments conducted on a six camera sensor network.     

Recovering Multiple View Geometry from Mutual Projections of Multiple Cameras

In this paper, we analyze the computation of epipolar geometry in some special cases where multiple cameras are projected each other in their images. In such cases, epipoles can be obtained directly from images as the projection of cameras. As the result, the epipolar geometry can be computed from less image correspondences with higher stability. In this paper, we analyze the number of corresponding points required for computing bifocal, trifocal and quadrifocal tensors linearly in the case where cameras are projected mutually. We next show a practical linear method for computing multifocal tensors by using the mutual projection of cameras. The degenerate configurations of points and cameras is also studied, and it is shown that some degenerate configurations in general cases are no longer degenerate under the mutual projection of cameras.     

Self-Calibration of Stationary Cameras

A new practical method is given for the self-calibration of a camera. In this method, at least three images are taken from the same point in space with different orientations of the camera and calibration is computed from an analysis of point matches between the images. The method requires no knowledge of the orientations of the camera. Calibration is based on the image correspondences only. This method differs fundamentally from previous results by Maybank and Faugeras on self-calibration using the epipolar structure of image pairs. In the method of this paper, there is no epipolar structure since all images are taken from the same point in space, and so Maybank and Faugeras's method does not apply. Since the images are all taken from the same point in space, determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur.A non-iterative calibration algorithm is given that works with any number of images. An iterative refinement method that may be used with noisy data is also described. The algorithm is implemented and validated on several sets of synthetic and real image data.     

Self-calibration with varying focal length from two images obtained by a stereo head

The self-calibration approach based on the absolute conic or its dual, the absolute dual quadric, has the merit of allowing the intrinsic camera parameters to vary in image sequence. In this paper, we show that certain linear equations resulting from the infinity homography can be added to a system of originally undetermined linear equations to find the absolute dual quadric for a stereo head. The special stereo configurations considered here allow one camera to rotate around either one or two of the coordinate axes defined by another camera. Experiments with synthetic images show that satisfactory results can be obtained with only the proposed linear methods. For real images obtained with non-ideal stereo configurations, and possibly with measurement noises, results obtained from the proposed linear methods may serve as reasonable initial guesses for some non-linear optimization procedure.