宽基线视差图像的拼接算法

针对两幅视差图像的拼接问题,提出了一种新算法,即利用Hessian仿射不变检测算子检测出特征区域,利用SIFT特征描述算子提取特征区域特征矢量,根据特征矢量的欧几里德距离来建立图像间的稀疏对应关系;由这些对应点稀疏地确定场景中的一些点,以这些点为顶点建立场景的三角面片近似,再据此将重叠区域重投影生成推扫式成像的中间部分图像。将中间部分推扫式成像图像和原左图像的左半部分以及原右图像的右半部分一起拼接生成大图像。利用实际图像进行的拼接实验表明该算法是一个有效的视差图像拼接算法。     

纹理映射中的平面校正技术研究

为了快速实时地进行由平面组成的结构景物的3D建模问题,文中介绍了一种在进行图像3D重构时纹理映射中的平面校正方法。介绍了三角形模型在图像处理、图形绘制、虚拟现实等技术中的重要作用。从射影几何的角度出发,给出了从两幅视图进行景物三维重构的分层重构方法。在已知欧氏重构即摄像机内参数的基础上,介绍一种基于标定的平面射影失真矫正方法。通过此方法,将矫正过的纹理映射到欧式点重构结构中,得到景物的3D模型。经实验验证,这种方法在处理由平面组成的景物的3D重构中是实时有效的。     

Generalized Mosaicing: High Dynamic Range in a Wide Field of View

We present an approach that significantly enhances the capabilities of traditional image mosaicking. The key observation is that as a camera moves, it senses each scene point multiple times. We rigidly attach to the camera an optical filter with spatially varying properties, so that multiple measurements are obtained for each scene point under different optical settings. Fusing the data captured in the multiple images yields an image mosaic that includes additional information about the scene. We refer to this approach as generalized mosaicing. In this paper we show that this approach can significantly extend the optical dynamic range of any given imaging system by exploiting vignetting effects. We derive the optimal vignetting configuration and implement it using an external filter with spatially varying transmittance. We also derive efficient scene sampling conditions as well as ways to self calibrate the vignetting effects. Maximum likelihood is used for image registration and fusion. In an experiment we mounted such a filter on a standard 8-bit video camera, to obtain an image panorama with dynamic range comparable to imaging with a 16-bit camera.     

Local surface shape estimation of 3-D textured surfaces usingGaussian Markov random fields and stereo windows

The problem of extracting the local shape information of a 3-D texture surface from a single 2-D image by tracking the perceived systematic deformations the texture undergoes by virtue of being present on a 3-D surface and by virtue of being imaged is examined. The surfaces of interest are planar and developable surfaces. The textured objects are viewed as originating by laying a rubber planar sheet with a homogeneous parent texture on it onto the objects. The homogeneous planar parent texture is modeled by a stationary Gaussian Markov random field (GMRF). A probability distribution function for the texture data obtained by projecting the planar parent texture under a linear camera model is derived, which is an explicit function of the parent GMRF parameters, the surface shape parameters. and the camera geometry. The surface shape parameter estimation is posed as a maximum likelihood estimation problem. A stereo-windows concept is introduced to obtain a unique and consistent parent texture from the image data that, under appropriate transformations, yields the observed texture in the image. The theory is substantiated by experiments on synthesized as well as real images of textured surfaces     

Automatic Registration of Multi‐Projector Domes Using a Single Uncalibrated Camera

In this paper we present a novel technique for easily calibrating multiple casually aligned projectors on spherical domes using a single uncalibrated camera. Using the prior knowledge of the display surface being a dome, we can estimate the camera intrinsic and extrinsic parameters and the projector to display surface correspondences automatically using a set of images. These images include the image of the dome itself and a projected pattern from each projector. Using these correspondences we can register images from the multiple projectors on the dome. Further, we can register displays which are not entirely visible in a single camera view using multiple pan and tilted views of an uncalibrated camera making our method suitable for displays of different size and resolution. We can register images from any arbitrary viewpoint making it appropriate for a single head‐tracked user in a 3D visualization system. Also, we can use several cartographic mapping techniques to register images in a manner that is appropriate for multi‐user visualization. Domes are known to produce a tremendous sense of immersion and presence in visualization systems. Yet, till date, there exists no easy way to register multiple projectors on a dome to create a high‐resolution realistic visualizations. To the best of our knowledge, this is the first method that can achieve accurate geometric registration of multiple projectors on a dome simply and automatically using a single uncalibrated camera.     

Rectified Surface Mosaics

We approach mosaicing as a camera tracking problem within a known parameterized surface. From a video of a camera moving within a surface, we compute a mosaic representing the texture of that surface, flattened onto a planar image. Our approach works by defining a warp between images as a function of surface geometry and camera pose. Globally optimizing this warp to maximize alignment across all frames determines the camera trajectory, and the corresponding flattened mosaic image. In contrast to previous mosaicing methods which assume planar or distant scenes, or controlled camera motion, our approach enables mosaicing in cases where the camera moves unpredictably through proximal surfaces, such as in medical endoscopy applications.     

一种简单快速的相机标定新方法

本文提出了一种新的相机自标定方法,该方法要求摄像机在3个(或3个以上)不同方位摄取一个包含其内接正三角形的圆的新型标定模板的图像.首先,从模板图像中推导得到圆环点的像点坐标;然后通过得到的圆环点像点坐标,可线性求解摄像机内参数.与传统方法不同的是,该方法避免了复杂的椭圆拟合和直线拟合,降低了计算复杂度,提高了标定速度和...     

基于二次曲线的纯旋转摄像机自标定

研究探讨了一种基于平面二次曲线的纯旋转摄像机自标定方法.在不同的方位拍摄三幅或三幅以上图像,每幅图像至少包含两个空间平面二次曲线、或两个二次曲面、或一个平面二次曲线与一个二次曲面的投影,利用图像之间的二次曲线对应关系,可以确定摄像机的内参数矩阵,同时可以获得摄像机不同方位之间的旋转矩阵.由于使用的定标基元为二次曲线,是较点和直线包含更多信息的基元,因而基元之间的匹配容易自动实现,并有助于提高标定算法的鲁棒性和在线实时性.模拟实验和真实图像实验表明文中所介绍的方法是可行的.     

Catadioptric Stereo Using Planar Mirrors

By using mirror reflections of a scene, stereo images can be captured with a single camera (catadioptric stereo). In addition to simplifying data acquisition single camera stereo provides both geometric and radiometric advantages over traditional two camera stereo. In this paper, we discuss the geometry and calibration of catadioptric stereo with two planar mirrors. In particular, we will show that the relative orientation of a catadioptric stereo rig is restricted to the class of planar motions thus reducing the number of external calibration parameters from 6 to 5. Next we derive the epipolar geometry for catadioptric stereo and show that it has 6 degrees of freedom rather than 7 for traditional stereo. Furthermore, we show how focal length can be recovered from a single catadioptric image solely from a set of stereo correspondences. To test the accuracy of the calibration we present a comparison to Tsai camera calibration and we measure the quality of Euclidean reconstruction. In addition, we will describe a real-time system which demonstrates the viability of stereo with mirrors as an alternative to traditional two camera stereo.     

Correspondence-free stereo vision: extension from planar scene case to polyhedral scene case

Correspondence establishment is a central problem of stereo vision. In a work Aloimonos and Herve (IEEE Trans Pattern Anal Mach Intell 12(5):504–510, 1990) presented an algorithm that could reconstruct a single planar surface without establishing point-to-point correspondences. The work uses images that are taken under a specific stereo configuration. In this paper, we generalize the algorithm to one for general stereo configuration of the cameras. We further provide an extension of the algorithm, so that not only distant or planar scene but also multi-surface polyhedral scene can be reconstructed. Experimental results on a number of real image sets are presented to illustrate the performance of the algorithm.     

Applications of a direct algorithm for the rectification of uncalibrated images

An algorithm for the rectification of uncalibrated images is presented and applied to a variety of cases. The algorithm generates the rectifying transformations directly from the geometrical relationship between the images, using any three correspondences in the images to define a reference plane. A small set of correspondences is used to calculate an initial rectification. Additional correspondences are introduced semi-automatically, by correlating regions of the rectified images. Since the rectified images of surfaces in the reference plane have no relative distortion, features can be matched very accurately by correlation, allowing small changes in disparity to be detected. In the 3-d reconstruction of an architectural scene, differences in depth are resolved to about 0.001 of the distance from camera to subject.     

An Efficient Direct Approach to Visual SLAM

The majority of visual simultaneous localization and mapping (SLAM) approaches consider feature correspondences as an input to the joint process of estimating the camera pose and the scene structure. In this paper, we propose a new approach for simultaneously obtaining the correspondences, the camera pose, the scene structure, and the illumination changes, all directly using image intensities as observations. Exploitation of all possible image information leads to more accurate estimates and avoids the inherent difficulties of reliably associating features. We also show here that, in this case, structural constraints can be enforced within the procedure as well (instead of a posteriori), namely the cheirality, the rigidity, and those related to the lighting variations. We formulate the visual SLAM problem as a nonlinear image alignment task. The proposed parameters to perform this task are optimally computed by an efficient second-order approximation method for fast processing and avoidance of irrelevant minima. Furthermore, a new solution to the visual SLAM initialization problem is described whereby no assumptions are made about either the scene or the camera motion. Experimental results are provided for a variety of scenes, including urban and outdoor ones, under general camera motion and different types of perturbations.      

A curved ray camera for handling occlusions through continuous multiperspective visualization

Most images used in visualization are computed with the planar pinhole camera. This classic camera model has important advantages such as simplicity, which enables efficient software and hardware implementations, and similarity to the human eye, which yields images familiar to the user. However, the planar pinhole camera has only a single viewpoint, which limits images to parts of the scene to which there is direct line of sight. In this paper we introduce the curved ray camera to address the single viewpoint limitation. Rays are C1-continuous curves that bend to circumvent occluders. Our camera is designed to provide a fast 3-D point projection operation, which enables interactive visualization. The camera supports both 3-D surface and volume datasets. The camera is a powerful tool that enables seamless integration of multiple perspectives for overcoming occlusions in visualization while minimizing distortions.     

Fingerprint image mosaicking by recursive ridge mapping.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.     

Multilinear Factorizations for Multi-Camera Rigid Structure from Motion Problems

Camera networks have gained increased importance in recent years. Existing approaches mostly use point correspondences between different camera views to calibrate such systems. However, it is often difficult or even impossible to establish such correspondences. But even without feature point correspondences between different camera views, if the cameras are temporally synchronized then the data from the cameras are strongly linked together by the motion correspondence: all the cameras observe the same motion. The present article therefore develops the necessary theory to use this motion correspondence for general rigid as well as planar rigid motions. Given multiple static affine cameras which observe a rigidly moving object and track feature points located on this object, what can be said about the resulting point trajectories? Are there any useful algebraic constraints hidden in the data? Is a 3D reconstruction of the scene possible even if there are no point correspondences between the different cameras? And if so, how many points are sufficient? Is there an algorithm which warrants finding the correct solution to this highly non-convex problem? This article addresses these questions and thereby introduces the concept of low-dimensional motion subspaces. The constraints provided by these motion subspaces enable an algorithm which ensures finding the correct solution to this non-convex reconstruction problem. The algorithm is based on multilinear analysis, matrix and tensor factorizations. Our new approach can handle extreme configurations, e.g. a camera in a camera network tracking only one single point. Results on synthetic as well as on real data sequences act as a proof of concept for the presented insights.     

Image mosaicing using sequential bundle adjustment

We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric bundle adjustment techniques to mosaicing; (ii) a new representation of image line measurements enabling the use of lines in camera self-calibration, including computation of the radial and other non-linear distortion; and (iii) the application of the variable state dimension filter to obtain efficient sequential updates of the mosaic as each image is added.We demonstrate that our method achieves better results than the alternative approach of optimising over pairs of images.     

Determining surface orientation by projecting a stripe pattern

A method is presented for determining the surface orientations of an object by projecting a stripe pattern on to it. Assuming orthographical projection as a camera model and parallel light projection of the stripe pattern, the method obtains a 2 1/2-D representation of objects by estimating surface normals from the slopes and intervals of the stripes in the image. The 2 1/2-D image is further divided into planar or singly curved surfaces by examining the distribution of the surface normals in gradient space. A simple application to finding a planar surface and determining its orientation and shape is shown. The error in surface orientation is discussed     

Synthesizing Novel Views from Unregistered 2-D Images

Synthesizing the image of a 3-D scene as it would be captured by a camera from an arbitrary viewpoint is a central problem in Computer Graphics. Given a complete 3-D model, it is possible to render the scene from any viewpoint. The construction of models is a tedious task. Here, we propose to bypass the model construction phase altogether, and to generate images of a 3-D scene from any novel viewpoint from prestored images. Unlike methods presented so far, we propose to completely avoid inferring and reasoning in 3-D by using projective invariants. These invariants are derived from corresponding points in the prestored images. The correspondences between features are established off-line in a semi-automated way. It is then possible to generate wireframe animation in real time on a standard computing platform. Well understood texture mapping methods can be applied to the wireframes to realistically render new images from the prestored ones. The method proposed here should allow the integration of computer generated and real imagery for applications such as walkthroughs in realistic virtual environments. We illustrate our approach on synthetic and real indoor and outdoor images.