Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3×3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and vehicle navigation. This paper gives an introduction to the epipolar geometry, and provides a complete review of the current techniques for estimating the fundamental matrix and its uncertainty. A well-founded measure is proposed to compare these techniques. Projective reconstruction is also reviewed. The software which we have developed for this review is available on the Internet.

We present a complete scalable system for 6 d.o.f. camera tracking based on natural features. Crucially, the calculation is based only on pre-captured reference images and previous estimates of the camera pose and is hence suitable for online applications. We match natural features in the current frame to two spatially separated reference images. We overcome the wide baseline matching problem by matching to the previous frame and transferring point positions to the reference images. We then minimize deviations from the two-view and three-view constraints between the reference images and the current frame as a function of the camera position parameters. We stabilize this calculation using a recursive form of temporal regularization that is similar in spirit to the Kalman filter. We can track camera pose over hundreds of frames and realistically integrate virtual objects with only slight jitter.

This paper deals with a fundamental problem in motion and stereo analysis, namely that of determining the camera intrinsic calibration parameters. A novel method is proposed that follows the autocalibration paradigm, according to which calibration is achieved not with the aid of a calibration pattern but by observing a number of image features in a set of successive images. The proposed method relies upon the Singular Value Decomposition of the fundamental matrix, which leads to a particularly simple form of the Kruppa equations. In contrast to the classical formulation that yields an over-determined system of constraints, the derivation proposed here provides a straightforward answer to the problem of determining which constraints to employ among the set of available ones. Moreover, the derivation is a purely algebraic one, without a need for resorting to the somewhat non-intuitive geometric concept of the absolute conic. Apart from the fundamental matrix itself, no other quantities...

Zoom lenses appear to fit very naturally into the framework of active vision --- controlling a zoom lens allows an adjustment of the image, enabling either an analysis of a wide scene or a close look at a region or object of particular interest. However, their integration into vision systems is not without difficulty since zoom interacts insidiously with both low and high level processes. This thesis concerns developing and analyzing algorithms that function in spite of zoom; algorithms for visual tracking, camera calibration and Euclidean reconstruction. An approach grounded in visual geometry is adopted, motivated by the notion that the geometric descriptions of point (corner) and line (straight edge) features are zoom-invariant.

Abstract. Reconstructing the scene from image sequences captured by moving cameras with varying intrinsic parameters is one of the major achievements of computer vision research in recent years. However, there remain gaps in the knowledge of what is reliably recoverable when the camera motion is constrained to move in particular ways. This paper considers the special case of multiple cameras whose optic centres are fixed in space, but which are allowed to rotate and zoom freely, an arrangement seen widely in practical applications. The analysis is restricted to two such cameras, although the methods are readily extended to more than two. As a starting point an initial self-calibration of each camera is obtained independently. The first contribution of this paper is to provide an analysis of nearambiguities which commonly arise in the self-calibration of rotating cameras. Secondly we demonstrate how their effects may be mitigated by exploiting the epipolar geometry. Results on simulated and real data are presented to demonstrate how a number of self-calibration methods perform, including a final bundleadjustment of all motion and structure parameters. 1

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We present a portable system to record synchronised, multi-video data for vision applications such as 3D reconstruction and video-based rendering of dynamic scenes. The aim of the project is to gain access to a greater number of scenes than what a static, wired indoor studio allows for. The portable acquisition system is constructed from a number of independent modules, each consisting of a FireWire camera and a laptop. Our software utilises wireless networking making the system behave like a tightly coupled unit without requiring the modules to be physically connected to each other. The scheme also includes an external calibration method suitable for general scenes. The system is scalable and allows for easy transportation and adhoc setup and configuration. We present recent results acquired in the field and their use for free-viewpoint video.

We present in this paper a method of tracking multiple objects (people) in 3D for application in video surveillance. The tracking method is designed to work on images with objects at low resolution and has two major contributions. First we propose a way to generate 3D point clouds that imposes multiple constraints (both geometric and appearancebased) to ensure minimal noise in the 3D data. Second, we incorporate a method to group the points into clouds (or clusters) that correspond to objects in the environment being imaged. We show that this method is more powerful than current 3D tracking techniques that try to fuse 2D tracking information into 3D tracks. A comparison to competing 3D tracking methods are shown, and performance and limitations are discussed.

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot.

........................................................................................... x CHAPTER 1 INTRODUCTION ...............................................................................1 1.1 WHAT IS IMAGE-BASED RENDERING AND MODELING...........................................1 1.2 ISSUES.................................................................................................................4 1.3 OBJECTIVE AND APPROACH .................................................................................6 1.4 OUTLINE AND NOTATIONS ...................................................................................8 CHAPTER 2 ELEMENTS OF PROJECTIVE GEOMETRY ...............................10 2.1 PROJECTIVE SPACE............................................................................................10 2.1.1 Definitions .................................................................................................10 2.1.2 Canonical affine space embedding and the plane-at-...