Recursive Camera Autocalibration with the Kalman Filter

Given a projective reconstruction of a 3D scene, we address the problem of recovering the Euclidean structure of the scene in a recursive way. This leads to the application of Kalman filtering to the problem of camera autocalibration and to new algorithms for the autocalibration of cameras with varying parameters. This has benefits in saving memory and computational effort, and obtaining faster updates of the 3D Euclidean structure of the scene under consideration

The Extraction and Use of Image Planes for Three-dimensional Metric Reconstruction

The three-dimensional (3D) metric reconstruction of a scene from two-dimensional images is a fundamental problem in Computer Vision. The major bottleneck in the process of retrieving such structure lies in the task of recovering the camera parameters. These parameters can be calculated either through a pattern-based calibration procedure, which requires an accurate knowledge of the scene, or using a more flexible approach, known as camera autocalibration, which exploits point correspondences across images. While pattern-based calibration requires the presence of a calibration object, autocalibration constraints are often cast into nonlinear optimization problems which are often sensitive to both image noise and initialization. In addition, autocalibration fails for some particular motions of the camera. To overcome these problems, we propose to combine scene and autocalibration constraints and address in this thesis (a) the problem of extracting geometric information of the scene from uncalibrated images, (b) the problem of obtaining a robust estimate of the affine calibration of the camera, and (c) the problem of upgrading and refining the affine calibration into a metric one. In particular, we propose a method for identifying the major planar structures in a scene from images and another method to recognize parallel pairs of planes whenever these are available. The identified parallel planes are then used to obtain a robust estimate of both the affine and metric 3D structure of the scene without resorting to the traditional error prone calculation of vanishing points. We also propose a refinement method which, unlike existing ones, is capable of simultaneously incorporating plane parallelism and perpendicularity constraints in the autocalibration process. Our experiments demonstrate that the proposed methods are robust to image noise and provide satisfactory results

Calibrage itératif de caméras à partir de scènes planes

National audienceWe present a method for camera autocalibration from images of an unknown planar object. The few existing methods for that problem proceed by non-linear batch optimization, whereas our method is of sequential nature, being based on Kalman filtering. Its overall computational cost is much smaller, thus enabling the use of video sequences as input for real-time, on-the-fly, autocalibration. Being able to use video efficiently is very beneficial, since a stable autocalibration requires images taken with significantly varying camera orientations : batch methods put limits on the number of images used, hence require either to select key frames from a video, or to use discrete image sequences, for which the matching is more difficult. Our method is not concerned by these issues. Although not optimal (in the bundle adjustment sense), our method compensates by using indeed all available information in an image sequence. Experimental results show that it performs similarly to a global batch method, in terms of accuracy and convergence, while allowing real-time autocalibration in practice.Nous présentons une méthode permettant le calibrage de caméras vidéo à partir de séquences d'images d'un objet plan texturé, mais inconnu. Les méthodes permettant de répondre à ce problème procèdent généralement par optimisation d'un système non-linéaire global (utilisant toutes les images simultanément) alors que notre méthode est par nature séquentielle, puisque basée sur un filtre de Kalman. Le coût de traitement est très faible, ce qui permet de calibrer des caméras vidéo en temps réel, en utilisant directement le flux des images produites par la caméra. Le fait de pouvoir utiliser le flux vidéo efficacement est profitable, puisqu'un auto-calibrage stable requière que les images soient prises avec des angles de vue différents. Les méthodes globales sont limitées par le nombre d'images qu'elles peuvent utiliser et il faut donc soit choisir des vues clés, soit utiliser des séquences de vues fortement espacées ce qui rend la mise en correspondance difficile. Notre méthode ne souffre pas de ces limites, et peut donc traiter des séquences de plusieurs milliers d'images. Bien que non-optimale (au sens de l'ajustement de faisceaux), notre méthode permet de traiter l'ensemble des informations d'une séquence. Des résultats expérimentaux montrent que, aussi bien en terme de précision que de convergence, notre méthode donne des résultats équivalents à un ajustement de faisceaux, et permet de plus l'auto-calibrage en temps réel

Estimating intrinsic camera parameters from the fundamental matrix using an evolutionary approach

Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then this calibration process is called autocalibration because it is done automatically, without user intervention. Using autocalibration, it is possible to create 3D reconstructions from a sequence of uncalibrated images without having to rely on a formal camera calibration process. The fundamental matrix describes the epipolar geometry between a pair of images, and it can be calculated directly from 2D image correspondences. We show that autocalibration from a set of fundamental matrices can simply be transformed into a global minimization problem utilizing a cost function. We use a stochastic optimization approach taken from the field of evolutionary computing to solve this problem. A number of experiments are performed on published and standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, that is, the images are taken from the same camera without varying such quantities as the focal length. We show that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences

A Self-Calibration Method of Zooming Camera

In this article we proposed a novel approach to self- calibrate a camera with variable focal length. We show that the estimation of camera’s intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera’s intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera’s intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence

A Factorization Based Self-Calibration for Radially Symmetric Cameras

The paper proposes a novel approach for planar selfcalibration of radially symmetric cameras. We model these camera images using notions of distortion center and concentric distortion circles around it. The rays corresponding to pixels lying on a single distortion circle form a right circular cone. Each of these cones is associated with two unknowns; optical center and focal length (opening angle). In the central case, we consider all distortion circles to have the same optical center, whereas in the non-central case they have different optical centers lying on the same optical axis. Based on this model we provide a factorization based self-calibration algorithm for planar scenes from dense image matches. Our formulation provides a rich set of constraints to validate the correctness of the distortion center. We also propose possible extensions of this algorithm i