Maintaining stereo calibration by tracking image points

Crowley, James L.; Bobet, Philippe; Schmid, Cordelia

doi:10.1109/cvpr.1993.341086

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…There exist various approaches for modeling zoom lenses and the influence of zooming on the intrinsic parameters, as well as for modeling other intricate phenomena related to camera calibration, see e.g., [11,16,53,70,96,100,102,128,150,313,469,531,533,532].…”

Section: Extensions For Modeling Zooming and Focusingmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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Section: Extensions For Modeling Zooming and Focusingmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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“…Several different algorithms for calibration can be found in the literature, e.g. [6], [7], [8], and we have experimented with some of them and we got the best result with the following algorithm(s).…”

Section: Camera Calibrationmentioning

confidence: 97%

3D rapid mapping

2008

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In this paper the performance of passive range measurement imaging using stereo technique in real time applications is described. Stereo vision uses multiple images to get depth resolution in a similar way as Synthetic Aperture Radar (SAR) uses multiple measurements to obtain better spatial resolution. This technique has been used in photogrammetry for a long time but it will be shown that it is now possible to do the calculations, with carefully designed image processing algorithms, in e.g. a PC in real time. In order to get high resolution and quantitative data in the stereo estimation a mathematical camera model is used. The parameters to the camera model are settled in a calibration rig or in the case of a moving camera the scene itself can be used for calibration of most of the parameters. After calibration an ordinary TV camera has an angular resolution like a theodolite, but to a much lower price. The paper will present results from high resolution 3D imagery from air to ground. The 3D-results from stereo calculation of image pairs are stitched together into a large database to form a 3D-model of the area covered.

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“…However, camera zooming invalidates most of the current solutions to computer vision problems (e.g., tracking or calibration), which assume constant intrinsic camera parameters, and therefore demands new approaches [1,2]. Zooming does not only change the focal length but also the principal point, due to optical and mechanical misalignments in the lens system of the camera [3,4]. The rest of intrinsic camera parameters (e.g., pixel size and aspect ratio) remain constant for long periods of time [5] and may be assumed known.…”

Section: Introductionmentioning

confidence: 99%

Contour-based 3D motion recovery while zooming

Martinez-Marroquin

Torras

2003

Robotics and Autonomous Systems

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This paper considers the problem of 3D motion recovery from a sequence of monocular images while zooming. Unlike the common trend based on point matches, the proposed method relies on the deformation of an active contour fitted to a reference object. We derive the relation between the contour deformation and the 3D motion components, assuming time-varying focal length and principal point. This relation allows us to present a method to extract the rotation matrix and the scaled translation along the optical axis.

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Maintaining stereo calibration by tracking image points

Abstract: An important problem in active 3-D vision

Cited by 12 publications

References 7 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

3D rapid mapping

Contour-based 3D motion recovery while zooming

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