Camera calibration: a quick and easy way to determine the scale factor

Penna, Michael A.

doi:10.1109/34.107007

Cited by 97 publications

(39 citation statements)

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“…Algorithms using images of spheres for the calibration of perspective cameras were proposed for example by Penna [398], Daucher et al [104], Teramoto and Xu [498], and Agrawal and Davis [6], to name a few.…”

Section: Spheresmentioning

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: Spheresmentioning

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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“…(2) Intrinsic versus extrinsic camera calibration. Intrinsic calibration is concerned only with obtaining the physical and optical parameters of the camera [16,17]. Besides, extrinsic calibration concerns the measurement of the position and orientation of the camera in the scene [18,19].…”

Section: Camera Localizationmentioning

confidence: 99%

A comparative review of camera calibrating methods with accuracy evaluation

Salví

Armangué

Batlle

2002

Pattern Recognition

406

186

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Camera calibrating is a crucial problem for further metric scene measurement. Many techniques and some studies concerning calibration have been presented in the last few years. However, it is still di cult to go into details of a determined calibrating technique and compare its accuracy with respect to other methods. Principally, this problem emerges from the lack of a standardized notation and the existence of various methods of accuracy evaluation to choose from. This article presents a detailed review of some of the most used calibrating techniques in which the principal idea has been to present them all with the same notation. Furthermore, the techniques surveyed have been tested and their accuracy evaluated. Comparative results are shown and discussed in the article. Moreover, code and results are available in internet. ?

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“…Fortunately, numerous methods can be used to calibrate them. These methods are usually sufficient to determine scale factors and biases ( [1], [2], [6], [8]). For this reason, in this paper, we assume that the sensors themselves are already calibrated and we consider the problem of sensors harmonization.…”

Section: Introductionmentioning

confidence: 99%

Harmonization of a multi-sensor navigation system

Dorveaux

Petit

2011

2011 International Conference on Indoor Positioning and Indoor Navigation

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Abstract-We address the problem of harmonization of two subsystems used separately to measure a velocity in a body reference frame and an attitude with respect to an inertial frame. This general problem is of particular importance when the two subsystems are jointly used to obtain an estimate of the inertial velocity, and, then, the position through an integration procedure. Typical possible applications encompass pedestrian navigation, and, more generally, indoor navigation. The paper demonstrates how harmonization can be achieved thanks to an analysis of propagation of projection errors. In particular, trajectories known to be closed can be used to determine one dominant harmonization factor which is the pitch relative orientation angle between the two subsystems. Computations yielding the harmonization procedure are exposed in this article, and experimental data serve to illustrate the application of the proposed method.

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Camera calibration: a quick and easy way to determine the scale factor

Cited by 97 publications

References 1 publication

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

A comparative review of camera calibrating methods with accuracy evaluation

Harmonization of a multi-sensor navigation system

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