Calibration Method for Misaligned Catadioptric Camera

Mashita, Tomohiro; Iwai, Yoshio; Yachida, Masahiko

doi:10.1093/ietisy/e89-d.7.1984

Cited by 25 publications

(23 citation statements)

References 19 publications

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“…Mashita et al [332] supposed that the perspective camera is calibrated and that the mirror's circular upper boundary can be extracted in the image. Then, classical algorithms for estimating the pose of a circle in 3D can be applied, e.g., [114], after which the pose of the entire mirror, relative to the camera, is known.…”

Section: Special Approaches Dedicated To Catadioptric Systemsmentioning

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: Special Approaches Dedicated To Catadioptric Systemsmentioning

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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show abstract

“…These methods are effective while compensating for minor mirror misalignments. (Mashita et al, 2006) also proposed a calibration method for a catadioptric camera system that they claim can estimate all degrees of freedom of mirror posture. This method assumes five degrees of freedom for the mirror, and uses the mirror boundary to determine the elliptic pattern on the image.…”

Section: Related Workmentioning

confidence: 99%

Calibration of Non-SVP Hyperbolic Catadioptric Robotic Vision Systems

Cunha¹,

Azevedo²,

Lau³

2010

Robot Vision

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“…Rather few methods have been proposed for calibrating noncentral cameras; the first attempt was made by Aliaga [1] who introduced a paracatadioptric camera calibration method that relaxes the assumption of the perfect orthographic projection and placement. Later, Mashita et al in [15] presented a calibration method for noncentral catadioptric cameras based on a SOR mirror. In particular, they devised a localization procedure for an off-axis catadioptric camera based on an hyperboloidal mirror [19].…”

Section: Introductionmentioning

confidence: 99%