A unifying geometric representation for central projection systems

Barreto, João Pedro

doi:10.1016/j.cviu.2006.06.003

Cited by 84 publications

(54 citation statements)

References 18 publications

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“…Section 3.1.7) and the division model. A similar extension was proposed by Barreto, who showed that when replacing the sphere with a paraboloid, one may represent the division model [26,27].…”

Section: Global Camera Models 55mentioning

confidence: 95%

“…The second method is of the bundle adjustment type; it estimates the distortion model's coefficients using as cost function the Sampson distance between image points and line images (conics). Barreto as well as Strand and Hayman proposed similar plumb-line methods for the 1-parameter division model, for which line images are circles [26,466].…”

Section: Lines -The Plumb-line Approachmentioning

confidence: 99%

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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: Global Camera Models 55mentioning

confidence: 95%

Section: Lines -The Plumb-line Approachmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

View full text Add to dashboard Cite

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“…The unitary sphere represents an other space which is particularly useful for omnidirectional image processing. As demonstrated in [5] and [6], there exists an equivalence between the unitary sphere and single view point (SVP), also called central images and even some fisheye images [7]. However, this equivalence is related to the knowledge of the intrinsic parameters of the sensor ( fig 3) and can be decomposed into a double projection as follows: 3D point P w is projected on the sphere surface according to the sphere center in P s which is then projected on the image plane into P i from the projection center O p located between the center and the north pole of the sphere.…”

Section: Introductionmentioning

confidence: 91%

Central catadioptric image processing with geodesic metric

Demonceaux

Vasseur

Fougerolle

2011

Image and Vision Computing

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Because of the distortions produced by the insertion of a mirror, catadioptric images can not be processed similarly to classical perspective images. Now, although the equivalence between such images and spherical images is well known, the use of spherical harmonic analysis often leads to image processing methods which are more difficult to implement. In this paper, we propose to define catadioptric image processing from the geodesic metric on the unitary sphere. We show that this definition allows to adapt very simply classical image processing methods. We focus more particularly on image gradient estimation, interest point detection, and matching. More generally, the proposed approach extends traditional image processing techniques based on Euclidean metric to central catadioptric images. We show in this paper the efficiency of the approach through different experimental results and quantitative evaluations.

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“…The polynomial approximation of the projection function was introduced by Scaramuzza (Scaramuzza et al, 2006), who proposed a calibration toolbox for his model. The generic model, also known as the unified model, was introduced by Geyer (Geyer & Daniilidis, 2000) and Barreto (Barreto, 2006), who proved its validity for all central catadioptric systems. This model was then modified by Mei (Mei & Rives, 2007), who generalized the projection matrix and also took into account the distortions.…”

Section: Projection Modelmentioning

confidence: 99%