Issues on the geometry of central catadioptric image formation

Barreto, João P.; Araújo, Hélder

doi:10.1109/cvpr.2001.990992

Cited by 106 publications

(110 citation statements)

References 9 publications

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“…Based on their unified model (see Section 3.1.5), they showed that from two respectively three images of lines, one may fully calibrate a central hyper-catadioptric respectively para-catadioptric camera. They gave an algorithm for the para-catadioptric case; Barreto and Araújo developed an algorithm for hyper-catadioptric cameras [28,31] as well as an improved algorithm for para-catadioptric ones that is able to fully compute the mirror shape [30,32]. Ying and Hu, Vandeportaele et al, and Wu et al gave similar algorithms [518,537,551].…”

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: Lines -The Plumb-line Approachmentioning

confidence: 99%

“…Barreto and Araújo proposed a modified version of this model and used it to study various geometric properties, especially those related to line images and their application for calibration [25,28,29]. Mei and Rives proposed to extend the model by adding classical terms for radial and tangential distortion [339].…”

Section: Global Camera Models 55mentioning

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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“…According to [9,3], all central catadioptric cameras can be modeled by a unit sphere and a perspective projection, such that the projection of 3D points can be performed in two steps (Fig. 1).…”

Section: Sphere Camera Modelmentioning

confidence: 99%

“…We integrate the sphere camera model [9,3], which describes any central catadioptric system, selecting it by one single parameter, with the partial differential equations on manifolds framework through the heat diffusion equation [5,1]. Using this framework and the mirror parameter we compute the metric representing that particular reflecting surface.…”

Section: Introductionmentioning

confidence: 99%

Scale space for central catadioptric systems: Towards a generic camera feature extractor

Puig

Guerrero

2011

2011 International Conference on Computer Vision

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In this paper we propose a new approach to compute the scale space of any omnidirectional image acquired with a central catadioptric system. When these cameras are central they are explained using the sphere camera model, which unifies in a single model, conventional, paracatadioptric and hypercatadioptric systems. Scale space is essential in the detection and matching of interest points, in particular scale invariant points based on Laplacian of Gaussians, like the well known SIFT. We combine the sphere camera model and the partial differential equations framework on manifolds, to compute the Laplace-Beltrami (LB) operator which is a second order differential operator required to perform the Gaussian smoothing on catadioptric images. We perform experiments with synthetic and real images to validate the generalization of our approach to any central catadioptric system.

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“…This means that the method proposed can work not only with classical perspective cameras but can also be applied to wide-angle cameras obeying the unified model [9,2]. Wide-angle cameras include catadioptric systems that combine mirrors and conventional cameras to create omnidirectional cameras providing 360 o panoramic views of a scene, or dioptric fish-eye lenses [1,5].…”

Section: Introductionmentioning

confidence: 99%

Robust image-based visual servoing using invariant visual information

Tahri

Araújo

Chaumette

et al. 2013

Robotics and Autonomous Systems

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This paper deals with the use of invariant visual features for visual servoing. New features are proposed to control the 6 degrees of freedom of a robotic system with better linearizing properties and robustness to noise than the state of the art in image-based visual servoing. We show in this paper that by using these features the behavior of image-based visual servoing in task space can be significantly improved. Several experimental results are provided and validate our proposal.

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Issues on the geometry of central catadioptric image formation

Abstract: Abstract

Cited by 106 publications

References 9 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Scale space for central catadioptric systems: Towards a generic camera feature extractor

Robust image-based visual servoing using invariant visual information

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