Constant resolution omnidirectional cameras

Gaspar, José António; Decco, C.; Okamoto, Jun; Santos-Victor, José

doi:10.1109/omnvis.2002.1044487

Cited by 43 publications

(35 citation statements)

References 13 publications

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“…Gaspar et al proposed a general approach allowing to derive several of the above mirror designs in a unified framework [156,157]. Hicks as well as Menegatti formulated and provided a solution to the so-called prescribed projection problem [221,341]: the input is a desired mapping between an object surface and the image plane of a camera in a given position.…”

Section: Non-central Catadioptric Camerasmentioning

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: Non-central Catadioptric Camerasmentioning

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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“…The catadioptric sensors that do not possess a single view point (non-SVP) are less used but have proved to be helpful for applications with specific requirements such as prescribed distortions (Hicks and Perline, 2001) or with linear projection constraints (Gaspar et al, 2002).…”

Section: Omnidirectional Vision Sensorsmentioning

confidence: 99%

Modelling and accuracy estimation of a new omnidirectional depth computation sensor

Orghidan

Salví

Mouaddib

2006

Pattern Recognition Letters

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Depth computation is an attractive feature in computer vision. The use of traditional perspective cameras for panoramic perception requires several images, most likely implying the use of several cameras or of a sensor with mobile elements. Moreover, misalignments can appear for non-static scenes. Omnidirectional cameras offer a much wider field of view (FOV) than perspective cameras, capture a panoramic image at every moment and alleviate problems due to occlusions. A practical way to obtain depth in computer vision is the use of structured light systems. This paper is focused on combining omnidirectional vision and structured light with the aim of obtaining panoramic depth information. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. The model and the prototype of a new omnidirectional depth computation sensor are presented in this article and its accuracy is estimated by means of laboratory experimental setups.

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“…If there is misalignment, the camera cannot maintain desired optical characteristics such as a single viewpoint [1,2], equi-resolution [3], or equi-areal [4]. The misalignment problem causes various errors in systems using catadioptric cameras.…”

Section: Introductionmentioning

confidence: 99%

“…Although these methods are effective for central catadioptric cameras, the mirror misaligned catadioptric camera is not a central catadioptric camera. Moreover, there are non-single viewpoints catadioptric cameras [3,4,9,10].…”

Section: Introductionmentioning

confidence: 99%