Precise radial un-distortion of images

Mallon, John; Whelan, Paul F.

doi:10.1109/icpr.2004.1333995

Cited by 42 publications

(37 citation statements)

References 9 publications

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“…Heikkilä showed how to formulate an approximate inverse model for radial and tangential distortions [215]. A more detailed and accurate inverse model was proposed by Mallon and Whelan [330].…”

Section: Inversion Of Modelsmentioning

confidence: 99%

“…Such models were for example applied for endoscope calibration, see [17,204,454,565]. Other calibration approaches using such models are [8,42,134,148,215,216,274,302,330,381,436,437,514,515,521,525,534]. These approaches often differ with respect to the choice of calibration object, potentially carrying out controlled motions to position it in front of the camera, and with respect to the strategy for estimating the unknowns (e.g., estimating some parameters first while keeping others at initial values).…”

Section: Inversion Of Modelsmentioning

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: Inversion Of Modelsmentioning

confidence: 99%

Section: Inversion Of Modelsmentioning

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

show abstract

“…The methods for image undistortion have been addressed in literature several times (Mallon and Whelan, 2004, Wang et al, 2011, Beauchemin and Bajcsy, 2001). In our case, radial and tangential…”

Section: System Overviewmentioning

confidence: 99%

Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Apeltauer

Babinec²,

Herman³

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a new approach to simultaneous detection and tracking of vehicles moving through an intersection in aerial images acquired by an unmanned aerial vehicle (UAV). Detailed analysis of spatial and temporal utilization of an intersection is an important step for its design evaluation and further traffic inspection. Traffic flow at intersections is typically very dynamic and requires continuous and accurate monitoring systems. Conventional traffic surveillance relies on a set of fixed cameras or other detectors, requiring a high density of the said devices in order to monitor the intersection in its entirety and to provide data in sufficient quality. Alternatively, a UAV can be converted to a very agile and responsive mobile sensing platform for data collection from such large scenes. However, manual vehicle annotation in aerial images would involve tremendous effort. In this paper, the proposed combination of vehicle detection and tracking aims to tackle the problem of automatic traffic analysis at an intersection from visual data. The presented method has been evaluated in several real-life scenarios.

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“…In order to resample a distorted image an inverse of (1) is required. We use the approximate model proposed in Mallon and Whelan (2004) :…”

Section: Simulating Lens Distortionmentioning

confidence: 99%

Which pattern? Biasing aspects of planar calibration patterns and detection methods

Mallon

Whelan

2007

Pattern Recognition Letters

101

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This paper provides a comparative study on the use of planar patterns in the generation of control points for camera calibration. This is an important but often neglected aspect in camera calibration. Two popular checkerboard and circular dot patterns are each examined with two detection strategies for invariance to the potential bias from projective transformations and nonlinear distortions. It is theoretically and experimentally shown that circular patterns can potentially be affected by both biasing sources. Guidelines are given to control such bias. In contrast, appropriate checkerboard detection is shown to be bias free. The findings have important implications for camera calibration, indicating that well accepted methods may give poorer results than necessary if applied naively.

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Precise radial un-distortion of images

Abstract: Abstract

Cited by 42 publications

References 9 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Which pattern? Biasing aspects of planar calibration patterns and detection methods

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