Automatic Radial Distortion Estimation from a Single Image

Bukhari, Faisal; Dailey, Matthew N.

doi:10.1007/s10851-012-0342-2

Cited by 142 publications

(105 citation statements)

References 25 publications

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“…In all these works the user is required to manually select the image contours corresponding to the projection of lines. Very recently Bukhari et al [9] suggested an algorithm for automatically detecting lines and accomplishing the calibration following the methods of [24]. We advance the state-of-the-art in "plumb-line" calibration by showing that 3 lines enable to also recover the aspect ratio and skew and, more importantly, by providing an algorithm for unsupervised calibration that largely outperforms [9] in terms of accuracy, robustness, and computational efficiency.…”

Section: Related Workmentioning

confidence: 86%

Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach

Melo

Antunes

Barreto

et al. 2013

2013 IEEE International Conference on Computer Vision

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Estimating the amount and center of distortion from lines in the scene has been addressed in the literature by the socalled "plumb-line" approach. In this paper we propose a new geometric method to estimate not only the distortion parameters but the entire camera calibration (up to an "angular" scale factor) using a minimum of 3 lines. We propose a new framework for the unsupervised simultaneous detection of natural image of lines and camera parameters estimation, enabling a robust calibration from a single image. Comparative experiments with existing automatic approaches for the distortion estimation and with ground truth data are presented.

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Section: Related Workmentioning

confidence: 86%

Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach

Melo

Antunes

Barreto

et al. 2013

2013 IEEE International Conference on Computer Vision

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“…Promising works for unsupervised single image calibration with RD were recently introduced [4,13,21,24]. As depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1, all these works start by detecting circles 1 in the image that are likely to be projections of scene lines. They use minimal solvers in a consensus maximization [4,21] or energy minimization [13,24] frameworks for computing some of the calibration parameters (refer to Table 1). It is rather obvious that the quality of the circle estimation in images with RD has a direct impact in the accuracy of camera calibration.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion

Antunes

Barreto

Aouada

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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The article concerns the automatic calibration of a camera with radial distortion from a single image. It is known that, under the mild assumption of square pixels and zero skew, lines in the scene project into circles in the image, and three lines suffice to calibrate the camera up to an ambiguity between focal length and radial distortion. The calibration results highly depend on accurate circle estimation, which is hard to accomplish, because lines tend to project into short circular arcs. To overcome this problem, we show that, given a short circular arc edge, it is possible to robustly determine a line that goes through the center of the corresponding circle. These lines, henceforth called Lines of Circle Centres (LCCs), are used in a new method that detects sets of parallel lines and estimates the calibration parameters, including the center and amount of distortion, focal length, and camera orientation with respect to the Manhattan frame. Extensive experiments in both semi-synthetic and real images show that our algorithm outperforms stateof-the-art approaches in unsupervised calibration from a single image, while providing more information.

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“…6 In the past decades, many methods for radial distortion estimation have been proposed in research. [18][19][20][21][22][23][24][25][26] Bukhari and Dailey 21 proposed a method for automatic radial distortion estimation based on the plumb-line approach. They compared statistical analyses of how different circle fitting methods contribute to accurate distortion parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Correction of image radial distortion based on division model

Wei

Wang

2017

Opt. Eng

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Abstract. This paper presents an approach for estimating and then removing image radial distortion. It works on a single image and does not require a special calibration. The approach is extremely useful in many applications, particularly those where human-made environments contain abundant lines. A division model is applied, in which a straight line in the distorted image is treated as a circular arc. Levenberg-Marquardt (LM) iterative nonlinear least squares method is adopted to calculate the arc's parameters. Then "Taubin fit" is applied to obtain the initial guess of the arc's parameters which works as the initial input to the LM iteration. This dramatically improves the convergence rate in the LM process to obtain the required parameters for correcting image radial distortion. Hough entropy, as a measure, has achieved the quantitative evaluation of the estimated distortion based on the probability distribution in one-dimensional θ Hough space. The experimental results on both synthetic and real images have demonstrated that the proposed method can robustly estimate and then remove image radial distortion with high accuracy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Automatic Radial Distortion Estimation from a Single Image

Cited by 142 publications

References 25 publications

Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach

Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach

Unsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion

Correction of image radial distortion based on division model

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Automatic Radial Distortion Estimation from a Single Image

Cited by 142 publications

References 25 publications

Unsupervised Intrinsic Calibration from a Single Frame Using a &#x0022;Plumb-Line&#x0022; Approach

Unsupervised Intrinsic Calibration from a Single Frame Using a &#x0022;Plumb-Line&#x0022; Approach

Unsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion

Correction of image radial distortion based on division model

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Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach

Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach