Line detection in images showing significant lens distortion and application to distortion correction

Alemán‐Flores, Miguel; Álvarez, Luis; Gómez, Luis; Santana-Cedrés, Daniel

doi:10.1016/j.patrec.2013.06.020

Cited by 25 publications

(30 citation statements)

References 20 publications

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“…To this aim, we use the technique proposed by Alemán-Flores et al [2,1]. In both articles, the authors propose a modified Hough transform in order to detect straight lines in the input image.…”

Section: Stage 2: Initial Estimation Of the One-parameter Lens Distormentioning

confidence: 99%

“…In [2], an efficient method to detect lines in images showing significant lens distortion is discussed. Once a set of straight lines has been detected, an algebraic lens distortion model with two distortion parameters is applied to estimate and correct the distortion.…”

Section: Introductionmentioning

confidence: 99%

“…In the second stage, we extract a one-parameter distortion model and the most voted lines using the extended Hough transform and the method proposed in [2].…”

Section: Introductionmentioning

confidence: 99%

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An Iterative Optimization Algorithm for Lens Distortion Correction Using Two-Parameter Models

Santana-Cedrés¹,

Gómez²,

Alemán‐Flores³

et al. 2016

Image Processing on Line

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We present a method for the automatic estimation of two-parameter radial distortion models, considering polynomial as well as division models. The method first detects the longest distorted lines within the image by applying the Hough transform enriched with a radial distortion parameter. From these lines, the first distortion parameter is estimated, then we initialize the second distortion parameter to zero and the two-parameter model is embedded into an iterative nonlinear optimization process to improve the estimation. This optimization aims at reducing the distance from the edge points to the lines, adjusting two distortion parameters as well as the coordinates of the center of distortion. Furthermore, this allows detecting more points belonging to the distorted lines, so that the Hough transform is iteratively repeated to extract a better set of lines until no improvement is achieved. We present some experiments on real images with significant distortion to show the ability of the proposed approach to automatically correct this type of distortion as well as a comparison between the polynomial and division models. Source CodeThe source code, the code documentation, and the online demo are accessible at the IPOL web page of this article 1 In this page, an implementation is available for download. Compilation and usage instructions are included in the README.txt file of the archive.

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Section: Stage 2: Initial Estimation Of the One-parameter Lens Distormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Iterative Optimization Algorithm for Lens Distortion Correction Using Two-Parameter Models

Santana-Cedrés¹,

Gómez²,

Alemán‐Flores³

et al. 2016

Image Processing on Line

Self Cite

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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%

“…Lens distortion is estimated by the division model using one parameter, which allows stating the problem into the Hough transform scheme by adding a distortion parameter to better extract straight lines from the image. [24][25][26] RGB-D cameras, such as the Microsoft Kinect, have become very widely used in perceptual computing applications. To utilize the full potential of RGB-D devices, calibration must be performed to determine the intrinsic and extrinsic parameters of the color and depth sensors and to reduce lens and depth distortion.…”

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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Straight Boundary Detection Algorithm Based on Orientation Filter

Cui

Wang

2018

Advances in Intelligent Systems and Computing

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Line detection in images showing significant lens distortion and application to distortion correction

Cited by 25 publications

References 20 publications

An Iterative Optimization Algorithm for Lens Distortion Correction Using Two-Parameter Models

An Iterative Optimization Algorithm for Lens Distortion Correction Using Two-Parameter Models

Correction of image radial distortion based on division model

Straight Boundary Detection Algorithm Based on Orientation Filter

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