Blind rectification of radial distortion by line straightness

Benligiray, Burak; Topal, Cihan

doi:10.1109/eusipco.2016.7760386

Cited by 10 publications

(15 citation statements)

References 22 publications

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“…The efficiency of LLS algorithm outperforms the nonlinear algorithms significantly while with nearly the same accuracy, which would be shown in Section IV-A. Because the procedure of distortion parameters estimation is repeated in each selection run, the application of LLS algorithm would indeed save a lot of time for feature selection, thus making our feature selection scheme more efficient than the one used in [31].…”

Section: Introductionmentioning

confidence: 73%

“…Though their method works very well, sometimes it may be non-deterministic caused by RANSAC. To improve the robustness of distortion estimation, Benligiray and Topal [31] applied a sequential backward selection scheme to eliminate lines that come from non-collinear elements. Line orientation is taken as the error function, which is minimized using a nonlinear optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The selection run would be repeated until no further improvement in the objective function or there are only three lines left in the lines group. (2) No nonlinear optimization is needed in distortion model estimation, though it is usually used in other's work [7], [31]. Instead, a Linear Least Squares (LLS) optimization algorithm is applied to estimate distortion parameters when there are more than three lines.…”

Section: Introductionmentioning

confidence: 99%

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Robust Line-Based Radial Distortion Estimation From a Single Image

Zhang

Shang

et al. 2019

IEEE Access

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The pinhole model utilized in most computer vision algorithms becomes unfeasible because of lens distortion. Thus it is a must to compensate lens distortion to make the pinhole model available. In this paper, we propose a new robust line-based distortion estimation method to correct radial distortion. Our method works from a single image and is able to estimate the distortion center rather than assuming it is at the image center. Distortion parameters are estimated from parameters of circulars arcs, on the basis that straight lines are imaged as circular arcs under one-parameter division model. A new feature selection scheme by refining circular arcs is introduced to make the process of distortion estimation fully automatic and more robust. Moreover, a linear optimization algorithm is applied to calculating parameters in each selection run, making our feature selection scheme more efficient. Experiments on synthetic images and real images show that our method performs well in radial distortion estimation even with severely distorted images.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Line-Based Radial Distortion Estimation From a Single Image

Zhang

Shang

et al. 2019

IEEE Access

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“…Because the line information is a dominant factor in selecting the optimally corrected image, the selection of an appropriate line detection method is important. Although Benligiray et al recently proposed a line detection method to correct the geometric distortion [20], they detected only collinear lines in the real world. For this reason, the accuracy of Benligiray's method depends on the number of collinear lines in the central and peripheral regions of the image.…”

Section: Selection Of the Optimal Distortion Parametermentioning

confidence: 99%

“…Line detection and distortion correction results using Benligiray's[20] and the proposed methods: (a) input distorted image, (b) line detection result using Benligiray's method, (c) line detection result using the proposed method using the distortion-adaptive sobel filter, (d) correction result using Benligiray's method, and (e) correction result using the proposed method. (Best viewed in color.…”

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confidence: 99%

Correction of Barrel Distortion in Fisheye Lens Images Using Image-Based Estimation of Distortion Parameters

2019

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Images acquired by a fisheye lens camera contain geometric distortion that results in deformation of the object's shape. To correct the lens distortion, existing methods use prior information, such as calibration patterns or lens design specifications. However, the use of a calibration pattern works only when an input scene is a 2-D plane at a prespecified position. On the other hand, the lens design specifications can be understood only by optical experts. To solve these problems, we present a novel image-based algorithm that corrects the geometric distortion. The proposed algorithm consists of three stages: i) feature detection, ii) distortion parameter estimation, and iii) selection of the optimally corrected image out of multiple corrected candidates. The proposed method can automatically select the optimal amount of correction for a fisheye lens distortion by analyzing characteristics of the distorted image using neither prespecified lens design parameters nor calibration patterns. Furthermore, our method performs not only on-line correction by using facial landmark points, but also off-line correction described in subsection III-C. As a result, the proposed method can be applied to a virtual reality (VR) or augmented reality (AR) camera with two fisheye lenses in a field-of-view (FOV) of 195 • , autonomous vehicle vision systems, wide-area visual surveillance systems, and unmanned aerial vehicle (UAV) cameras.

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