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Automatic Radial Distortion Estimation From a Single Image

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dc.contributor.advisor Dailey, Matthew N. (Chairman) en_US
dc.contributor.author Bukhari, Faisal en_US
dc.contributor.other Afzulpurkar, Nitin (Member) en_US
dc.contributor.other Duboz, Raphael (Member) en_US
dc.contributor.other Dr. Luis G omez D eniz (External Examiner) en_US
dc.date.accessioned 2015-01-12T10:37:12Z
dc.date.available 2015-01-12T10:37:12Z
dc.date.issued 2012-12 en_US
dc.identifier.other AIT Diss no.CS-12-05 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/108
dc.description.abstract Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with o -the-shelf cameras is usually signi cant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. In this dissertation we propose a new and fully automatic method for radial distortion estimation based on the plumb-line approach. First, the method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon's division model. Second, we devise a new algorithm for robust estimation of circular arcs. Third, we design and implement a new algorithm for robust estimation of lens distortion parameters based on the estimated circular arcs. Fourth, we perform an extensive empirical study of the method on synthetic images. We develop our own data set for synthetic images under di erent levels of lambda and distortion center. Fifth, we perform a comparative statistical analysis of how di erent circle tting methods contribute to accurate distortion parameter estimation. Sixth, we provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method's ability to accurately identify distortion parameters and remove distortion from images. Seventh, we perform a direct comparison of our method with that of Alvarez et al. (Alvarez, Gomez, & Sendra, 2009), the only researchers who have provided a publicly accessible implementation of their method, on synthetic images. Finally, we provide the source code based on OpenCV (Bradski, 2000) online1 for researchers interested in evaluating or extending our procedure. en_US
dc.description.sponsorship HEC en_US
dc.language.iso eng en_US
dc.subject.lcsh Others en_US
dc.title Automatic Radial Distortion Estimation From a Single Image en_US
dc.type Dissertation en_US

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