Locally-weighted Homographies for Calibration of Imaging Systems

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014

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A homography is traditionally formulated as a linear transformation and is used in multiple-view geometry as a linear map between projective planes (or images). Analogous to the use of homography-based techniques to calibrate a pin-hole camera, non-linear homographies extend the pin-hole camera model to deal with non-linearities such as lens distortion. In this work, we propose a novel non-parametric non-linear homography technique. Unlike a parametric non-linear mapping that can have inherent biases, this technique automatically adjusts model complexity to account for non-linearities in observed data. With this technique, we demonstrate non-parametric estimation of lens distortion from a single calibration image. We evaluate this technique on real-world lenses and show that this technique can improve the stability of camera-calibration. Furthermore, the non-parametric nature of our technique allows rectification of arbitrary sources of lens distortion.


    TITLE      = {Locally-weighted Homographies for Calibration of Imaging Systems},
    AUTHOR     = {Pradeep Ranganathan and Edwin Olson},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2014},
    MONTH      = {October},
    KEYWORDS   = {Calibration and Identification, Computer Vision},