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Open AccessArticle

MATE: Machine Learning for Adaptive Calibration Template Detection

iMinds - IPI, Ghent University, Ghent B-9000, Belgium
Author to whom correspondence should be addressed.
Academic Editor: Fabrizio Lamberti
Sensors 2016, 16(11), 1858;
Received: 16 July 2016 / Revised: 14 September 2016 / Accepted: 2 November 2016 / Published: 4 November 2016
(This article belongs to the Section Physical Sensors)
The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves—typically in the form of chessboard corners—need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups. View Full-Text
Keywords: computer vision; camera calibration; checkerboard detection; deep learning computer vision; camera calibration; checkerboard detection; deep learning
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Donné, S.; De Vylder, J.; Goossens, B.; Philips, W. MATE: Machine Learning for Adaptive Calibration Template Detection. Sensors 2016, 16, 1858.

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