Camera Calibration with Distortion Models and Accuracy Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Projective to Euclidean Space Under any Practical Situation, a Criticism of Self-Calibration
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Nonmetric Lens Distortion Calibration: Closed-form Solutions, Robust Estimation and Model Selection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pseudo-linearizing collinearity constraint for accurate pose estimation from a single image
Pattern Recognition Letters
Parameter-Free Radial Distortion Correction with Center of Distortion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera calibration based on arbitrary parallelograms
Computer Vision and Image Understanding
High-Accuracy and Robust Localization of Large Control Markers for Geometric Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we apply the collinearity constraint for accurate camera calibration and correction. The novel method consists of two steps: the first is to estimate the relative parameters of interest with closed form solutions. The second employs the well known Levernburg-Marquardt (LM) algorithm for the global optimization of all the parameters of interest: 4 intrinsic, 7 extrinsic and 4 distortion parameters. The LM algorithm is initialised either as the parameters estimated in the first step or as zero. The optimization is achieved through minimising the sum of the squared back projected errors. The distorted points are finally corrected using again the LM algorithm initialized by the distorted image points themselves, minimizing the squared difference between the distorted corrected point and the given distorted image point. A comparative study based on both synthetic data and real images show that the proposed algorithm produces promising camera calibration and correction results.