Using vanishing points for camera calibration
International Journal of Computer Vision
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Localization Of Modelled Objects Of Revolution In Monocular Perspective Vision
ECCV '90 Proceedings of the First European Conference on Computer Vision
Sensitivity of Calibration to Principal Point Position
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Camera Calibration with One-Dimensional Objects
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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
Camera calibration using spheres: A semi-definite programming approach
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera calibration with two arbitrary coaxial circles
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present a novel algorithm that applies conics to realize reliable camera calibration. In particular, we show that a single view of two coplanar circles is sufficiently powerful to give a fully automatic calibration framework that estimates both intrinsic and extrinsic parameters. This method stems from the previous work of conic based calibration and calibration-free scene analysis. It eliminates many a priori constraints such as known principal point, restrictive calibration patterns, or multiple views. Calibration is achieved statistically through identifying multiple orthogonal directions and optimizing a probability function by maximum likelihood estimate. Orthogonal vanishing points, which build the basic geometric primitives used in calibration, are identified based on the fact that they represent conjugate directions with respect to an arbitrary circle under perspective transformation. Experimental results from synthetic and real scenes demonstrate the effectiveness, accuracy, and popularity of the approach.