A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using vanishing points for camera calibration
International Journal of Computer Vision
Finding the perspective projection of an axis of revolution
Pattern Recognition Letters
A theory of self-calibration of a moving camera
International Journal of Computer Vision
Numerical recipes in C (2nd ed.): the art of scientific computing
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Recognizing general curved objects efficiently
Geometric invariance in computer vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Calibration of Rotating and Zooming Cameras
International Journal of Computer Vision
Self-Calibration from Image Triplets
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Camera Calibration from Symmetry
Proceedings of the 9th IMA Conference on the Mathematics of Surfaces
Repeated Structures: Image Correspondence Constraints and 3D Structure Recovery
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ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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Quadric Reconstruction from Dual-Space Geometry
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding
Self-calibration from one circular motion sequence and two images
Pattern Recognition
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Computer Vision and Image Understanding
Camera calibration based on receptive fields
Pattern Recognition
Camera Calibration from Images of Spheres
IEEE Transactions on Pattern Analysis and Machine Intelligence
Euclidean path modeling for video surveillance
Image and Vision Computing
Self-calibrated visual servoing with respect to axial-symmetric 3D objects
Robotics and Autonomous Systems
Self-calibration from turn-table sequences in presence of zoom and focus
Computer Vision and Image Understanding
Geometric calibration of digital cameras through multi-view rectification
Image and Vision Computing
Camera calibration and geo-location estimation from two shadow trajectories
Computer Vision and Image Understanding
Automatic 3D model reconstruction of cutting tools from a single camera
Computers in Industry
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IEEE Transactions on Robotics
Euclidean Upgrading from Segment Lengths
International Journal of Computer Vision
Shape reconstruction and texture sampling by active rectification and virtual view synthesis
Computer Vision and Image Understanding
Camera calibration with two arbitrary coaxial circles
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Multiple view reconstruction of a quadric of revolution from its occluding contours
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Conics with a common axis of symmetry: properties and applications to camera calibration
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
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This paper addresses the problem of calibrating a pinhole camera from images of a surface of revolution. Camera calibration is the process of determining the intrinsic or internal parameters (i.e., aspect ratio, focal length, and principal point) of a camera, and it is important for both motion estimation and metric reconstruction of 3D models. In this paper, a novel and simple calibration technique is introduced, which is based on exploiting the symmetry of images of surfaces of revolution. Traditional techniques for camera calibration involve taking images of some precisely machined calibration pattern (such as a calibration grid). The use of surfaces of revolution, which are commonly found in daily life (e.g., bowls and vases), makes the process easier as a result of the reduced cost and increased accessibility of the calibration objects. In this paper, it is shown that two images of a surface of revolution will provide enough information for determining the aspect ratio, focal length, and principal point of a camera with fixed intrinsic parameters. The algorithms presented in this paper have been implemented and tested with both synthetic and real data. Experimental results show that the camera calibration method presented here is both practical and accurate.