Self-calibration from multiple views with a rotating camera
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
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ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
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Proceedings of the 24th DAGM Symposium on Pattern Recognition
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Violating Rotating Camera Geometry: The Effect of Radial Distortion on Self-Calibration
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
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International Journal of Computer Vision
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ACM SIGGRAPH 2006 Papers
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ACM Transactions on Mathematical Software (TOMS)
The impact of radial distortion on the self-calibration of rotating cameras
Computer Vision and Image Understanding
Self-calibration with partially known rotations
Proceedings of the 29th DAGM conference on Pattern recognition
Active self-calibration of multi-camera systems
Proceedings of the 32nd DAGM conference on Pattern recognition
An Efficient Shortest Triangle Paths Algorithm Applied to Multi-camera Self-calibration
Journal of Mathematical Imaging and Vision
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We present a method for active self-calibration of multi-camera systems consisting of pan-tilt zoom cameras. The main focus of this work is on extrinsic self-calibration using active camera control. Our novel probabilistic approach avoids multi-image point correspondences as far as possible. This allows an implicit treatment of ambiguities. The relative poses are optimized by actively rotating and zooming each camera pair in a way that significantly simplifies the problem of extracting correct point correspondences. In a final step we calibrate the entire system using a minimal number of relative poses. The selection of relative poses is based on their uncertainty. We exploit active camera control to estimate consistent translation scales for triplets of cameras. This allows us to estimate missing relative poses in the camera triplets. In addition to this active extrinsic self-calibration we present an extended method for the rotational intrinsic self-calibration of a camera that exploits the rotation knowledge provided by the camera's pan-tilt unit to robustly estimate the intrinsic camera parameters for different zoom steps as well as the rotation between pan-tilt unit and camera. Quantitative experiments on real data demonstrate the robustness and high accuracy of our approach. We achieve a median reprojection error of $$0.95$$0.95 pixel.