Curve and surface fitting with splines
Curve and surface fitting with splines
Non-Parametric Self-Calibration
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Parameter-Free Radial Distortion Correction with Centre of Distortion Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Factorization Based Self-Calibration for Radially Symmetric Cameras
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Discrete camera calibration from pixel streams
Computer Vision and Image Understanding
Generic self-calibration of central cameras
Computer Vision and Image Understanding
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Generic Self-calibration of Central Cameras from Two Rotational Flows
International Journal of Computer Vision
Self-calibration of a general radially symmetric distortion model
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Planar Motion Estimation and Linear Ground Plane Rectification using an Uncalibrated Generic Camera
International Journal of Computer Vision
International Journal of Computer Vision
Shape from specular flow: Is one flow enough?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Scale Space Analysis and Active Contours for Omnidirectional Images
IEEE Transactions on Image Processing
Scale space for central catadioptric systems: Towards a generic camera feature extractor
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The calibration of a generic central camera can be described non-parametrically by a map assigning to each image pixel a 3D projection ray. We address the determination of this map and the motion of a camera that performs two infinitesimal rotations about linearly independent axes. A complex closed-form solution exists, which in practice allows to visually identify the geometry of a range of sensors, but it only works at the center of the image domain and not accurately. We present a new two-step method to solve the stated self-calibration problem that overcomes these drawbacks. Firstly, the Gram matrix of the camera rotation velocities is estimated jointly with the Lie bracket of the two rotational flows computed from the data images. Secondly, the knowledge that such Lie bracket is also a rotational flow is exploited to provide a solution for the calibration map which is defined on the whole image domain. Both steps are essentially linear, being robust to the noise inherent to the computation of optical flow from images. The accuracy of the proposed method is quantitatively demonstrated for different noise levels, rotation pairs, and imaging geometries. Several applications are exemplified, and possible extensions and improvements are also considered.