Matrix algorithms
Properties of the Catadioptric Fundamental Matrix
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Omni-Directional Structure from Motion
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Ego-Motion and Omnidirectional Cameras
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multi-View Geometry for General Camera Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Towards Complete Generic Camera Calibration
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A generic structure-from-motion framework
Computer Vision and Image Understanding - Special issue on omnidirectional vision and camera networks
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
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In estimating motions of multi-centered optical systems using the generalized camera model, one can use the linear seventeen-point algorithm for obtaining a generalized essential matrix, the counterpart of the eight-point algorithm for the essential matrix of a pair of cameras. Like the eight-point algorithm, the seventeen-point algorithm has degenerate cases. However, mechanisms of the degeneracy of this algorithm have not been investigated. We propose a method to find degenerate cases of the algorithm by decomposing a measurement matrix that is used in the algorithm into two matrices about ray directions and centers of projections. This decomposition method allows us not only to prove degeneracy of the previously known degenerate cases, but also to find a new degenerate configuration.