Some Properties of the E Matrix in Two-View Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematical Programming: Series A and B
International Journal of Computer Vision - 1998 Marr Prize
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Globally Convergent Autocalibration Using Interval Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Focal length calibration from two views: method and analysis of singular cases
Computer Vision and Image Understanding
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This paper presents a framework for random sampling nonlinear optimization for camera self-calibration with modeling of the camera intrinsic parameter space. The focal length is modeled using a Gaussian distribution derived from the results of the Kruppa equations, while the optical center is modeled based on the assumption that the optical center is close to the image center but deviates from it due to some manufacturing imprecision. This model enables us to narrow the search range of parameter space and therefore reduce the computation cost. In addition, a random sampling strategy is utilized in order to avoid local optima, where the samples are drawn according to this model. Experimental results are presented to show the effectiveness of the proposed nonlinear optimization algorithm, even in the under-constrained case involving only two frames.