Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Conics-based stereo, motion estimation, and pose determination
International Journal of Computer Vision
Invariants of Six Points and Projective Reconstruction From Three Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conic Reconstruction and Correspondence From Two Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision
Reconstruction of General Curves, Using Factorization and Bundle Adjustment
International Journal of Computer Vision
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Multiple View Geometry of General Algebraic Curves
International Journal of Computer Vision
Projective Reconstruction from Multiple Views with Minimization of 2D Reprojection Error
International Journal of Computer Vision
A subspace approach for matching 2D shapes under affine distortions
Pattern Recognition
Projective reconstruction of general 3D planar curves from uncalibrated cameras
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Ellipse constraints for improved wide-baseline feature matching and reconstruction
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
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This paper presents a new approach for reconstructing 3D ellipses (including circles) from a sequence of 2D images taken by uncalibrated cameras. Our strategy is to estimate an ellipse in 3D space by reconstructing N(=5) 3D points (called representative points) on it, where the representative points are reconstructed by minimizing the distances from their projections to the measured 2D ellipses on different images (i.e., 2D reprojection error). This minimization problem is transformed into a sequence of minimization sub-problems that can be readily solved by an algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Our method can reconstruct multiple 3D ellipses simultaneously from multiple images and it readily handles images with missing and/or partially occluded ellipses. The proposed method is evaluated using both synthetic and real data.