Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Self-calibration of an affine camera from multiple views
International Journal of Computer Vision
A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear and Incremental Acquisition of Invariant Shape Models From Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Paraperspective Factorization Method for Shape and Motion Recovery
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Affine Epipolar Geometry via Factorization Method
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
3D Fundus Pattern Reconstruction and Display from Multiple Fundus Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
3-D Shape Reconstruction of Retinal Fundus
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Hybrid retinal image registration
IEEE Transactions on Information Technology in Biomedicine
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We study 3D retinal surface reconstruction by using an affine camera due to two following reasons: (1) NIH's retinal imaging protocols specify a narrow field of view and (2) each retinal image has small depth variation. Specifically, we incorporate the prior knowledge of human retina geometry in the reconstruction process, and introduce a point-based approach to estimate the retinal spherical surface. We also show that lens distortion removal and affine bundle adjustment improve the reconstruction error in terms of the deviation from the underling spherical surface. Simulation results on both synthetic data and real images show the effectiveness and robustness of the proposed algorithm.