Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Creating full view panoramic image mosaics and environment maps
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Mirrors in motion: Epipolar geometry and motion estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Radon-Based Structure from Motion without Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A unifying geometric representation for central projection systems
Computer Vision and Image Understanding - Special issue on omnidirectional vision and camera networks
Homography Estimation from Planar Contours
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
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Homographies arewidely used in tasks like camera calibration, tracking, mosaicing ormotion estimation and numerous linear and non linear methods for homography estimation have been proposed in the case of classical cameras. Recently, some works have also proved the validity of homography for catadioptric cameras but only a linear estimator has been proposed. In order to improve the estimation based on correspondence features, we suggest in this article some non linear estimators for catadioptric sensors. Catadioptric camera motion estimation from a sequence of a planar scene is the proposed application for the evaluation and the comparison of these estimation methods. Experimental results with simulated and real sequences show that non linear methods are more accurate.