IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Properties of the E Matrix in Two-View Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion from point matches: multiple of solutions
International Journal of Computer Vision
Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Robust egomotion estimation from affine motion parallax
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Recovery of Ego-Motion Using Region Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Differential Algorithm for Motion Recovery: AGeometric Approach
International Journal of Computer Vision
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Comparison of Approaches to Egomotion Computation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
| Hi-index | 0.00 |
In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that, provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion which allows to separate the image deformation in a similarity and a "purely" projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediately deduce camera motion parameters.