Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
3-D interpretation of optical flow by renormalization
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Linear Differential Algorithm for Motion Recovery: AGeometric Approach
International Journal of Computer Vision
Optimal Structure from Motion: Local Ambiguities and Global Estimates
International Journal of Computer Vision
On the consistency of instantaneous rigid motion estimation
International Journal of Computer Vision
Understanding the Behavior of SFM Algorithms: A Geometric Approach
International Journal of Computer Vision
Comparison of Approaches to Egomotion Computation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Removal of Translation Bias when Using Subspace Methods
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The least-squares error for structure from infinitesimal motion
International Journal of Computer Vision
Damped Newton Algorithms for Matrix Factorization with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A phase-based approach to the estimation of the optical flow field using spatial filtering
IEEE Transactions on Neural Networks
An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow
Proceedings of the 31st DAGM Symposium on Pattern Recognition
On-chip ego-motion estimation based on optical flow
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
A review and evaluation of methods estimating ego-motion
Computer Vision and Image Understanding
Parallel architecture for hierarchical optical flow estimation based on FPGA
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Hi-index | 0.00 |
A novel method is introduced for optimal estimation of rigid camera motion from instantaneous velocity measurements. The error surface associated with this problem is highly complex and existing algorithms suffer heavily from local minima. Repeated minimization with different random initializations and selection of the minimum-cost solution are a common (albeit ad hoc) procedure to increase the likelihood of finding the global minimum. We instead show that the optimal estimation problem can be transformed into one of arbitrary complexity, which allows for a gradual regularization of the error function. A simple reweighting scheme is presented that smoothly increases the problem complexity at each iteration. We show that the resulting method retains all the desirable properties of optimal algorithms, such as unbiasedness and minimal variance of the parameter estimates, but is substantially more robust to local minima. This robustness comes at the expense of a slightly increased computational complexity.