IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Algorithm for Discontinuity-Preserving Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
A two-stage algorithm for motion discontinuity-preserving optical flow estimation
Computer Vision and Image Understanding
Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining the Hough Transform and Multiresolution MRF's for the Robust Motion Estimation
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Estimating Piecewise-Smooth Optical Flow with Global Matching and Graduated Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global parametric image alignment via high-order approximation
Computer Vision and Image Understanding
Detection and compression of moving objects based on new panoramic image modeling
Image and Vision Computing
New panoramic image generation based on modeling of vignetting and illumination effects
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
| Hi-index | 0.14 |
This paper proposes a motion estimation algorithm that is robust to motion discontinuity and noise. The proposed algorithm is constructed by embedding the least median squares (LMedS) of robust statistics into the maximum a posteriori (MAP) estimator. Difficulties in accurate estimation of the motion field arise from the smoothness constraint and the sensitivity to noise. To cope robustly with these problems, a median operator and the concept of reweighted least squares (RLS) are applied to the MAP motion estimator, resulting in the reweighted robust MAP (RRMAP). The proposed RRMAP motion estimation algorithm is also generalized for multiple image frame cases. Computer simulation with various synthetic image sequences shows that the proposed algorithm reduces errors, compared to three existing robust motion estimation algorithms that are based on M-estimation, total least squares (TLS), and Hough transform. It is also observed that the proposed algorithm is statistically efficient and robust to additive Gaussian noise and impulse noise. Furthermore, the proposed algorithm yields reasonable performance for real image sequences.