An Image Motion Estimation Technique Based on a Combined Statistical Test and Spatiotemporal Generalised Likelihood Ratio Approach
Abstract
We present a method based on Kalman filtering, for image motion estimation. Within Kalman formalism, a motion boundary can be modelled as a jump in the evolution equation of the filter. The detection of such a jump relies on a χ^2 statistical test applied to the innovation signal. The optimal estimation of the jump parameters and the compensation of the current estimate are performed using a General Likelihood Ratio (GLR) algorithm. To exploit the spatial redundancy inherent to a motion boundary, the original GLR algorithm is reformulated by integrating spatiotemporal motion information. This results in a significant decrease of the compensation delay.
Cite
Text
Germain and Skordas. "An Image Motion Estimation Technique Based on a Combined Statistical Test and Spatiotemporal Generalised Likelihood Ratio Approach." European Conference on Computer Vision, 1994. doi:10.1007/3-540-57956-7_17Markdown
[Germain and Skordas. "An Image Motion Estimation Technique Based on a Combined Statistical Test and Spatiotemporal Generalised Likelihood Ratio Approach." European Conference on Computer Vision, 1994.](https://mlanthology.org/eccv/1994/germain1994eccv-image/) doi:10.1007/3-540-57956-7_17BibTeX
@inproceedings{germain1994eccv-image,
title = {{An Image Motion Estimation Technique Based on a Combined Statistical Test and Spatiotemporal Generalised Likelihood Ratio Approach}},
author = {Germain, Florence and Skordas, Thomas},
booktitle = {European Conference on Computer Vision},
year = {1994},
pages = {152-157},
doi = {10.1007/3-540-57956-7_17},
url = {https://mlanthology.org/eccv/1994/germain1994eccv-image/}
}