Unscented transformation for depth from motion-blur in videos
Published in IEEE Conference on Computer Vision and Pattern Recognition-Workshops, 2010
Paramanand Chandramouli, Ambasamudram Narayanan Rajagopalan
Abstract
In images and videos of a 3D scene, blur due to camera shake can be a source of depth information. Our objective is to find the shape of the scene from its motion-blurred observations without having to restore the original image. In this paper, we pose depth recovery as a recursive state estimation problem. We show that the relationship between the observation and the scale factor of the motion-blur kernel associated with the depth at a point is nonlinear and propose the use of the unscented Kalman filter for state estimation. The performance of the proposed method is evaluated on many examples.
Resources
Bibtex
@inproceedings{paramanand2010unscented, title={Unscented transformation for depth from motion-blur in videos}, author={Paramanand, Chandramouli and Rajagopalan, Ambasamudram N}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition-Workshops}, pages={38–44}, year={2010}, organization={IEEE} }