Parametric Object Motion from Blur

• Published in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1846 - 1854
• Main Authors: Gast, Jochen, Sellent, Anita, Roth, Stefan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Cameras; Computer vision; Estimation; Image segmentation; Kernel; Motion segmentation; Optical imaging
• DOI: 10.1109/CVPR.2016.204

Motion blur can adversely affect a number of vision tasks, hence it is generally considered a nuisance. We instead treat motion blur as a useful signal that allows to compute the motion of objects from a single image. Drawing on the success of joint segmentation and parametric motion models in the context of optical flow estimation, we propose a parametric object motion model combined with a segmentation mask to exploit localized, non-uniform motion blur. Our parametric image formation model is differentiable w.r.t. the motion parameters, which enables us to generalize marginal-likelihood techniques from uniform blind deblurring to localized, non-uniform blur. A two-stage pipeline, first in derivative space and then in image space, allows to estimate both parametric object motion as well as a motion segmentation from a single image alone. Our experiments demonstrate its ability to cope with very challenging cases of object motion blur.