Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Unifying Registration and Segmentation for Multi-sensor Images
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Joint registration and segmentation of dynamic cardiac perfusion images using MRFs
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Template based gibbs probability distributions for texture modeling and segmentation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
| Hi-index | 0.00 |
Usually object segmentation and motion estimation are considered (and modelled) as different tasks. For motion estimation this leads to problems arising especially at the boundary of an object moving in front of another if e.g. prior assumptions about continuity of the motion field are made. Thus we expect that a good segmentation will improve the motion estimation and vice versa. To demonstrate this we consider the simple task of joint segmentation and motion estimation of an arbitrary (non-rigid) object moving in front of a still background. We propose a statistical model which represents the moving object as a triangular (hexagonal) mesh of pairs of corresponding points and introduce an provably correct iterative scheme, which simultaneously finds the optimal segmentation and corresponding motion field.