Abstract
Motion estimation in sequences with transparencies
is an important problem in robotics and medical
imaging applications. In this work we propose a
variational approach for estimating multi-valued velocity
fields in transparent sequences. Starting from existing
local motion estimators, we derive a variational
model for integrating in space and time such a local
information in order to obtain a robust estimation of
the multi-valued velocity field. With this approach, we
can indeed estimate multi-valued velocity fields which
are not necessarily piecewise constant on a layer –each
layer can evolve according to a non-parametric optical
flow. We show how our approach outperforms existing
methods; and we illustrate its capabilities on challenging
experiments on both synthetic and real sequences.
is an important problem in robotics and medical
imaging applications. In this work we propose a
variational approach for estimating multi-valued velocity
fields in transparent sequences. Starting from existing
local motion estimators, we derive a variational
model for integrating in space and time such a local
information in order to obtain a robust estimation of
the multi-valued velocity field. With this approach, we
can indeed estimate multi-valued velocity fields which
are not necessarily piecewise constant on a layer –each
layer can evolve according to a non-parametric optical
flow. We show how our approach outperforms existing
methods; and we illustrate its capabilities on challenging
experiments on both synthetic and real sequences.
| Original language | English |
|---|---|
| Journal | Journal of Mathematical Imaging and Vision |
| Volume | 40 |
| Issue number | 3 |
| Pages (from-to) | 285-304 |
| Number of pages | 20 |
| ISSN | 0924-9907 |
| DOIs | |
| Publication status | Published - Jul 2011 |
Keywords
- Faculty of Science
- transparent optical flow
- Image regularization
- Multiple motions
- RDK sequences