Abstract
A popular framework for the interpretation of image sequences is
based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2].
Jojic and Frey [3] provide a generative probabilistic model framework for this
task. However, this layered models do not explicitly account for variation due to
changes in the pose and self occlusion. In this paper we show that if the motion
of the object is large so that different aspects (or views) of the object are visible at
different times in the sequence, we can learn appearance models of the different
aspects using a mixture modelling approach.
based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2].
Jojic and Frey [3] provide a generative probabilistic model framework for this
task. However, this layered models do not explicitly account for variation due to
changes in the pose and self occlusion. In this paper we show that if the motion
of the object is large so that different aspects (or views) of the object are visible at
different times in the sequence, we can learn appearance models of the different
aspects using a mixture modelling approach.
| Original language | English |
|---|---|
| Title of host publication | Advances in Informatics |
| Subtitle of host publication | 10th Panhellenic Conference on Informatics, PCI 2005, Volas, Greece, November 11-13, 2005. Proceedings |
| Publisher | Springer |
| Pages | 746-756 |
| Number of pages | 11 |
| ISBN (Electronic) | 978-3-540-32091-3 |
| ISBN (Print) | 978-3-540-29673-7 |
| DOIs | |
| Publication status | Published - 2005 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 3746 |
| ISSN (Print) | 0302-9743 |