Abstract
Typically, the problems of spatial and temporal alignment
of sequences are considered disjoint. That is, in order
to align two sequences, a methodology that (non)-rigidly
aligns the images is first applied, followed by temporal
alignment of the obtained aligned images. In this paper, we
propose the first, to the best of our knowledge, methodology
that can jointly spatio-temporally align two sequences,
which display highly deformable texture-varying objects.
We show that by treating the problems of deformable spatial
and temporal alignment jointly, we achieve better results
than considering the problems independent. Furthermore,
we show that deformable spatio-temporal alignment
of faces can be performed in an unsupervised manner (i.e.,
without employing face trackers or building person-specific
deformable models).
Original language | Undefined |
---|---|
Title of host publication | Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR 2016) |
Place of Publication | USA |
Publisher | IEEE Computer Vision and Pattern Recognition |
Pages | 3382-3390 |
Number of pages | 9 |
ISBN (Print) | 978-1-4673-8852-8 |
DOIs | |
Publication status | Published - Jun 2016 |
Event | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, NV, USA, Las Vegas, United States Duration: 26 Jun 2016 → 1 Jul 2016 Conference number: 29 |
Conference
Conference | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
---|---|
Abbreviated title | CVPR 2016 |
Country/Territory | United States |
City | Las Vegas |
Period | 26/06/16 → 1/07/16 |
Other | 29 June - 1 July 2016 |
Keywords
- deformable spatial and temporal alignment of sequences
- HMI-HF: Human Factors
- EWI-27132