Shift-map Image Registration
(2010) International Conference on Pattern Recognition (ICPR 2010)- Abstract
- Shift-map image processing is a new framework
based on energy minimization over a large space of
labels. The optimization utilizes -expansion moves
and iterative refinement over a Gaussian pyramid. In
this paper we extend the range of applications to image
registration. To do this, new data and smoothness
terms have to be constructed. We note a great improvement
when we measure pixel similarities with the dense
DAISY descriptor. The main contributions of this paper
are:
• The extension of the shift-map framework to include
image registration. We register images for
which SIFT only provides 3 correct matches.
• A publicly... (More) - Shift-map image processing is a new framework
based on energy minimization over a large space of
labels. The optimization utilizes -expansion moves
and iterative refinement over a Gaussian pyramid. In
this paper we extend the range of applications to image
registration. To do this, new data and smoothness
terms have to be constructed. We note a great improvement
when we measure pixel similarities with the dense
DAISY descriptor. The main contributions of this paper
are:
• The extension of the shift-map framework to include
image registration. We register images for
which SIFT only provides 3 correct matches.
• A publicly available implementation of shift-map
image processing (e.g. inpainting, registration).
We conclude by comparing shift-map registration to a
recent method for optical flow with favorable results. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1609650
- author
- Svärm, Linus LU and Strandmark, Petter LU
- organization
- publishing date
- 2010
- type
- Contribution to conference
- publication status
- in press
- subject
- conference name
- International Conference on Pattern Recognition (ICPR 2010)
- conference location
- Istanbul, Turkey
- conference dates
- 2010-08-23 - 2010-08-26
- language
- English
- LU publication?
- yes
- id
- f1ef390d-111c-4a86-a7a8-bbf39e98e722 (old id 1609650)
- date added to LUP
- 2016-04-04 13:45:54
- date last changed
- 2019-04-30 21:46:25
@misc{f1ef390d-111c-4a86-a7a8-bbf39e98e722,
abstract = {{Shift-map image processing is a new framework<br/><br>
based on energy minimization over a large space of<br/><br>
labels. The optimization utilizes -expansion moves<br/><br>
and iterative refinement over a Gaussian pyramid. In<br/><br>
this paper we extend the range of applications to image<br/><br>
registration. To do this, new data and smoothness<br/><br>
terms have to be constructed. We note a great improvement<br/><br>
when we measure pixel similarities with the dense<br/><br>
DAISY descriptor. The main contributions of this paper<br/><br>
are:<br/><br>
• The extension of the shift-map framework to include<br/><br>
image registration. We register images for<br/><br>
which SIFT only provides 3 correct matches.<br/><br>
• A publicly available implementation of shift-map<br/><br>
image processing (e.g. inpainting, registration).<br/><br>
We conclude by comparing shift-map registration to a<br/><br>
recent method for optical flow with favorable results.}},
author = {{Svärm, Linus and Strandmark, Petter}},
language = {{eng}},
title = {{Shift-map Image Registration}},
url = {{https://lup.lub.lu.se/search/files/6199559/1609651.pdf}},
year = {{2010}},
}