Shift-map Image Registration
Author
Summary, in English
Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes $\alpha$-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.
* The first 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.
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.
* The first 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.
Department/s
- Mathematics (Faculty of Engineering)
- Mathematical Imaging Group
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
Publishing year
2010
Language
English
Full text
Document type
Conference paper
Topic
- Computer Vision and Robotics (Autonomous Systems)
- Mathematics
Conference name
Swedish Symposium on Image Analysis (SSBA) 2010
Conference date
2010-03-11 - 2010-03-12
Conference place
Uppsala, Sweden
Status
Unpublished
Research group
- Mathematical Imaging Group