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Tractable and Reliable Registration of 2D Point Sets

Ask, Erik LU ; Enqvist, Olof LU ; Svärm, Linus LU ; Kahl, Fredrik LU and Lippolis, Giuseppe LU (2014) 13th European Conference on Computer Vision - ECCV 2014 In Lecture Notes in Computer Science (Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I) 8689. p.393-406
Abstract
This paper introduces two new methods of registering 2D point sets over rigid transformations when the registration error is based on a robust loss function. In contrast to previous work, our methods are guaranteed to compute the optimal transformation, and at the same time, the worst-case running times are bounded by a low-degree polynomial in the number of correspondences. In practical terms, this means that there is no need to resort to ad-hoc procedures such as random sampling or local descent methods that cannot guarantee the quality of their solutions.



We have tested the methods in several different settings, in particular, a thorough evaluation on two benchmarks of microscopic images used for histologic analysis... (More)
This paper introduces two new methods of registering 2D point sets over rigid transformations when the registration error is based on a robust loss function. In contrast to previous work, our methods are guaranteed to compute the optimal transformation, and at the same time, the worst-case running times are bounded by a low-degree polynomial in the number of correspondences. In practical terms, this means that there is no need to resort to ad-hoc procedures such as random sampling or local descent methods that cannot guarantee the quality of their solutions.



We have tested the methods in several different settings, in particular, a thorough evaluation on two benchmarks of microscopic images used for histologic analysis of prostate cancer has been performed. Compared to the state-of-the-art, our results show that the methods are both tractable and reliable despite the presence of a significant amount of outliers. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Optimization, 2D Registration, L1 norm
in
Lecture Notes in Computer Science (Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I)
editor
Fleet, David; Pajdla, Tomas; Schiele, Bernt; Tuytelaars, Tinne; ; ; and
volume
8689
pages
14 pages
publisher
Springer
conference name
13th European Conference on Computer Vision - ECCV 2014
external identifiers
  • wos:000345524200026
  • scopus:84906495656
ISSN
1611-3349
0302-9743
ISBN
978-3-319-10589-5 (Print)
978-3-319-10590-1 (Online)
DOI
10.1007/978-3-319-10590-1_26
language
English
LU publication?
yes
id
9edeafb0-fa89-42c4-9f8c-a96f38b6b333 (old id 4644690)
date added to LUP
2014-09-16 15:05:11
date last changed
2017-05-28 03:03:58
@inproceedings{9edeafb0-fa89-42c4-9f8c-a96f38b6b333,
  abstract     = {This paper introduces two new methods of registering 2D point sets over rigid transformations when the registration error is based on a robust loss function. In contrast to previous work, our methods are guaranteed to compute the optimal transformation, and at the same time, the worst-case running times are bounded by a low-degree polynomial in the number of correspondences. In practical terms, this means that there is no need to resort to ad-hoc procedures such as random sampling or local descent methods that cannot guarantee the quality of their solutions. <br/><br>
<br/><br>
We have tested the methods in several different settings, in particular, a thorough evaluation on two benchmarks of microscopic images used for histologic analysis of prostate cancer has been performed. Compared to the state-of-the-art, our results show that the methods are both tractable and reliable despite the presence of a significant amount of outliers.},
  author       = {Ask, Erik and Enqvist, Olof and Svärm, Linus and Kahl, Fredrik and Lippolis, Giuseppe},
  booktitle    = {Lecture Notes in Computer Science (Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I)},
  editor       = {Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne},
  isbn         = {978-3-319-10589-5 (Print)},
  issn         = {1611-3349},
  keyword      = {Optimization,2D Registration,L1 norm},
  language     = {eng},
  pages        = {393--406},
  publisher    = {Springer},
  title        = {Tractable and Reliable Registration of 2D Point Sets},
  url          = {http://dx.doi.org/10.1007/978-3-319-10590-1_26},
  volume       = {8689},
  year         = {2014},
}