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Tractable and Reliable Registration of 2D Point Sets

På svenska

Author

  • Erik Ask
  • Olof Enqvist
  • Linus Svärm
  • Fredrik Kahl
  • Giuseppe Lippolis

Editor

  • David Fleet
  • Tomas Pajdla
  • Bernt Schiele
  • Tinne Tuytelaars

Summary, in English

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.

Publishing year

2014

Language

English

Pages

393-406

Publication/Series

Lecture Notes in Computer Science (Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I)

Volume

8689

Document type

Conference paper

Publisher

Springer

Topic

  • Mathematics
  • Computer Vision and Robotics (Autonomous Systems)

Keywords

  • Optimization
  • 2D Registration
  • L1 norm

Conference name

13th European Conference on Computer Vision - ECCV 2014

Conference date

2014-09-06 - 2014-09-12

Status

Published

Research group

  • Urological cancer, Malmö
  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 0302-9743
  • ISSN: 1611-3349
  • ISBN: 978-3-319-10589-5 (Print)
  • ISBN: 978-3-319-10590-1 (Online)