Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Tractable and Reliable Registration of 2D Point Sets

In English

Författare

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

Redaktör

  • 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.

Publiceringsår

2014

Språk

Engelska

Sidor

393-406

Publikation/Tidskrift/Serie

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

Volym

8689

Dokumenttyp

Konferensbidrag

Förlag

Springer

Ämne

  • Mathematics
  • Computer Vision and Robotics (Autonomous Systems)

Nyckelord

  • 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

Forskningsgrupp

  • Urological cancer, Malmö
  • Mathematical Imaging Group

ISBN/ISSN/Övrigt

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