CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching

  • Motilal Agrawal
  • Kurt Konolige
  • Morten Rufus Blas
Conference paper

DOI: 10.1007/978-3-540-88693-8_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)
Cite this paper as:
Agrawal M., Konolige K., Blas M.R. (2008) CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching. In: Forsyth D., Torr P., Zisserman A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5305. Springer, Berlin, Heidelberg

Abstract

We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Motilal Agrawal
    • 1
  • Kurt Konolige
    • 2
  • Morten Rufus Blas
    • 3
  1. 1.SRI InternationalMenlo ParkUSA
  2. 2.Willow GarageMenlo ParkUSA
  3. 3.Elektro/DTU UniversityLyngbyDenmark

Personalised recommendations