CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching

  • Motilal Agrawal
  • Kurt Konolige
  • Morten Rufus Blas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


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.


Image Match Integral Image Visual Odometry Center Surround Viewpoint Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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

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