Adaptive and Generic Corner Detection Based on the Accelerated Segment Test

  • Elmar Mair
  • Gregory D. Hager
  • Darius Burschka
  • Michael Suppa
  • Gerhard Hirzinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)


The efficient detection of interesting features is a crucial step for various tasks in Computer Vision. Corners are favored cues due to their two dimensional constraint and fast algorithms to detect them. Recently, a novel corner detection approach, FAST, has been presented which outperforms previous algorithms in both computational performance and repeatability. We will show how the accelerated segment test, which underlies FAST, can be significantly improved by making it more generic while increasing its performance.We do so by finding the optimal decision tree in an extended configuration space, and demonstrating how specialized trees can be combined to yield an adaptive and generic accelerated segment test. The resulting method provides high performance for arbitrary environments and so unlike FAST does not have to be adapted to a specific scene structure. We will also discuss how different test patterns affect the corner response of the accelerated segment test.


Decision Tree Corner Detection Mask Size Memory Access Time Ternary Tree 
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 2010

Authors and Affiliations

  • Elmar Mair
    • 1
  • Gregory D. Hager
    • 2
  • Darius Burschka
    • 1
  • Michael Suppa
    • 3
  • Gerhard Hirzinger
    • 3
  1. 1.Department of Computer ScienceTechnische Universität München (TUM)Garching bei MünchenGermany
  2. 2.Department of Computer ScienceJohns Hopkins University (JHU)BaltimoreUSA
  3. 3.German Aerospace Center (DLR)Institute of Robotics and MechatronicsWesslingGermany

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