Adaptive and generic corner detection based on the accelerated segment test

Authors:
Elmar Mair;Gregory D. Hager;Darius Burschka;Michael Suppa;Gerhard Hirzinger
Affiliations:
Technische Universität München, Department of Computer Science, Garching bei München, Germany;Johns Hopkins University, Department of Computer Science, Baltimore, MD;Technische Universität München, Department of Computer Science, Garching bei München, Germany;German Aerospace Center, Institute of Robotics and Mechatronics, Wessling, Germany;German Aerospace Center, Institute of Robotics and Mechatronics, Wessling, Germany
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Year:
2010

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Abstract

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 presentedwhich 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.