Article

Adaptive and generic corner detection based on the accelerated segment test

Authors:

Gregory D. Hager,

Darius Burschka,

Gerhard HirzingerAuthors Info & Claims

ECCV'10: Proceedings of the 11th European conference on Computer vision: Part II

Pages 183 - 196

Published: 05 September 2010 Publication History

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.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECCV'10: Proceedings of the 11th European conference on Computer vision: Part II

September 2010

813 pages

ISBN:3642155510

Editors:
Kostas Daniilidis
GRASP Laboratory, University of Pennsylvania, Philadelphia, PA
,
Petros Maragos
National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece
,
Nikos Paragios
Ecole Centrale de Paris, Department of Applied Mathematics, Chatenay-Malabry, France

Sponsors

Adobe
Google Inc.
Microsoft Research: Microsoft Research
INRIA: Institut Natl de Recherche en Info et en Automatique
IBM: IBM

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 September 2010

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https://dl.acm.org/doi/10.1007/978-3-031-27199-1_58
Neubert TKraetzer CDittmann JCogranne RVerdoliva LLyu SPastoriza JZhang X(2019)A Face Morphing Detection Concept with a Frequency and a Spatial Domain Feature Space for Images on eMRTDProceedings of the ACM Workshop on Information Hiding and Multimedia Security10.1145/3335203.3335721(95-100)Online publication date: 2-Jul-2019
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