@inproceedings{66a2e93a939c441cab1400c77f45943e,
title = "Adaptive and generic corner detection based on the accelerated segment test",
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 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.",
keywords = "AGAST, AST, adaptive, corner detector, effcient, generic",
author = "Elmar Mair and Hager, {Gregory D.} and Darius Burschka and Michael Suppa and Gerhard Hirzinger",
year = "2010",
doi = "10.1007/978-3-642-15552-9_14",
language = "English (US)",
isbn = "3642155510",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "183--196",
booktitle = "Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings",
edition = "PART 2",
note = "11th European Conference on Computer Vision, ECCV 2010 ; Conference date: 10-09-2010 Through 11-09-2010",
}