BRISK: Binary Robust Invariant Scalable Keypoints
2014-10-24Today in 20min: "Rettungsroboter nach dem Vorbild der Natur"
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2014-06-30The paper from G. Darivianakis, K. Alexis, M. Burri and R. Siegwart on "Hybrid Predictive Control for Aerial Robotic Physical Interaction towards Inspection Operations" finalisted for the Best Automation Paper Award on ICRA 2014, Hong Kong.
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2013-11-07F. Colas, F. Pomerleau, M. Liu et al. won Best RoboCup paper award at IROS
2013-09-26Dr. Margarita Chli appointed Chancellor's Fellow and Lecturer at School of Informatics of the University of Edinburgh
2013-09-25Michael Neunert received Johann Puch Innovation Award 2012 for Master Thesis
2013-08-29Dr. M. Liu and Prof. R. Siegwart won the "Best Paper in Information" at ICIA 2013!
2013-08-15Dr. Ming Liu appointed Assistant Professor at the The Hong Kong University of Science and Technology
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2012-03-27Talk of Prof. Kazuya Yoshida on "Mobile Robots for Exploration of Extreme and Uncertain Environments in Terrestrial and Planetary Applications"
Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Established leaders in the field are the SIFT and SURF algorithms which exhibit great performance under a variety of image transformations, with SURF in particular considered as the most computationally efficient amongst the high-performance methods to date.
We propose BRISK, a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK's adaptive, high quality performance as in state-of-the-art algorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.
Watch BRISK in action.
ICCV 2011 Paper
Stefan Leutenegger, Margarita Chli and Roland Siegwart, BRISK: Binary Robust Invariant Scalable Keypoints, to appear in Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2011.
Open-source package containing windows and linux libraries, a demo application, and a Matlab mex interface:
download brisk.zip (16 MB)
(v0.1 December 10th 2011)
Provided under the terms and conditions of the BSD license.
Please contact email@example.com, if you have related questions or suggestions.