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Scale-invariant corner keypoints
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (DML,I2lab)
KTH.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (DML,I2lab)
2014 (English)Conference paper (Other academic)
Abstract [en]

Effective and efficient generation of keypoints from images is the first step of many computer vision applications, such as object matching. The last decade presented us with an arms race toward faster and more robust keypoint detection, feature description and matching. This resulted in several new algorithms, for example Scale Invariant Features Transform (SIFT), Speed-up Robust Feature (SURF), Oriented FAST and Rotated BRIEF (ORB) and Binary Robust Invariant Scalable Keypoints (BRISK). The keypoint detection has been improved using various techniques in most of these algorithms. However, in the search for faster computing, the accuracy of the algorithms is decreasing. In this paper, we present SICK (Scale-Invariant Corner Keypoints), which is a novel method for fast keypoint detection. Our experiment results show that SICK is faster to compute and more robust than recent state-of-the-art methods. 

Place, publisher, year, edition, pages
2014.
Keyword [en]
Keypoint detection, image matching, edge detection, corner detection, scale-space
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:umu:diva-111185DOI: 10.1109/ICIP.2014.7026161OAI: oai:DiVA.org:umu-111185DiVA: diva2:867845
Conference
IEEE International Conference on Image Processing, October 27-30, CNIT La Défence, Paris France
Note

IEEE International Conference on Image Processing, October 2014

Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2016-02-23Bibliographically approved

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Li, BoLi, HaiboSöderström, Ulrik
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ReferencesLink to record
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