Scale-invariant corner keypoints
2014 (English)Conference paper (Refereed)
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
IEEE conference proceedings, 2014. 5741-5745 p.
corner detection, edge detection, image matching, Keypoint detection, scale-space, Algorithms, Computer vision, Fire fighting equipment, Image processing, Binary robust invariant scalable keypoints (BRISK), Computer vision applications, Feature description, Oriented fast and rotated brief (ORB), Scale Invariant Features Transform (SIFT), Scale spaces
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-174816DOI: 10.1109/ICIP.2014.7026161ScopusID: 2-s2.0-84949927695ISBN: 9781479957514OAI: oai:DiVA.org:kth-174816DiVA: diva2:881109
2014 IEEE International Conference on Image Processing, ICIP 2014
QC 201512092015-12-092015-10-072015-12-09Bibliographically approved