Scale selection
2021 (English) In: Computer Vision / [ed] Katsushi Ikeuchi (Editor-in-Chief), Springer, 2021, 2, p. 1-14Chapter in book (Refereed)
Abstract [en]
The notion of scale selection refers to methods for estimating characteristic scales in image data and for automatically determining locally appropriate scales in a scale-space representation, so as to adapt subsequent processing to the local image structure and compute scale invariant image features and image descriptors.
An essential aspect of the approach is that it allows for a bottom-up determination of inherent scales of features and objects without first recognizing them or delimiting alternatively segmenting them from their surrounding.
Scale selection methods have also been developed from other viewpoints of performing noise suppression and exploring top-down information.
Place, publisher, year, edition, pages Springer, 2021, 2. p. 1-14
Keywords [en]
Automatic scale selection, Scale invariant image features and image descriptors, Scale-space, Feature detection, Scale invariance, Interest point detection, Blob detection, Corner detection, Edge detection, Ridge detection, Frequency estimation, Feature tracking, Image-based matching and recognition, Object recognition
National Category
Computer graphics and computer vision Mathematics
Research subject Computer Science
Identifiers URN: urn:nbn:se:kth:diva-267559 DOI: 10.1007/978-3-030-03243-2_242-1 OAI: oai:DiVA.org:kth-267559 DiVA, id: diva2:1392833
Note Part of ISBN 978-3-030-03243-2
QC 20231024
2020-02-132020-02-132025-02-01 Bibliographically approved