kth.sePublications KTH
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Scale selection
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). (Computational Brain Science Lab)ORCID iD: 0000-0002-9081-2170
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-267559DOI: 10.1007/978-3-030-03243-2_242-1OAI: oai:DiVA.org:kth-267559DiVA, id: diva2:1392833
Note

Part of ISBN 978-3-030-03243-2

QC 20231024

Available from: 2020-02-13 Created: 2020-02-13 Last updated: 2025-02-01Bibliographically approved

Open Access in DiVA

fulltext(4658 kB)590 downloads
File information
File name FULLTEXT03.pdfFile size 4658 kBChecksum SHA-512
7ca796ffae89fc9428123817be0937c867638e46ddce2fcc3e90c330c7b39ef929f4b4da86734bb9ed5ed50dbdc5fe894d8bd016835699234338a75e08b1fd10
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Lindeberg, Tony

Search in DiVA

By author/editor
Lindeberg, Tony
By organisation
Computational Science and Technology (CST)
Computer graphics and computer visionMathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 634 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 4666 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf