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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
2020 (English)In: Computer Vision: A Reference Guide, Springer, 2020, 2Chapter in book (Other academic)
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, 2020, 2.
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 Vision and Robotics (Autonomous Systems) Mathematics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-267559OAI: oai:DiVA.org:kth-267559DiVA, id: diva2:1392833
Note

QC 20200214

Available from: 2020-02-13 Created: 2020-02-13 Last updated: 2020-02-14Bibliographically approved

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CiteExportLink to record
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Citation style
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