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Dense scale selection over space, time and space-time
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Computational Brain Science Lab)ORCID iD: 0000-0002-9081-2170
2018 (English)In: SIAM Journal on Imaging Sciences, ISSN 1936-4954, E-ISSN 1936-4954Article in journal (Refereed) Accepted
Abstract [en]

Scale selection methods based on local extrema over scale of scale-normalized derivatives have been primarily developed to be applied sparsely --- at image points where the magnitude of ascale-normalized differential expression additionally assumes local extrema over the domain where the data are defined. This paper presents a methodology for performing dense scale selection, so that hypotheses about local characteristic scales in images, temporal signals and video can be computed at every image point and every time moment.

A critical problem when designing mechanisms for dense scale selection is that the scale at which scale-normalized differential entities assume local extrema over scale can be strongly dependent on the local order of the locally dominant differential structure. To address this problem, we propose a methodology where local extrema over scale are detected of a quasi quadrature measure involving scale-space derivatives up to order two and propose two independent mechanisms to reduce the phase dependency of the local scale estimates by: (i) introducing a second layer of post-smoothing prior to the detection of local extrema over scale and (ii) performing local phase compensation based on a model of the phase dependency of the local scale estimates depending on the relative strengths between first- vs. second-order differential structure.

This general methodology is applied over three types of domains: (i) spatial images, (ii) temporal signals and (iii) spatio-temporal video. Experiments show that the proposed methodology leads to intuitively reasonable results with local scale estimates that reflect variations in the characteristic scale of locally dominant structures over space and time.

Place, publisher, year, edition, pages
SIAM , 2018.
Keyword [en]
scale, scale selection, spatial, temporal, spatio-temporal, scale invariance, scale space, feature detection, differential invariant, video analysis, image analysis, computer vision
National Category
Computer Vision and Robotics (Autonomous Systems) Mathematics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-218340DOI: 10.1137/17M114892XOAI: oai:DiVA.org:kth-218340DiVA: diva2:1160646
Projects
Scale-space theory for invariant and covariant visual receptive fields
Funder
Swedish Research Council, 2014-4083
Note

QCR 20171207

Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-01-18

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CiteExportLink to record
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  • apa
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