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Dynamic texture recognition using time-causal spatio-temporal scale-space filters
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Computational Brain Science Lab)ORCID iD: 0000-0003-0011-6444
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Computational Brain Science Lab)ORCID iD: 0000-0002-9081-2170
2017 (English)In: Scale Space and Variational Methods in Computer Vision, Springer, 2017, Vol. 10302, 16-28 p.Conference paper, Published paper (Refereed)
Abstract [en]

This work presents an evaluation of using time-causal scale-space filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatiotemporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain. We evaluate one member in this family, constituting a joint binary histogram, on two widely used dynamic texture databases. The experimental evaluation shows competitive performance compared to previous methods for dynamic texture recognition, especially on the more complex DynTex database. These results support the descriptive power of time-causal spatio-temporal scale-space filters as primitives for video analysis.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10302, 16-28 p.
Series
Springer Lecture Notes in Computer Science, 10302
Keyword [en]
dynamic texture, receptive field, spatio-temporal, time-causal, time-recursive, receptive field histogram, spatio-temporal descriptor, video descriptor, scale space, recognition, video analysis, computer vision
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-202697DOI: 10.1007/978-3-319-58771-4_2OAI: oai:DiVA.org:kth-202697DiVA: diva2:1094826
Conference
SSVM 2017: 6th International Conference on Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, June 4-8, 2017
Projects
Scale-space theory for invariant and covariant visual receptive fieldsTime-causal receptive fields for computer vision and modelling of biological vision
Funder
Swedish Research Council, 2014-4083Stiftelsen Olle Engkvist Byggmästare, 2015/465
Note

QC 20170512

Available from: 2017-05-11 Created: 2017-05-11 Last updated: 2017-06-13

Open Access in DiVA

DTRecognSpatioTemporalScSpFilters_JanssonLindeberg_SSVM2017(5900 kB)16 downloads
File information
File name FULLTEXT01.pdfFile size 5900 kBChecksum SHA-512
2a543e9abb130113fb34fd9c97f40425898cb0cf3803923f1a567851d01b394d094193b7862bc2e72497a22d5d1641f15d5898ced44f550007f1395cf05f7c78
Type fulltextMimetype application/pdf

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Jansson, YlvaLindeberg, Tony
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • 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