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Texture Classification by High Order Symmetry Derviatives of Gaussians
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
2003 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

In this master thesis we propose high order symmetry derivatives of gaussians for texture classification. The symmetry derivative approach is applied to a multiresolutional pyramid structure, which finally results in a more dimensional feature space represented by high order complex moments. For visualization of results the features are presented to a image classification and segmentation algorithm using multidimensional clustering and orientation adaptive boundary refinement. Test images are generated to validate the functionality of symmetry derivatives for textures with multiple orientations and in this context we propose an extension of double angle representati onto visualize multiple local orientations. Further experiments with real texture images are carried out to improve the quality of the feature space byusing different methods for preprocessing and feature space dimensionality reduction.

Place, publisher, year, edition, pages
2003. , p. 67
National Category
Computer Systems Computer Engineering
Identifiers
URN: urn:nbn:se:hh:diva-12002Local ID: U11041OAI: oai:DiVA.org:hh-12002DiVA, id: diva2:367145
Subject / course
Computer Systems Technology
Uppsok
Technology
Available from: 2010-11-09 Created: 2010-11-09 Last updated: 2018-01-12Bibliographically approved

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fulltext(5417 kB)153 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
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