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Structure Preserving Binary Image Morphing using Delaunay Triangulation
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. (BigData Project Profile)ORCID iD: 0000-0002-4390-411X
2017 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 85, 8-14 p.Article in journal (Refereed) Published
Abstract [en]

Mathematical morphology has been of a great significance to several scientific fields. Dilation, as one of the fundamental operations, has been very much reliant on the common methods based on the set theory and on using specific shaped structuring elements to morph binary blobs. We hypothesised that by performing morphological dilation while exploiting geometry relationship between dot patterns, one can gain some advantages. The Delaunay triangulation was our choice to examine the feasibility of such hypothesis due to its favourable geometric properties. We compared our proposed algorithm to existing methods and it becomes apparent that Delaunay based dilation has the potential to emerge as a powerful tool in preserving objects structure and elucidating the influence of noise. Additionally, defining a structuring element is no longer needed in the proposed method and the dilation is adaptive to the topology of the dot patterns. We assessed the property of object structure preservation by using common measurement metrics. We also demonstrated such property through handwritten digit classification using HOG descriptors extracted from dilated images of different approaches and trained using Support Vector Machines. The confusion matrix shows that our algorithm has the best accuracy estimate in 80% of the cases. In both experiments, our approach shows a consistent improved performance over other methods which advocates for the suitability of the proposed method.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 85, 8-14 p.
Keyword [en]
Binary image; Delaunay triangulation; Dilation; Distance transform; Mathematical morphology; Pattern recognition; Set theory; Structuring element
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems) Media and Communication Technology
Identifiers
URN: urn:nbn:se:bth-13576DOI: 10.1016/j.patrec.2016.11.010ISI: 000390661600002OAI: oai:DiVA.org:bth-13576DiVA: diva2:1055369
Funder
Knowledge Foundation, 20140032
Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2017-01-20Bibliographically approved

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S016786551630335X

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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