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Stochastic Watershed: A Comparison of Different Seeding Methods
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
2012 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

We study modifications to the novel stochastic watershed method for segmentation of digital images. This is a stochastic version of the original watershed method which is repeatedly realized in order to create a probability density function for the segmentation. The study is primarily done on synthetic images with both same-sized regions and differently sized regions, and at the end we apply our methods on two endothelial cell images of the human cornea. We find that, for same-sized regions, the seeds should be placed in a spaced grid instead of a random uniform distribution in order to yield a more accurate segmentation. When images with differently sized regions are being segmented, the seeds should be placed dependent on the gradient, and by also adding uniform or gaussian noise to the image in every iteration a satisfactory result is obtained.

Place, publisher, year, edition, pages
2012. , 35 p.
Series
TVE, 12024
Keyword [en]
image analysis, watershed, image segmentation
Keyword [sv]
bildanalys, bildsegmentering
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-176639OAI: oai:DiVA.org:uu-176639DiVA: diva2:536271
Educational program
Master Programme in Engineering Physics
Presentation
(Swedish)
Uppsok
Technology
Supervisors
Examiners
Available from: 2012-07-03 Created: 2012-06-21 Last updated: 2012-07-03Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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