SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:DiVA.org:uu-173319"
 

Search: onr:"swepub:oai:DiVA.org:uu-173319" > Coverage segmentati...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist
  • Lindblad, JoakimSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion,,Centre for Image Analysis (author)

Coverage segmentation based on linear unmixing and minimization of perimeter and boundary thickness

  • Article/chapterEnglish2012

Publisher, publication year, extent ...

  • Elsevier BV,2012
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:uu-173319
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-173319URI
  • https://doi.org/10.1016/j.patrec.2011.12.014DOI
  • https://res.slu.se/id/publ/41162URI

Supplementary language notes

  • Language:English
  • Summary in:English &language:-1_t

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • We present a method for coverage segmentation, where the, possibly partial, coverage of each image element by each of the image components is estimated. The method combines intensity information with spatial smoothness criteria. A model for linear unmixing of image intensities is enhanced by introducing two additional conditions: (i) minimization of object perimeter, leading to smooth object boundaries, and (ii) minimization of the thickness of the fuzzy object boundary, and to some extent overall image fuzziness, to respond to a natural assumption that imaged objects are crisp, and that fuzziness is mainly due to the imaging and digitization process. The segmentation is formulated as an optimization problem and solved by the Spectral Projected Gradient method. This fast, deterministic optimization method enables practical applicability of the proposed segmentation method. Evaluation on both synthetic and real images confirms very good performance of the algorithm.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Sladoje, Natasa (author)
  • Uppsala universitetBildanalys och människa-datorinteraktion (creator_code:org_t)
  • Sveriges lantbruksuniversitet

Related titles

  • In:Pattern Recognition Letters: Elsevier BV33:6, s. 728-7380167-86551872-7344

Internet link

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Search outside SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view