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Unbounded Sparse Census Transform using Genetic Algorithm
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7934-6917
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5832-5452
2019 (English)In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), IEEE , 2019, p. 1616-1625Conference paper, Published paper (Refereed)
Abstract [en]

The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

Place, publisher, year, edition, pages
IEEE , 2019. p. 1616-1625
Series
IEEE Winter Conference on Applications of Computer Vision, ISSN 2472-6737
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-44332DOI: 10.1109/WACV.2019.00177ISI: 000469423400170ISBN: 978-1-7281-1975-5 (print)OAI: oai:DiVA.org:mdh-44332DiVA, id: diva2:1327850
Conference
19th IEEE Winter Conference on Applications of Computer Vision (WACV), JAN 07-11, 2019, Waikoloa Village, HI
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-06-20Bibliographically approved

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Ahlberg, CarlLeon, MiguelEkstrand, FredrikEkström, Mikael

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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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