mdh.sePublications
Change search
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
Intensity normalization of sidescan sonar imagery
Departamento de Eletrónica, Telecomunicações e Informática (DETI), Universidade de Aveiro, Aveiro, Portugal.
Departamento de Eletrónica, Telecomunicações e Informática (DETI), Universidade de Aveiro, Aveiro, Portugal.
Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM), Universidad Politécnica de Madrid, Spain.
Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM), Universidad Politécnica de Madrid, Spain.
Show others and affiliations
2016 (English)In: 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, 2016, 7820967Conference paper, (Refereed)
Abstract [en]

Sonar imaging is currently the exemplary choice used in underwater imaging. However, since sound signals are absorbed by water, an image acquired by a sonar will have gradient illumination; thus, underwater maps will be difficult to process. In this work, we investigated this phenomenon with the objective to propose methods to normalize the images with regard to illumination. We propose to use MIxed exponential Regression Analysis (MIRA) estimated from each image that requires normalization. Two sidescan sonars have been used to capture the seabed in Lake Vattern in Sweden in two opposite directions west-east and east-west; hence, the task is extremely difficult due to differences in the acoustic shadows. Using the structural similarity index, we performed similarity analyses between corresponding regions extracted from the sonar images. Results showed that MIRA has superior normalization performance. This work has been carried out as part of the SWARMs project (http://www.swarms.eu/).

Place, publisher, year, edition, pages
2016. 7820967
Keyword [en]
acoustic shadow, dark channel prior, echo decay, exponential regression, illumination normalization, inverse square law, Sidscan sonar, Image analysis, Regression analysis, Sonar, Underwater acoustics, Underwater imaging, Dark channel priors, Image processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-34985DOI: 10.1109/IPTA.2016.7820967ISI: 000393589800019Scopus ID: 2-s2.0-85013223054ISBN: 9781467389105 (print)OAI: oai:DiVA.org:mdh-34985DiVA: diva2:1078700
Conference
6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, 12 December 2016 through 15 December 2016
Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2017-03-16Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Curuklu, Baran
By organisation
Embedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 23 hits
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