hh.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
Vision-based hazard detection
Department of Computer and Electrical Engineering, OGI School of Science and Engineering.
Department of Electrical and Computer Engineering, Portland State University.
Department of Electrical and Computer Engineering, Portland State University.
Portland, USA.
2006 (English)In: Intelligent Engineering Systems Through Artificial Neural Networks - Volume 16, New York: ASME Press (American Society of Mechanical Engineers) , 2006, 6- p.Conference paper, (Refereed)
Abstract [en]

Advances in sensor and GPS technologies make possible better guidance tools for vehicle/aerial navigation. In the work reported here we look at the detection of hazards, such as aircraft or other objects, on or near the target runway. Our system consists of two modules: 1) regions of interest (ROI) detection, and 2) hazard recognition. One of the harder problems in object recognition is to segment the target object from a cluttered background. In this system we use a “poor man’s” segmentation, by taking advantage of the fact that we have an approximate reference that we can differentiate from. The regions of interest are defined as significant differences between the input image and the reference image. Since this differencing can be complex due to the fact that the images may not be precisely registered, we employed a novel histogram method which is reasonably invariant to spatial transformations for ROI detection.

Place, publisher, year, edition, pages
New York: ASME Press (American Society of Mechanical Engineers) , 2006. 6- p.
Series
ASME Press series on intelligent engineering systems through artificial neural networks, 16
Keyword [en]
GPS, vehicle navigation, aerial navigation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-2177Local ID: 2082/2574OAI: oai:DiVA.org:hh-2177DiVA: diva2:239395
Conference
Artificial Neural Networks, November 2006, St. Louis, MO
Available from: 2008-12-03 Created: 2008-12-03 Last updated: 2010-01-13Bibliographically approved

Open Access in DiVA

fulltext(159 kB)66 downloads
File information
File name FULLTEXT01.pdfFile size 159 kBChecksum SHA-512
ca5ae6aeaa520578c70865d5e8b43df755189309a88486212c9f8eef841ea9b57a8cd9984e974cb6690fde11e4c85fc443368b33664e1f3bc010e0710f1d441c68c86f8217ee1aafdc9bda75e235d994
Type fulltextMimetype application/pdf

Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 66 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 60 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