Local image features matching for real-time seabed tracking applications

Real-time seabed tracking applications play an important role in underwater systems. A lot of them use computer vision for servoing, positioning, navigation, odometry and simultaneous localisation and mapping. They are mostly based on local image features, therefore feature detection, description and matching are crucial for their efficient operations. The aim of this study was to investigate the most popular feature detection and description algorithms such as SIFT, SURF, FAST, STAR, HARRIS, ORB, BRISK and FREAK. Additionally, the image correction technique was presented and image enhancement methods were analysed in order to increase efficiency of image features matching. The matching algorithm was based on the homography matrix and random sample consensus technique. The authors' results indicate that the combination of the histogram equalisation technique and ORB detector and descriptor enables real-time seabed tracking with sufficient efficiency.

• Record URL:
  • https://doi.org/10.1080/20464177.2017.1386266
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20464177
• Supplemental Notes:
  • © 2017 Institute of Marine Engineering, Science & Technology. Abstract republished with permission of Taylor & Francis.
• Authors:
  • Żak, Bogdan
  • Hożyń, Stanisław
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 273-282
• Serial:
  • Journal of Marine Engineering & Technology
  • Volume: 16
  • Issue Number: 4
  • Publisher: Taylor & Francis
  • ISSN: 2046-4177
  • Serial URL: http://www.tandfonline.com/tmar20

Subject/Index Terms

• TRT Terms: Computer algorithms; Computer vision; Detection and identification systems; Detection and identification technologies; Histograms; Image analysis; Image processing; Ocean bottom; Random sampling; Real time data processing; Tracking systems; Underwater vehicles
• Subject Areas: Data and Information Technology; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01670745
• Record Type: Publication
• Files: TRIS
• Created Date: May 29 2018 4:03PM