• DocumentCode
    3185512
  • Title

    Automated Registration Evaluation System (ARES)

  • Author

    Lewis, Derek ; Bergeron, Stuart ; Kim, Melissa ; Doucette, Peter

  • Author_Institution
    Contractor for the Nat. Geospatial-Intell. Agency (NGA), Reston
  • fYear
    2007
  • fDate
    10-12 Oct. 2007
  • Firstpage
    51
  • Lastpage
    56
  • Abstract
    This paper describes the automated registration evaluation system (ARES). The goal of the ARES is to dramatically reduce the cost of evaluating different automated data (raster and/or vector) registration technologies. Under the first year of the ARES project, five different automated registration methods were selected for evaluation. These methods represented the state-of-the-art in automated image-to-image registration and image-to-3D feature registration. During the first year of testing, over two terabytes of commercial imagery and GIS feature (vector) data were acquired. ARES performed over 4000 individual registration test cases among the five different automated registration methods. ARES was able to ascertain the operating conditions and performance of each of these methods against a wide range of user requirements. Results from ARES suggest that automated image-to-image matching for near-nadir satellite imagery (with good initial approximations) is essentially a solved research problem, whereas feature-to-image matching is not.
  • Keywords
    image matching; image registration; automated image-to-image matching; automated image-to-image registration; automated registration evaluation system; automated registration methods; image-to-3D feature registration; near-nadir satellite imagery; Automatic testing; Costs; Geographic Information Systems; Least squares approximation; Least squares methods; Performance evaluation; Satellites; Software algorithms; Software testing; System testing; GIS features; automated registration; evaluation; imagery;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applied Imagery Pattern Recognition Workshop, 2007. AIPR 2007. 36th IEEE
  • Conference_Location
    Washington, DC
  • ISSN
    1550-5219
  • Print_ISBN
    978-0-7695-3066-6
  • Type

    conf

  • DOI
    10.1109/AIPR.2007.14
  • Filename
    4476123