Finding and Identifying Objects Based on Noisy Data: A Global Optimization Approach - Part 1: Theoretical Approach and Applicability with Deployment Examples; and Part 2 UXO Finding and Discrimination. Results from Field Production: Translation of R\&D work into Field Production Tools UXOMF

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Deschaine:2006:euro,
  author =       "Larry M. Deschaine and Frank D. Francone and 
                 Janos D. Pinter and Melissa McKay and Jeff Warren and 
                 Seth Blanchard",
  title =        "Finding and Identifying Objects Based on Noisy Data: A
                 Global Optimization Approach - Part 1: Theoretical
                 Approach and Applicability with Deployment Examples;
                 and Part 2 UXO Finding and Discrimination. Results from
                 Field Production: Translation of R\&D work into Field
                 Production Tools UXOMF",
  booktitle =    "EURO XXI",
  year =         "2006",
  editor =       "Tuula Kinnunen",
  address =      "Reykjavik, Iceland",
  month =        "2-6 " # jul,
  organisation = "Icelandic Operations Research Society and The
                 Association of European OR Societies",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Euro2006_Deschaine_Finding_Subsurface_Objects_of_Interest_6-29-06_Final.pdf",
  URL =          "https://www.euro-online.org/euro21/display.php?page=treate_abstract&frompage=edit_session_cluster&sessionid=661&paperid=3361",
  abstract =     "Automated object recognition of images or signals is
                 important, to identify items of interest, or anomalies
                 (such as tumours in tissues). In such analyses it is
                 often necessary to deal with noise in the values
                 observed. Such noise complicates automated search
                 procedures, and can affect the solution. In our
                 example, the location, orientation and dimensions of an
                 elliptical object are determined based on noisy data
                 from electromagnetic surveys. We then use a global
                 optimisation approach to find the best function fit.
                 Our results demonstrate the success of this general
                 approach.",
  notes =        "

                 http://www.euro2006.org/

                 ",
}

Genetic Programming entries for Larry M Deschain Frank D Francone Janos D Pinter Melissa C McKay Jeffrey J Warren Seth Blanchard

Citations