- 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/
",
