Parisian camera placement for vision metrology

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@Article{Dunn20061209,
  author =       "Enrique Dunn and Gustavo Olague and Evelyne Lutton",
  title =        "Parisian camera placement for vision metrology",
  journal =      "Pattern Recognition Letters",
  volume =       "27",
  number =       "11",
  pages =        "1209--1219",
  year =         "2006",
  note =         "Evolutionary Computer Vision and Image Understanding",
  ISSN =         "0167-8655",
  DOI =          "DOI:10.1016/j.patrec.2005.07.019",
  URL =          "http://www.sciencedirect.com/science/article/B6V15-4HX477K-2/2/e82b5b25f9a7a82607ac4b30c9fb9c45",
  keywords =     "genetic algorithms, genetic programming, Camera
                 placement, Accurate 3D reconstruction, Photogrammetric
                 network design, Evolutionary computation, Parisian
                 approach",
  abstract =     "This paper presents a novel camera network design
                 methodology based on the Parisian evolutionary
                 computation approach. This methodology proposes to
                 partition the original problem into a set of
                 homogeneous elements, whose individual contribution to
                 the problem solution can be evaluated separately. A
                 population comprised of these homogeneous elements is
                 evolved with the goal of creating a single solution by
                 a process of aggregation. The goal of the Parisian
                 evolutionary process is to locally build better
                 individuals that jointly form better global solutions.
                 The implementation of the proposed approach requires
                 addressing aspects such as problem decomposition and
                 representation, local and global fitness integration,
                 as well as diversity preservation mechanisms. The
                 benefit of applying the Parisian approach to our camera
                 placement problem is a substantial reduction in
                 computational effort expended in the evolutionary
                 optimization process. Moreover, experimental results
                 coincide with previous state of the art photogrammetric
                 network design methodologies, while incurring in only a
                 fraction of the computational cost.",
}

Genetic Programming entries for Enrique Dunn Gustavo Olague Evelyne Lutton

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