Computational Intelligence based construction of a Body Condition Assessment system for cattle

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@InProceedings{Tedin:2013:CIVEMSA,
  author =       "Rafael Tedin and J. A. Becerra and Richard J. Duro and 
                 Fernando {Lopez Pena}",
  title =        "Computational Intelligence based construction of a
                 Body Condition Assessment system for cattle",
  booktitle =    "IEEE International Conference on Computational
                 Intelligence and Virtual Environments for Measurement
                 Systems and Applications (CIVEMSA 2013)",
  year =         "2013",
  month =        "15-17 " # jul,
  pages =        "185--190",
  address =      "Milan",
  keywords =     "genetic algorithms, genetic programming, biology
                 computing, regression analysis, Cows, Image
                 segmentation",
  DOI =          "doi:10.1109/CIVEMSA.2013.6617418",
  size =         "6 pages",
  abstract =     "The objective of this paper is to describe a
                 Computational Intelligence based Automatic Body
                 Conditioning System for cattle we have called Automatic
                 Body Condition Assessment (ABiCA). It is an automatic
                 body condition scoring system for dairy cattle that
                 aims to overcome the flaws of the subjective and time
                 consuming scoring task that is usually carried out by
                 experts. No special set-ups are needed since the system
                 uses pictures taken using normal hand-held cameras.
                 ABiCA is split into two components. A first component
                 for the segmentation of the rear-end shape of a cow
                 from its picture through Active Shape Models Active
                 Shape Models (ASMs) that are evolved using an
                 evolutionary algorithm. The second component is in
                 charge of estimating the Body Condition Score (BCS) of
                 a cow from the shape provided by the ASM. Several
                 classifiers and a symbolic regression function evolved
                 by means of genetic programming techniques are tested
                 for this task. The whole system is tested over a set of
                 images coming from different cattle farms and its
                 goodness provided in terms of the classifications
                 obtained by a set of experts.",
  notes =        "Also known as \cite{6617418}",
}

Genetic Programming entries for Rafael Tedin Jose Antonio Becerra Permuy Richard J Duro Fernandez Fernando Lopez Pena

Citations