Evolving cascades of voting feature detectors for vehicle detection in satellite imagery

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Krzysztof Krawiec and Bartosz Kukawka and 
                 Tomasz Maciejewski",
  title =        "Evolving cascades of voting feature detectors for
                 vehicle detection in satellite imagery",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  URL =          "http://www.cs.put.poznan.pl/kkrawiec/pubs/2010CECVehicleDetection.pdf",
  size =         "8 pages",
  abstract =     "We propose an evolutionary method for detection of
                 vehicles in satellite imagery which involves a large
                 number of simple elementary features and multiple
                 detectors trained by genetic programming. The complete
                 detection system is composed of several detectors that
                 are chained into a cascade and successively filter out
                 the negative examples. Each detector is a committee of
                 genetic programming trees that together vote over the
                 decision concerning vehicle presence, and is trained
                 only on the examples classified as positive by the
                 previous cascade node. The individual trees use typical
                 arithmetic transformations to aggregate features
                 selected from a very large collections of Haar-like
                 features derived from the input image. The paper
                 presents detailed description of the proposed algorithm
                 and reports the results of an extensive computational
                 experiment carried out on real-world satellite images.
                 The evolved detection system exhibits competitive
                 sensitivity and relatively low false positive rate for
                 testing images, despite not making use of
                 domain-specific knowledge.",
  DOI =          "doi:10.1109/CEC.2010.5586155",
  notes =        "WCCI 2010. Also known as \cite{5586155}",

Genetic Programming entries for Krzysztof Krawiec Bartosz Kukawka Tomasz Maciejewski