A Soft Computing Approach to Road Classification

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@Article{2000-shanahan,
  author =       "J. Shanahan and B. Thomas and M. Mirmehdi and 
                 T. Martin and N. Campbell and J. Baldwin",
  title =        "A Soft Computing Approach to Road Classification",
  journal =      "Journal of Intelligent and Robotic Systems",
  ISSN =         "0921-0296",
  volume =       "29",
  number =       "4",
  pages =        "349--387",
  month =        dec,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000525",
  pubtype =      "101",
  DOI =          "doi:10.1023/A:1008158907779",
  abstract =     "Current learning approaches to computer vision have
                 mainly focused on low-level image processing and object
                 recognition, while tending to ignore high-level
                 processing such as understanding. Here we propose an
                 approach to object recognition that facilitates the
                 transition from recognition to understanding. The
                 proposed approach embraces the synergistic spirit of
                 soft computing, exploiting the global search powers of
                 genetic programming to determine fuzzy probabilistic
                 models. It begins by segmenting the images into regions
                 using standard image processing approaches, which are
                 subsequently classified using a discovered fuzzy
                 Cartesian granule feature classifier. Understanding is
                 made possible through the transparent and succinct
                 nature of the discovered models. The recognition of
                 roads in images is taken as an illustrative problem in
                 the vision domain. The discovered fuzzy models while
                 providing high levels of accuracy (97per cent), also
                 provide understanding of the problem domain through the
                 transparency of the learnt models. The learning step in
                 the proposed approach is compared with other techniques
                 such as decision trees, naive Bayes and neural networks
                 using a variety of performance criteria such as
                 accuracy, understandability and efficiency.",
  notes =        "Further Information.This paper is not on-line, please
                 contact the author.",
}

Genetic Programming entries for James G Shanahan Barry Thomas M Mirmehdi Trevor P Martin Neill Campbell James F Baldwin

Citations