Genetic programming applied to image discrimination

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Tackett:1997:HEC,
  author =       "Walter Alden Tackett and K. Govinda Char",
  title =        "Genetic programming applied to image discrimination",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and David B. Fogel and 
                 Zbigniew Michalewicz",
  chapter =      "section G8.2",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  URL =          "http://www.crcnetbase.com/isbn/9780750308953",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
  size =         "10 pages",
  abstract =     "Automatic target recognition (ATR) involves the
                 determination of objects in natural scenes in different
                 weather conditions and in the presence of various
                 contaminants. This high degree of variability requires
                 a flexible system control capable of adapting to the
                 changing conditions. There is no single set of adaptive
                 algorithms that would give consistent, reliable results
                 when subject to the full variety of target conditions.
                 Although genetic programming (GP) has been successfully
                 applied to a wide variety of problems its performance
                 in scaling up to real-world situations needs to be
                 addressed. In this case study we present the simulation
                 results of applying GP to ATR through the development
                 of a processing tree for classification of features
                 extracted from images: measurements from a set of input
                 nodes are weighted and combined through linear and
                 nonlinear operations to form an output response. No
                 constraints are placed upon size, shape, or order of
                 processing within the network. This network is used to
                 classify feature vectors extracted from infra-red
                 imagery into target/nontarget categories using a
                 database of 2000 training samples. Performance is
                 tested against a separate database of 7000 samples.
                 This represents a significant scaling up from the
                 problems to which GP has been applied to date. Two
                 experiments are performed: in the first set, we input
                 classical statistical image features and minimize
                 misclassification of target and non-target samples. In
                 the second set of experiments, GP is allowed to form
                 its own feature set from primitive intensity
                 measurements. For purposes of comparison, the same
                 training and test sets are used to train two other
                 adaptive classifier systems, the binary tree classifier
                 and the multilayer perceptron/backpropagation neural
                 network. The GP network achieves higher performance
                 with reduced computational requirements. The
                 contributions of GP building blocks, or subtrees, to
                 the performance of generated trees are examined.",
}

Genetic Programming entries for Walter Alden Tackett K Govinda Char

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