Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Zhang:evowks03,
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author = "Mengjie Zhang and Peter Andreae and Mark Pritchard",
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title = "Pixel Statistics and False Alarm Area in Genetic
Programming for Object Detection",
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booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
Evo{MUSART}, Evo{ROB}, Evo{STIM}",
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year = "2003",
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editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and
Juan Jes\'us Romero Cardalda and David W. Corne and
Jens Gottlieb and Agn\`es Guillot and Emma Hart and
Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and
Martin Middendorf",
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volume = "2611",
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series = "LNCS",
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pages = "455--466",
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address = "University of Essex, UK",
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publisher_address = "Berlin",
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month = "14-16 " # apr,
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organisation = "EvoNet",
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publisher = "Springer-Verlag",
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email = "mengjie@mcs.vuw.ac.nz",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation, applications, object recognition",
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isbn13 = "978-3-540-00976-4",
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DOI = "doi:10.1007/3-540-36605-9_42",
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abstract = "This paper describes a domain independent approach to
the use of genetic programming for object detection
problems. Rather than using raw pixels or high level
domain specific features, this approach uses domain
independent statistical features as terminals in
genetic programming. Besides position invariant
statistics such as mean and standard deviation, this
approach also uses position dependent pixel statistics
such as moments and local region statistics as
terminals. Based on an existing fitness function which
uses linear combination of detection rate and false
alarm rate, we introduce a new measure called 'false
alarm area' to the fitness function. In addition to the
standard arithmetic operators, this approach also uses
a conditional operator ifin the function set. This
approach is tested on two object detection problems.
The experiments suggest that position dependent pixel
statistics computed from local (central) regions and
nonlinear condition functions are effective to object
detection problems. Fitness functions with false alarm
area can reflect the smoothness of evolved genetic
programs. This approach works well for the detecting
small regular multiple class objects on a relatively
uncluttered background.",
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notes = "EvoWorkshops2003",
- }
Genetic Programming entries for
Mengjie Zhang
Peter Andreae
Mark Pritchard
Citations