Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Zhang:evowks03,

• author = "Mengjie Zhang and Peter Andreae and Mark Pritchard",
• title = "Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection",
• booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}",
• year = "2003",
• 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",
• volume = "2611",
• series = "LNCS",
• pages = "455--466",
• address = "University of Essex, UK",
• publisher_address = "Berlin",
• month = "14-16 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• email = "mengjie@mcs.vuw.ac.nz",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, applications, object recognition",
• isbn13 = "978-3-540-00976-4",
• DOI = "doi:10.1007/3-540-36605-9_42",
• 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.",
• notes = "EvoWorkshops2003",

}

Genetic Programming entries for Mengjie Zhang Peter Andreae Mark Pritchard

Citations