Evolution of Vehicle Detectors for Infrared Linescan Imagery

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

@InProceedings{roberts:1999:evdIRLI,

• author = "Simon C. Roberts and Daniel Howard",
• title = "Evolution of Vehicle Detectors for Infrared Linescan Imagery",
• booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99",
• year = "1999",
• editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty",
• volume = "1596",
• series = "LNCS",
• pages = "110--125",
• address = "Goteborg, Sweden",
• publisher_address = "Berlin",
• month = "28-29 " # may,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-65837-8",
• DOI = "doi:10.1007/10704703_9",
• abstract = "The paper addresses an important and difficult problem of object recognition in poorly constrained environments and with objects having large variability. This research uses genetic programming (GP) to develop automatic object detectors. The task is to detect vehicles in infrared line scan (IRLS) images gathered by low flying aircraft. This is a difficult task due to the diversity of vehicles and the environments in which they can occur, and because images vary with numerous factors including fly-over, temporal and weather characteristics. A novel multi-stage approach is presented which addresses automatic feature detection, automatic object segregation, rotation invariance and generalisation across diverse objects whilst discriminating from a myriad of potential non-objects. The approach does not require imagery to be pre-processed.",
• notes = "EvoIASP99'99",

}

Genetic Programming entries for Simon C Roberts Daniel Howard

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