Detecting motion from noisy scenes using Genetic Programming

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

@InProceedings{Pinto:2009:IVCNZ,

• title = "Detecting motion from noisy scenes using Genetic Programming",
• author = "Brian Pinto and Andy Song",
• year = "2009",
• pages = "322--327",
• booktitle = "Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09",
• month = "23-25 " # nov,
• address = "Wellington",
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-4697-1",
• ISSN = "2151-2205",
• DOI = "doi:10.1109/IVCNZ.2009.5378389",
• abstract = "A machine learning approach is presented in this study to automatically construct motion detection programs. These programs are generated by Genetic Programming (GP), an evolutionary algorithm. They detect motion of interest from noisy data when there is no prior knowledge of the noise. Programs can also be trained with noisy data to handle noise of higher levels. Furthermore, these auto-generated programs can handle unseen variations in the scene such as different weather conditions and even camera movements.",
• notes = "Also known as \cite{5378389}",

}

Genetic Programming entries for Brian Pinto Andy Song

Citations