Efficient low-level vision program design using Sub-machine-code Genetic Programming

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

@InProceedings{oai:CiteSeerPSU:539182,

• author = "Giovanni Adorni and Stefano Cagnoni and Monica Mordonini",
• title = "Efficient low-level vision program design using Sub-machine-code Genetic Programming",
• booktitle = "AIIA 2002, Workshop sulla Percezione e Visione nelle Macchine",
• year = "2002",
• editor = "Marco Gori",
• address = "Siena, Italy",
• month = "10-13 " # sep,
• keywords = "genetic algorithms, genetic programming",
• citeseer-isreferencedby = "oai:CiteSeerPSU:127480; oai:CiteSeerPSU:236266; oai:CiteSeerPSU:154480; oai:CiteSeerPSU:124779; oai:CiteSeerPSU:98673; oai:CiteSeerPSU:519310; oai:CiteSeerPSU:493558; oai:CiteSeerPSU:151056; oai:CiteSeerPSU:128026; oai:CiteSeerPSU:195245; oai:CiteSeerPSU:154216; oai:CiteSeerPSU:198970; oai:CiteSeerPSU:98326; oai:CiteSeerPSU:197064; oai:CiteSeerPSU:88615; oai:CiteSeerPSU:200149; oai:CiteSeerPSU:299112",
• citeseer-references = "oai:CiteSeerPSU:70349; oai:CiteSeerPSU:329358",
• annote = "The Pennsylvania State University CiteSeer Archives",
• description = "Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs.",
• language = "en",
• oai = "oai:CiteSeerPSU:539182",
• rights = "unrestricted",
• URL = "http://www-dii.ing.unisi.it/aiia2002/paper/PERCEVISIO/adorni-aiia02.pdf",
• URL = "http://citeseer.ist.psu.edu/539182.html",
• size = "8 pages",
• abstract = "Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs.

  The paper describes an approach to low-level vision algorithm design for real-time applications by means of Sub-machine-code Genetic Programming(SmcGP), a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs. The SmcGPbased design of two processing modules of a license-plate recognition system is taken into consideration as a case study to show the potential of the approach.

  The paper reports results obtained in recognizing the very low-resolution binary patterns that have to be classified in such an application along with preliminary results obtained using SmcGP to design a license-plate extraction algorithm.",

• notes = "http://www-dii.ing.unisi.it/aiia2002/paper.htm",

}

Genetic Programming entries for Giovanni Adorni Stefano Cagnoni Monica Mordonini

Citations