Development of evolution based technology for Image
Recognition Systems
J. Sandvik. The University Of Oslo, (4 November 2005)
Аннотация
A traffic sign detection system in the vehicle can be
of great help for the driver. The number of accidents
can be reduced by 20 percent if the speed limits are
followed. A system that warns the driver about speeding
could therefore save lives if the driver reduces the
speed. This work focus on the colour classification
used in traffic sign detection methods. Existing
methods are compared, and a Genetic Algorithm is used
for optimising parameters used in the existing colour
classification methods. Cartesian Genetic Programming
is used for evolving colour classifiers for traffic
signs, and compared to the existing methods. The
evolved classifier is tested with three different
luminance adjustment algorithms. The results show that
the GA is able to find better parameters than the
reported parameters, and some of the evolved colour
classifiers were better than the existing methods. The
CGP architecture did find better classifiers than the
existing. The luminance adjustment algorithms did not
result in better classification results.
%0 Thesis
%1 Hovedoppgave_Jens-Petter_Sandvik
%A Sandvik, Jens-Petter Skjelvag
%D 2005
%K algorithms, cartesian genetic programming programming,
%T Development of evolution based technology for Image
Recognition Systems
%U http://urn.nb.no/URN:NBN:no-11481
%X A traffic sign detection system in the vehicle can be
of great help for the driver. The number of accidents
can be reduced by 20 percent if the speed limits are
followed. A system that warns the driver about speeding
could therefore save lives if the driver reduces the
speed. This work focus on the colour classification
used in traffic sign detection methods. Existing
methods are compared, and a Genetic Algorithm is used
for optimising parameters used in the existing colour
classification methods. Cartesian Genetic Programming
is used for evolving colour classifiers for traffic
signs, and compared to the existing methods. The
evolved classifier is tested with three different
luminance adjustment algorithms. The results show that
the GA is able to find better parameters than the
reported parameters, and some of the evolved colour
classifiers were better than the existing methods. The
CGP architecture did find better classifiers than the
existing. The luminance adjustment algorithms did not
result in better classification results.
@mastersthesis{Hovedoppgave_Jens-Petter_Sandvik,
abstract = {A traffic sign detection system in the vehicle can be
of great help for the driver. The number of accidents
can be reduced by 20 percent if the speed limits are
followed. A system that warns the driver about speeding
could therefore save lives if the driver reduces the
speed. This work focus on the colour classification
used in traffic sign detection methods. Existing
methods are compared, and a Genetic Algorithm is used
for optimising parameters used in the existing colour
classification methods. Cartesian Genetic Programming
is used for evolving colour classifiers for traffic
signs, and compared to the existing methods. The
evolved classifier is tested with three different
luminance adjustment algorithms. The results show that
the GA is able to find better parameters than the
reported parameters, and some of the evolved colour
classifiers were better than the existing methods. The
CGP architecture did find better classifiers than the
existing. The luminance adjustment algorithms did not
result in better classification results.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Sandvik, Jens-Petter Skjelvag},
bibsource = {OAI-PMH server at wo.uio.no},
biburl = {https://www.bibsonomy.org/bibtex/2de02e26928755ebe5789cd33dad22b0a/brazovayeye},
interhash = {1ea9a32c28dd05fab2d4820663e1df62},
intrahash = {de02e26928755ebe5789cd33dad22b0a},
keywords = {algorithms, cartesian genetic programming programming,},
language = {eng},
month = {4 November},
oai = {oai:digbib.uio.no/32422},
school = {The University Of Oslo},
size = {115 pages},
subject = {VDP:420},
timestamp = {2008-06-19T17:51:01.000+0200},
title = {Development of evolution based technology for Image
Recognition Systems},
url = {http://urn.nb.no/URN:NBN:no-11481},
year = 2005
}