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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: 2016 Fifteenth Mexican International Conference On Artificial Intelligence (micai): Advances In Artificial Intelligence Título del capítulo: A Comparative Study of the use of a Robust Color Image Segmentation Method
Autores UNAM: VLADISLAV KHARTCHENKO;
Autores externos: Idioma: Año de publicación: 2016Palabras clave:
color segmentation evaluation; CIELAB L*a*b* color space; color metrics; color image segmentation; synthetic color image generation
Resumen:
In this paper, a comparative study of some basic close related color
image segmentation methods is presented. It is focused in the evaluation
of two segmentation methods based on a recently published adaptive color
similarity function making use of: 1) pixel samples of both figure and
background and classifying by maximum similarity, and 2) pixel samples
of only figure and classifying by automatic thresholding thus employing
only half of input information. It is also presented for comparison, the
results of classification using the Euclidean metric of a* and b*
channels rejecting L* in the L*a*b* color space and with the
Euclidian metric of the R, G, and B channels in the RGB color space.
From the results it was obtained that the segmentation technique using
the adaptive color similarity function and classifying by automatic
thresholding (employing only half of the information supplied to the
other methods) had better performance than those implemented in the
L*a*b* and RGB color spaces in all cases of study. The performance
is equivalent to that using pixel sample of both figure and background
and classifying by maximum similarity. The improvement in quality of the
segmentation techniques using the color similarity function is
substantially significant.
