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Published Articles >> Table of Contents >> Abstract
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03)
p. 151
Associative Memory Based on Ratio Learning for Real Time Skin Color Detection
Ming-Jung Seow, Old Dominion University, Norfolk, VA
Vijayan K. Asari, Old Dominion University, Norfolk, VA
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AIPR.2003.1284264
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| Abstract |
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A novel approach for skin color modelling using ratio
rule learning algorithm is proposed in this paper. The
learning algorithm is applied to a real time skin color
detection application. The neural network learn, based on
the degree of similarity between the relative magnitudes
of the output of each neuron with respect to that of all
other neurons. The activation/threshold function of the
network is determined by the statistical characteristic of
the input patterns. Theoretical analysis has shown that
the network is able to learn and recall the trained
patterns without much problem. It is shown
mathematically that the network system is stable and
converges in all circumstances for the trained patterns.
The network utilizes the ratio-learning algorithm for
modeling the characteristic of skin color in the RGB
space as a linear attractor. The skin color will converge
to a line of attraction. The new technique is applied to
images captured by a surveillance camera and it is
observed that the skin color model is capable of
processing 420x315 resolution images of 24-bit color at
30 frames per second ibn a dual Xeon 2.2 Ghz CPU
workstation running Windows 2000.
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 Additional Information
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Citation:
Ming-Jung Seow, Vijayan K. Asari,
"Associative Memory Based on Ratio Learning for Real Time Skin Color Detection,"
aipr,
p. 151,
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03),
2003
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