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Chia-Feng Juang, Hwai-Sheng Perng and Shih-Hsuan Chiu
Department of Electrical Engineering
National Chung Hsing University
Taichung, 402 Taiwan
E-mail: cfjuang@dragon.nchu.edu.tw
Skin color segmentation by a block histogram-based neural fuzzy network is proposed
in this paper. The Hue-Saturation (HS) color model is used. Color information is
represented by a block histogram in an HS space image. Several non-uniform quantization
approaches on HS space are proposed to represent histogram information as accurately
as possible. The neural fuzzy network used is the self-constructing neural fuzzy
inference network (SONFIN). Block histogram information from images under different
environments is used to train SONFIN to make the method as robust as possible. Experiments
on skin color segmentation are performed to verify the performance of the
proposed method. For comparison, three other segmentation methods, including principal
component transformation (PCT), histogram-based skin classifier (HSC), and mixture
of Gaussian classifier (MGC) are applied to the same problem. Comparisons show
that the proposed approach achieves the best segmentation results. In addition, the proposed
non-uniform HS partition approach also improves segmentation performance.
Received September 15, 2005; revised June 30, 2006; accepted August 9, 2006.
Communicated by Chung-Yu Wu.
*A preliminary version has been presented in IEEE & INNS International Joint Conference on Neural Network,
Montreal, Canada, Aug., 2005.