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JCST

Journal of Current Science and Technology

ISSN 2630-0656 (Online)

Asiatic skin color segmentation using an adaptive algorithm in changing luminance environment

  • Chutisant Kerdvibulvech, Faculty of Information Technology, Rangsit University, Pathum Thani, Thailand , Corresponding author; E-mail: dr-chutisant@hotmail.com

Abstract

         Skin color of Asiatic people is a unique color.  Therefore, Asiatic skin color segmentation is not trivial, particularly in non-static background.  This paper proposes a novel algorithm to segment an area of Asiatic skin color effectively.  By using the on-line adaptation of color probabilities, the algorithm proposes two main benefits: it can cope with luminance changes very well, and also it can be processed in real-time. Bayes’ theorem and Bayesian classifier are employed to compute the probability of skin color of Asiatic people.  Representative results from the experiments are presented to show the efficiency of the proposed system.  The system presented can be further used to develop the real-time vision-based applications in many challenging environments.

Keywords: Asiatic skin; color segmentation; Bayes theorem; Bayesian; adaptive algorithm; luminance

PDF (379.92 KB)

DOI: 10.14456/rjas.2012.4

References

Argyros, A. A. & Lourakis, M. I. A. (2004). Real time Tracking of Multiple Skin-Colored Objects with a Possibly Moving Camera. European Conference on Computer Vision.

Cabrol, A. D., Bonnin, P., Costis, T., Hugel, V., & Blazevic, P. (2005). A New Video Rate Region Color Segmentation and Classication for Sony Legged RoboCup Application. Lecture Notes in Computer Science. RoboCup.

Chai, D. & Ngan, K. N. (1998). Locating facial region of a head-and-shoulders color image. 3rd IEEE International Conference on Automatic Face and Gesture Recognition (FG’98).

Chia-Feng, J. & Shen-Jie, S. (2008). Using self-organizing fuzzy network with support vector learning for face detection in color images. Neurocomputing, 71(16-18), pp. 3409-3420.

Gonzalez, R. & Woods, R. E. (2002). Digital Image Processing. 2nd ed, Prentice Hall Press., ISBN 0-201-18075-8.

Hua, R. C. K., Silva, L. C. D. & Vadakkepat, P., (2002). Detection and Tracking of Faces in Real-Time Environments. International Conference on Imaging Science, Systems, and Technology.

Iraji, M. S. & Yavari, A. (2011). Skin Color Segmentation in Fuzzy YCBCR Color Space with the Mamdani Inference, American Journal of Scientific Research, ISSN 1450-223, X Issue, pp.131-137.

Jack, K. (2004). Video demystified, Elsevier science, 4th edition.

Jebara, T. S. & Pentland, A. (1997). Parameterized structure from motion for 3D adaptive feedback tracking of faces, IEEE Conference on Computer Vision and Pattern Recognition.

Kakumanu, K., Makrogiannis, S., & Bourbakis, N. (2007), A survey of skin color modelling and detection methods. Pattern Recognition 40(3), pp.1106-1122.

Kim, S. H., Kim, N. K., Ahn, S. C., & Kim, H. G. (1998). Object oriented face detection using range and color information. 3rd IEEE International Conference on Automatic Face and Gesture Recognition.

Sigal, L., Sclaroff, S. & Athitsos, V. (2000). Estimation and prediction of evolving color distributions for skin segmentation under varying illumination. IEEE International Conference on Computer Vision and Pattern Recognition. Volume 2, ISBN 0-7695-0662-3, pp. 152-159.

Shi, S., Wang, L., Jin, W., & Zhao, Y. (2007). Color night vision based on color transfer in YUV color space. International Symposium on Photoelectronic Detection and Imaging, pp. 66230B-66230B-12.

Zabulis, X., Baltzakis, H. & Argyros, A.A. (2009). Vision-based Hand Gesture Recognition for Human-Computer Interaction, In "The Universal Access Handbook", Lawrence Erlbaum Associates, Inc. (LEA), Series on "Human Factors and Ergonomics", ISBN: 978-0-8058-6280-5.

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