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A new dynamic cellular learning automata-based skin detector

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Abstract

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.

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Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ahmad Ali Abin, Mehran Fotouhi & Shohreh Kasaei

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  1. Ahmad Ali Abin
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  2. Mehran Fotouhi
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  3. Shohreh Kasaei
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Correspondence to Shohreh Kasaei.

Additional information

This work was in part supported by a grant from Iran telecommunication research center (ITRC). We also would like to thank Dr. Beigy for his help on the theory and implementation of CLA.

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Abin, A.A., Fotouhi, M. & Kasaei, S. A new dynamic cellular learning automata-based skin detector. Multimedia Systems 15, 309–323 (2009). https://doi.org/10.1007/s00530-009-0165-1

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