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A new dynamic cellular learning automata-based skin detector

Abin, A. A ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1007/s00530-009-0165-1
  3. Publisher: 2009
  4. Abstract:
  5. 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. © 2009 Springer-Verlag
  6. Keywords:
  7. Skin map ; Cellular learning automata ; Color images ; False positive rates ; Image processing applications ; Optimum method ; Primary task ; Skin color space ; Skin detection ; Skin model ; Skin probability maps ; Skin-color regions ; Statistical properties ; Texture analysis ; Texture analyzers ; Texture features ; Texture information ; True positive rates ; Automata theory ; Cellular automata ; Color ; Color image processing ; Detectors ; Education ; Image enhancement ; Imaging systems ; Learning algorithms ; Probability density function ; Robots ; Textures ; Translation (languages) ; Skin
  8. Source: Multimedia Systems ; Volume 15, Issue 5 , 2009 , Pages 309-323 ; 09424962 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s00530-009-0165-1