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Skin segmentation based on cellular learning automata

Abin, Ahmad Ali ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1145/1497185.1497238
  3. Publisher: 2008
  4. Abstract:
  5. In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive rate of about 11.3% on the Compaq skin database. Experimental results show the effectiveness of the proposed algorithm. © 2008 ACM
  6. Keywords:
  7. Cellular learning automata ; Color images ; Color textures ; False positive rates ; Learning automaton ; Novel algorithm ; Skin color ; Skin detection ; Skin model ; Skin probability maps ; Skin segmentation ; Skin-color regions ; Statistical properties ; Texture analysis ; Texture features ; Texture information ; True positive rates ; Automata theory ; Cellular automata ; Color ; Computer science ; Education ; Learning algorithms ; Mobile computing ; Multimedia systems ; Probability density function ; Robots ; Textures ; Translation (languages) ; Skin
  8. Source: 6th International Conference on Advances in Mobile Computing and Multimedia, MoMM2008, Linz, 24 November 2008 through 26 November 2008 ; November , 2008 , Pages 254-259 ; 9781605582696 (ISBN)
  9. URL: https://dl.acm.org/doi/10.1145/1497185.1497238