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    Skin detection using contourlet texture analysis

    , Article 2009 14th International CSI Computer Conference, CSICC 2009, 20 October 2009 through 21 October 2009, Tehran ; 2009 , Pages 367-372 ; 9781424442621 (ISBN) Fotouhi, M ; Rohban, M. H ; Kasaei, S ; Sharif University of Technology
    Abstract
    A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The... 

    Skin segmentation based on cellular learning automata

    , Article 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) Abin, Ahmad Ali ; Fotouhi, M ; Kasaei, S ; Sharif University of Technology
    2008
    Abstract
    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... 

    A new dynamic cellular learning automata-based skin detector

    , Article Multimedia Systems ; Volume 15, Issue 5 , 2009 , Pages 309-323 ; 09424962 (ISSN) Abin, A. A ; Fotouhi, M ; Kasaei, S ; Sharif University of Technology
    2009
    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...