Báo cáo hóa học: " Research Article On the Performance of Kernel Methods for Skin Color Segmentation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article On the Performance of Kernel Methods for Skin Color Segmentation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 856039 13 pages doi 2009 856039 Research Article On the Performance of Kernel Methods for Skin Color Segmentation A. Guerrero-Curieses 1 J. L. Rojo-Alvarez 1 P. Conde-Pardo 2 I. Landesa-Vazquez 2 J. Ramos-Lopez 1 and J. L. Alba-Castro2 1 Departamento de Teoria de la Serial y Comunicaciones Universidad Rey Juan Carlos 28943 Fuenlabrada Spain 2 Departamento de Teoria de la Serial y Comunicaciones Universidad de Vigo 36200 Vigo Spain Correspondence should be addressed to A. Guerrero-Curieses Received 26 September 2008 Revised 23 March 2009 Accepted 7 May 2009 Recommended by . Kuo Human skin detection in color images is a key preprocessing stage in many image processing applications. Though kernel-based methods have been recently pointed out as advantageous for this setting there is still few evidence on their actual superiority. Specifically binary Support Vector Classifier two-class SVM and one-class Novelty Detection SVND have been only tested in some example images or in limited databases. We hypothesize that comparative performance evaluation on a representative application-oriented database will allow us to determine whether proposed kernel methods exhibit significant better performance than conventional skin segmentation methods. Two image databases were acquired for a webcam-based face recognition application under controlled and uncontrolled lighting and background conditions. Three different chromaticity spaces YCbCr CIEL a b and normalized RGB were used to compare kernel methods two-class SVM SVND with conventional algorithms Gaussian Mixture Models and Neural Networks . Our results show that two-class SVM outperforms conventional classifiers and also one-class SVM SVND detectors specially for uncontrolled lighting conditions with an acceptably low complexity. Copyright 2009 A. Guerrero-Curieses et al. This is an open .

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