Skin Detection using Contourlet Texture Analysis
Publish place: 14th annual International CSI Computer Conference
Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSICC14_041
تاریخ نمایه سازی: 24 خرداد 1388
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 Markov property of images is addressed in post-processing to join separate neighbor skin detected regions. Comparison of the proposed method with other state-of-the-art methods shows a lower false positive rate with a little decrease in true positive rate
Authors
Mehran Fotouhi
Sharif University of Technology, Computer Engineering Dep., Tehran, Iran
Mohammad H Rohban
Sharif University of Technology, Computer Engineering Dep., Tehran, Iran
Shohreh Kasaei
Senior Member, IEEE