Unsupervised and adaptive Gaussian skin-color model

被引：60

作者：

Bergasa, LM ^{[1
]}

Mazo, M ^{[1
]}

Gardel, A ^{[1
]}

Sotelo, MA ^{[1
]}

Boquete, L ^{[1
]}

机构：

[1] Univ Alcala de Henares, Escuela Politecn, Dept Elect, Alcala De Henares 28805, Madrid, Spain

来源：

IMAGE AND VISION COMPUTING | 2000年 / 18卷 / 12期

关键词：

skin segmentation; color clustering; unsupervised learning; Bayesian methods; Gaussian mixture models;

D O I：

10.1016/S0262-8856(00)00042-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article a segmentation method is described for the face skin of people of any race in real time, in an adaptive and unsupervised way, based on a Gaussian model of the skin color (that will be referred to as Unsupervised and Adaptive Gaussian Skin-Color Model, UAGM). It is initialized by clustering and it is not required that the user introduces any initial parameters. It works with complex color images, with random backgrounds and it is robust to lighting and background changes. The clustering method used, based on the Vector Quantization (VQ) algorithm, is compared to other optimum model selection methods, based on the EM algorithm, using synthetic data. Finally, real results of the proposed method and conclusions are shown. (C) 2000 Elsevier Science B.V. All rights reserved.

引用

页码：987 / 1003

页数：17

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