摘要
由于受环境、光照、人种等因素影响,不同图像中的肤色分布并不一样。在复杂背景情况下,采用固定的阈值边界模型进行肤色分割将导致较大的漏检或误检。基于YCbCr颜色空间,在固定阈值边界模型分割的基础上,运用简化的期望最大化(EM)算法计算出针对特定图像的自适应肤色高斯模型;然后综合考虑固定阈值边界模型以及自适应肤色高斯模型在不同颜色区域上划分的准确性,给出最终的肤色分割结果。实验结果表明,该方法相比固定阈值边界模型的分割方法,能同时降低误检率和漏检率,从而提高肤色识别的准确率。
Because of the effects of environment, illumination and ethnicity, skin-color clustering in different images are not the same. As a result, for images with complex backgrounds, a fixed decision boundary skin-color model may lead to high false rejection rate and false detection rate. Based on the segmentation result of a fixed decision boundary skin-color model in YCbCr color space, a simplified Expectation Maximization （EM） algorithm was used to train the adaptive Gaussian model for a specific image. Combining the fixed model and the adaptive Gaussian model could produce the final skin color model for skin color segmentation. The experimental results show that the method can greatly improve skin-color segmentation accuracy, and reduce both false rejection rate and false detection rate.
出处
《计算机应用》
CSCD
北大核心
2010年第10期2698-2701,共4页
journal of Computer Applications
基金
广东省自然科学基金资助项目(9351064101000003)
华南理工大学中央高校基本科研业务费专项资金资助项目(2009ZM0104)
关键词
自适应模型
肤色检测
YCBCR颜色空间
期望最大化算法
贝叶斯决策
adaptive model
skin color detection
YCbCr color space
Expectation Maximization （EM） algorithm
Bayesian decision
