摘要
提出了一种基于自适应阈值的肤色检测算法.相对于固定阈值的直方图检测方法,该算法可以针对不同的图像内容产生相应的最优分割阈值.通过对肤色概率分布直方图(SPDH)的观察分析,可以提取出4点线索来帮助寻找最优阈值,在此基础上训练出一个人工神经网络分类器来确定最优阈值.同时提出了一种图像逻辑运算,可以最大限度地去除混淆背景.提出的算法在寻找最优阈值的过程中无需迭代计算,因此速度快,适合于实时应用.实验结果表明其性能优于广泛采用的固定阈值肤色检测方法.
In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, this method can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram （SPDH） to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A novel image relation operation is also proposed to eliminate confusing backgrounds. The method is fast and thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2006年第9期1674-1680,共7页
Journal of Computer Research and Development
基金
国家"八六三"高技术研究发展计划基金项目(2003AA142140)
