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科技导报
|研究论文
2011
, 29
(17) :
58
-64
基于模糊-神经网络的肤色像素检测算法
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李怀颖
作者信息
凯里学院计算机与信息科学学院,贵州 凯里 556011
A Skin Pixel Detection Algorithm Based on Fuzzy-Neural Network
LI Huaiying
Affiliations
Institute of Computer Science and Information, Kaili Univercity, Kaili 556011, Guizhou Province, China
出版时间: 2011-06-18
doi: 10.3981/j.issn.1000-7857.2011.17.008
文章导航
肤色像素检测技术是成人图像识别、人脸识别等与人体相关的图像识别系统的基础和重要组成部分。为了提高肤色像素检测的准确度，本文提出一种模糊理论与FP神经网络（Forward Propagation Neural Network）相结合的肤色像素检测算法。算法首先通过模糊理论和直觉模糊理论提取待识别像素的颜色特征，构成特征向量，其中包括像素对常见肤色像素颜色值的隶属度和犹豫度，为完整的表达肤色像素的特征，再加入粗糙度特征进行补充；然后训练出FP神经网络，对所提取的特征向量进行肤色像素与非肤色像素的分类。实验证明，该算法能够提高肤色像素检测的准确度，可以有效地应用在有关人体的识别系统中。
肤色像素检测
/
模糊
/
直觉模糊
/
FP神经网络
Skin pixel detection technique is a basic and important part of image recognition system related to human being, such as adult image recognition, face recognition, etc. In order to improve the precision of skin pixel detection technique, a skin pixel detection algorithm that combines fuzzy theory with Forward Propagation(FP) Neural Network is proposed. The algorithm composes a feature vector based on extracted color features of the pixel through fuzzy theory and intuitionistic fuzzy theory. The feature vector includes membership and hesitancy degree of the pixel to common skin pixels. Roughness is a supplementary feature in the feature vector in order to completely express the feature of skin pixel. Then the algorithm is used to train a FP neural network and the feature vectors are classified into skin pixels and non-skin pixels. Experiment shows that the algorithm could improve the precision of skin detection and be effectively used in recognition systems related to human being.
skin pixel detection
/
fuzzy
/
intuitionistic fuzzy
/
FP Neural Network
李怀颖.
基于模糊-神经网络的肤色像素检测算法.
科技导报,
2011
, 29
(17)
: 58
-64
.
DOI: 10.3981/j.issn.1000-7857.2011.17.008
LI Huaiying.
A Skin Pixel Detection Algorithm Based on Fuzzy-Neural Network[J].
Science & Technology Review ,
2011
, 29
(17)
: 58
-64
.
DOI: 10.3981/j.issn.1000-7857.2011.17.008
2011年第29卷第17期
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文章信息
doi: 10.3981/j.issn.1000-7857.2011.17.008
接收时间：2011-02-01
首发时间：2011-06-18
出版时间：2011-06-18
收稿日期：2011-02-01
修回日期：2011-05-26
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2011.17.008
引用本文
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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