Pixel-wise skin colour detection based on flexible neural tree
文献类型：期刊论文
| 作者 | Tao Xu ; Yunhong Wang; Zhaoxiang Zhang
|
| 刊名 | IET Image Processing
 |
| 出版日期 | 2013-12-12 |
| 卷号 | 7期号:8页码:751-761 |
| 关键词 | Skin Computer Vision Image Colour Analysis |
| 英文摘要 | Skin colour detection plays an important role in image processing and computer vision. Selection of a suitable colour space is one key issue. The question that which colour space is most appropriate for pixel-wise skin colour detection is not yet concluded. In this study, a pixel-wise skin colour detection method is proposed based on the flexible neural tree (FNT) without considering the problem of selecting a suitable colour space. A FNT-based skin model is constructed by using large skin data sets which identifies the important components of colour spaces automatically. Experimental results show improved accuracy and false positive rates (FPRs). The structure and parameters of FNT are optimised via genetic programming and particle swarm optimisation algorithms, respectively. In the experiments, nine FNT skin models are constructed and evaluated on features extracted from RGB, YCbCr, HSV and CIE-Lab colour spaces. The Compaq and ECU datasets are used for constructing FNT-based skin model and evaluating its performance compared with other skin detection methods. Without extra processing steps, the authors method achieves state of the art performance in skin pixel classification and better performance in terms of accuracy and FPRs. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13229]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Tao Xu |
| 推荐引用方式 GB/T 7714 | Tao Xu,Yunhong Wang,Zhaoxiang Zhang. Pixel-wise skin colour detection based on flexible neural tree[J]. IET Image Processing,2013,7(8):751-761. |
| APA | Tao Xu,Yunhong Wang,&Zhaoxiang Zhang.(2013).Pixel-wise skin colour detection based on flexible neural tree.IET Image Processing,7(8),751-761. |
| MLA | Tao Xu,et al."Pixel-wise skin colour detection based on flexible neural tree".IET Image Processing 7.8(2013):751-761. |
入库方式： OAI收割
来源：自动化研究所
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