Object Extraction in Cluttered Environments via a P300-Based IFCE. | |
Mao, Xiaoqian; Li, Wei; He, Huidong; Chen, Genshe; Niu, Linwei; Zhou, Huihui; Zeng, Ming; Xian, Bin | |
刊名 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
2017 | |
文献子类 | 期刊论文 |
英文摘要 | One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities. |
URL标识 | 查看原文 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.siat.ac.cn:8080/handle/172644/11911] |
专题 | 深圳先进技术研究院_脑所 |
作者单位 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
推荐引用方式 GB/T 7714 | Mao, Xiaoqian,Li, Wei,He, Huidong,et al. Object Extraction in Cluttered Environments via a P300-Based IFCE.[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2017. |
APA | Mao, Xiaoqian.,Li, Wei.,He, Huidong.,Chen, Genshe.,Niu, Linwei.,...&Xian, Bin.(2017).Object Extraction in Cluttered Environments via a P300-Based IFCE..COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE. |
MLA | Mao, Xiaoqian,et al."Object Extraction in Cluttered Environments via a P300-Based IFCE.".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2017). |
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