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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 5468208, 12 pages
Research Article

Object Extraction in Cluttered Environments via a P300-Based IFCE

1School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
2Department of Computer & Electrical Engineering and Computer Science, California State University, Bakersfield, CA 93311, USA
3State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, Liaoning 110016, China
4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
5Department of Math and Computer Science, West Virginia State University, 5000 Fairlawn Ave, Institute, WV 25112, USA
6Intelligent Fusion Technology, Inc., Germantown, MD 20876, USA

Correspondence should be addressed to Wei Li

Received 14 December 2016; Revised 3 April 2017; Accepted 24 May 2017; Published 27 June 2017

Academic Editor: Hasan Ayaz

Copyright © 2017 Xiaoqian Mao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.