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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 7347421, 15 pages
Research Article

Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2Department of Computer & Electrical Engineering and Computer Science, California State University, Bakersfield, CA 93311, USA
3Department of Math and Computer Science, West Virginia State University, Institute, WV 25112, USA
4Intelligent Fusion Technology, Inc., Germantown, MD 20876, USA

Correspondence should be addressed to Wei Li; ude.busc@ilw

Received 16 June 2017; Revised 4 August 2017; Accepted 9 August 2017; Published 25 September 2017

Academic Editor: Ping Feng Pai

Copyright © 2017 Huidong He 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.


The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE). Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.