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Journal of Electrical and Computer Engineering
Volume 2014, Article ID 768519, 12 pages
Research Article

Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features

1Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2Chongqing Laboratory of Material Physics and Information Display, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3Graduate Telecommunications and Networking Program, University of Pittsburgh, Pittsburgh, PA 15260, USA

Received 21 April 2014; Revised 2 August 2014; Accepted 11 August 2014; Published 26 August 2014

Academic Editor: Adam Panagos

Copyright © 2014 Mu Zhou 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.


This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform the target matching according to the comparisons of the 64D feature point descriptor vectors. Experimental results show that our proposed approach can effectively enhance the target matching performance, as well as preserving the real-time capacity.