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Mathematical Problems in Engineering
Volume 2012, Article ID 857210, 7 pages
Research Article

Entropy-Based Maximally Stable Extremal Regions for Robust Feature Detection

1Digital Interactive Media Laboratory, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received 25 August 2012; Accepted 2 October 2012

Academic Editor: Sheng-yong Chen

Copyright © 2012 Huiwen Cai 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.


Maximally stable extremal regions (MSER) is a state-of-the-art method in local feature detection. However, this method is sensitive to blurring because, in blurred images, the intensity values in region boundary will vary more slowly, and this will undermine the stability criterion that the MSER relies on. In this paper, we propose a method to improve MSER, making it more robust to image blurring. To find back the regions missed by MSER in the blurred image, we utilize the fact that the entropy of probability distribution function of intensity values increases rapidly when the local region expands across the boundary, while the entropy in the central part remains small. We use the entropy averaged by the regional area as a measure to reestimate regions missed by MSER. Experiments show that, when dealing with blurred images, the proposed method has better performance than the original MSER, with little extra computational effort.