TITLE

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

AUTHOR(S)
Mu Zhou; Xia Hong; Zengshan Tian; Huining Dong; Mingchun Wang; Kunjie Xu
PUB. DATE
January 2014
SOURCE
Journal of Electrical & Computer Engineering;2014, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This paper proposes a 2-dimensional (2D) maximumentropy 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.
ACCESSION #
100519146

 

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