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.

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2d haar wavelet local extremum of box filter responses gray level 64dimensional 64d feature point descriptor vectors feature points approach realtime capacity 2d gray histogram second image target matching 2dimensional 2d maximum entropy threshold segmentation 2dmets based speededup robust features surf approach

Zengshan Tian,Mu Zhou,Xia Hong,Huining Dong,Mingchun Wang,Kunjie Xu,.Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features. 2014 (),.

Zengshan Tian,Mu Zhou,Xia Hong,Huining Dong,Mingchun Wang,Kunjie Xu,"Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features" 2014

Zengshan Tian,Mu Zhou,Xia Hong,Huining Dong,Mingchun Wang,Kunjie Xu,"Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features"