Abstract
SURF-based algorithms have been proved to be one of the most effective image matching methods. Considering the challenges induced by the poor illumination conditions or local-feature-similar noises, one enhanced SURF-based image matching method(E-SURF) using pre- and post-processing is developed in this work: pre- and post-processing is adopted to enhance the image matching performance in some challenging cases: Median filtering and Histogram linear transformation is adopted as the preprocessing to remove the isolated noises and amplify the illumination contrast, so that more SURF points can be found; After SURF matching, one LBP-based filtering is used to filter the possible false matching points using local texture features. Experimental results on some complicated images show that the proposed method can outperform the existing SIFT and SURF schemes.
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Acknowledgements
This work was partly funded by NSFC (No. 61571297, No. 61371146, No. 61527804, and No. 61521062), 111 Project (B07022), and China National Key Technology R&D Program (No. 2012BAH07B01). We also thank GFocus Technologies Co. Ltd. for their test images supporting.
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Zhang, C., Wu, Y., Liu, N., Zhang, C. (2017). Enhanced SURF-Based Image Matching Using Pre- and Post-processing. In: Yang, X., Zhai, G. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2016. Communications in Computer and Information Science, vol 685. Springer, Singapore. https://doi.org/10.1007/978-981-10-4211-9_9
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