Affiliation:
1. North China University of Technology
Abstract
Directed at the defects of time-consuming feature points extracting and out-of-sync between matching feature points and processing video frames in the original SURF (Speeded Up Robust Features) algorithm in mobile pattern recognition applications. For these shortcomings, this paper proposes an improved SURF algorithm. The algorithm uses buffer mechanism. An adaptation threshold is used when extracting feature points. Experimental results show that using the improved SURF algorithm in mobile applications has achieved the purpose of real-time processing. It has certain values in both theory and practice.
Publisher
Trans Tech Publications, Ltd.
Reference7 articles.
1. Krystian Mikolajczyk, Cordelia Schmid. Scale & affine in-variant interest point detectors[J]. International Journal of Computer Vision, 2004, 60: 63-86.
2. Jim Mutch, David G, Lowe. Object class recognition and localization using sparse features with limited receptive fields[J]. International Journal of Computer Vision, 2008, 80(1): 45-57.
3. Leng Xuefei, Liu Jianye, Xiong Zhi. Navigation real-time image matching algorithm based on the branch feature points [J]. AAS, 2007, 33(7): 678-682.
4. Qiao Yongjun, Xie Xiaofang, Li Dedong etc. Research of the block acceleration method in SURF feature matching[J]. Laser and Infrared. 2011. 6.
5. D.G. Lowe, Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision , 2004, 60(2): 91–110.
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1 articles.
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1. Fast Robust Image Feature Matching Algorithm Improvement and Optimization;Proceedings of the 2nd International Conference on Vision, Image and Signal Processing;2018-08-27