Pattern Recognition Application of Improved SURF Algorithm in Mobile Phone

Hui Bai Wang; Lu Nan Yang

doi:10.4028/www.scientific.net/AMM.610.471

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 610Pattern Recognition Application of Improved SURF...

Pattern Recognition Application of Improved SURF Algorithm in Mobile Phone

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.

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Periodical:

Applied Mechanics and Materials (Volume 610)

Pages:

471-476

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.610.471

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Online since:

August 2014

Authors:

Hui Bai Wang, Lu Nan Yang*

Keywords:

Feature Matching, Feature Points Extraction, SURF

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* - Corresponding Author

References

[1] Krystian Mikolajczyk, Cordelia Schmid. Scale & affine in-variant interest point detectors[J]. International Journal of Computer Vision, 2004, 60: 63-86.

DOI: 10.1023/b:visi.0000027790.02288.f2

Google Scholar

[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.

DOI: 10.1007/s11263-007-0118-0

Google Scholar

[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.

Google Scholar

[4] Qiao Yongjun, Xie Xiaofang, Li Dedong etc. Research of the block acceleration method in SURF feature matching[J]. Laser and Infrared. 2011. 6.

Google Scholar

[5] D.G. Lowe, Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision , 2004, 60(2): 91–110.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[6] Bay H. SURF: Speeded Up Robust Features[J]. Computer Vision and Image Understanding, San Diego: Academic Press Inc Elsevier Science, 2008, 110(3): 346-359.

DOI: 10.1016/j.cviu.2007.09.014

Google Scholar

[7] Shao Ping, Yang Luming, Zeng Yaorong. Improved algorithm that calculate the rotation Haar type feature's integral image[J]. Computer Technology and Development. 2006(16).

Google Scholar