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Method of item recognition based on SIFT and SURF†

Published online by Cambridge University Press:  04 September 2014

WENYU CHEN ,
WENZHI XIE  and
RU ZENG
WENYU CHEN
Affiliation:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan Chengdu 610054, P.R.China Email: [email protected]; [email protected]; [email protected]
WENZHI XIE
Affiliation:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan Chengdu 610054, P.R.China Email: [email protected]; [email protected]; [email protected]
RU ZENG
Affiliation:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan Chengdu 610054, P.R.China Email: [email protected]; [email protected]; [email protected]
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Abstract

Item recognition has become a hotspot in the field of computer vision research. SIFT has the advantage of requiring a low amount of information, a fast running speed and high precision, but it requires large data calculations and thus takes a long time to perform the item recognition. In this paper we propose a method of item recognition based on SIFT and SURF that provides a new way to solve the problem of item recognition, and has both feasibility and availability. This technique currently ignores colour information when dealing with colour images, but the evaluation method is capable of taking colour quality characteristics into account so it should be possible to improve the algorithm in the future. Experimental results show that this system of item recognition based on the SURF algorithm gives better matching recognition, is faster and has greater robustness.

Type
Paper
Information
Mathematical Structures in Computer Science , Volume 24 , Special Issue 5: Mathematical Method in Intelligent Computing and Automation , October 2014 , e240506
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

†

This work was supported by the Chinese National Science Foundation Grant Number 61073120

References

Bay, H., Ess, A., Tuytelaars, T. and Gool, L. V. (2008) Speeded-Up Robust Features (SURF) – preprint.Google Scholar
Bay, H., Tuytelaars, T. and Gool, L. V. (2006) SURF: speeded up robust features. Proceedings of the 9th European Conference on Computer Vision 404–417.Google Scholar
Brown, J. G. (1992) A survey of image registration techniques. ACM Computing Surveys 24 (4)325–376.Google Scholar
Delponte, E., Isgrò, F., Odone, F. and Verri, A. (2006) SVD-matching using SIFT features. Graphical Models 68 (5)415–431.CrossRefGoogle Scholar
Fischler, M. A. and Bolles, R. C. (2010) Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24 (6)381–395.Google Scholar
Grabner, M., Grabner, H. and Bischof, H. (2010) Fast approximated SIFT. Proceedings of the Asian Conference on Computer Vision 1 918–927.Google Scholar
Ke, Y. and Sukthankar, R. (2004) PCA-SIFT: a more distinctive representation for local image descriptors. Computer Vision and Pattern Recognition, 2004. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 511–517.Google Scholar
Lowe, D. G. (1999) Object Recognition from Local Scale-Invariant Features. Computer Vision 1999: Proceedings of the International Conference on Computer Vision 1150–1157.Google Scholar
Niu, L.-P., Mao, S.-Y. and Chen, W. (2010) Image registration based on Hausdorff distance. Journal of Electronics and Information Technology 36 (11)2247–2250.Google Scholar