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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

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