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IT Convergence and Security 2012 pp 529–535Cite as

Feature Reduction and Noise Removal in SURF Framework for Efficient Object Recognition in Images

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 215))

Abstract

Speeded up Robust Features (SURF) is an interest point detector and descriptor which has been popularly used for object recognition. However, in real time object recognition applications, SURF framework can not be used because of its expensive nature. In this paper, a feature reduction process is proposed by using only the most repeatable features for matching. The feature reduction step results in a remarkable computational speed up with little loss of accuracy. A noise-reduction process allows a further increase in matching speed and also reduces the false positive rates. A modified definition of the second-neighbor in the nearest neighbor ratio matching strategy allows matching with increased reliability. The comparative analysis with SURF framework shows that the proposed framework can be useful in applications where the accuracy can be sacrificed to save computational cost.

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Acknowledgments

This research was supported by: (1)The Industrial Strategic technology development program, 10041772, (The Development of an Adaptive Mixed-Reality Space based on Interactive Architecture) funded by the Ministry of Knowledge Economy(MKE, Korea), (2) The MKE(The Ministry of Knowledge Economy), Korea, under IT/SW Creative research program supervised by the “NIPA(National IT Industry Promotion Agency)” (NIPA-2012- H0502-12-1013).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea

    Naveed Ejaz & Sung Wook Baik

  2. College of Business, Honam University, Gwangju, Republic of Korea

    Ran Baik

Authors
  1. Naveed Ejaz
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  2. Ran Baik
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  3. Sung Wook Baik
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Corresponding author

Correspondence to Sung Wook Baik .

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Editors and Affiliations

  1. Convergence Security, Kyoung-gi University, Suwon, Gyeonggi-do, Korea, Republic of (South Korea)

    Kuinam J. Kim

  2. Dept. of Computer Information Engineerin, Sangji University, Wonju-si Gangwon-do, Korea, Republic of (South Korea)

    Kyung-Yong Chung

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© 2013 Springer Science+Business Media Dordrecht

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Cite this paper

Ejaz, N., Baik, R., Baik, S.W. (2013). Feature Reduction and Noise Removal in SURF Framework for Efficient Object Recognition in Images. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_63

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  • DOI: https://doi.org/10.1007/978-94-007-5860-5_63

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-5859-9

  • Online ISBN: 978-94-007-5860-5

  • eBook Packages: EngineeringEngineering (R0)

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