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

  • Naveed Ejaz
  • Ran Baik
  • Sung Wook Baik
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 215)


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.


Object recognition SURF features Pose estimation Noise reduction Feature matching 



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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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.College of Electronics and Information Engineering, Sejong UniversitySeoulRepublic of Korea
  2. 2.College of Business, Honam UniversityGwangjuRepublic of Korea

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