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Object Recognition Using Frequency Domain Blur Invariant Features

  • Ville Ojansivu
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

In this paper, we propose novel blur invariant features for the recognition of objects in images. The features are computed either using the phase-only spectrum or bispectrum of the images and are invariant to centrally symmetric blur, such as linear motion or defocus blur as well as linear illumination changes. The features based on the bispectrum are also invariant to translation, and according to our knowledge they are the only combined blur-translation invariants in the frequency domain. We have compared our features to the blur invariants based on image moments in simulated and real experiments. The results show that our features can recognize blurred images better and, in a practical situation, they are faster to compute using FFT.

Keywords

Object Recognition Discrete Fourier Transform Point Spread Function Motion Blur Moment Invariant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Ville Ojansivu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, PO Box 4500, 90014Finland

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