Design and Implementation of an Accelerated Gabor Filter Bank Using Parallel Hardware

  • Nikolaus Voß
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2147)


In computer vision, images are often preprocessed by the so-called Gabor transform. Using a Gabor filter bank, an image can be decomposed into orientational components lying in a specified frequency range. This biologically motivated decomposition simplifies higher level image processing like extraction of contours or pattern recognition. However, the IEEE floating-point implementation of this filter is too slow for real-time image-processing, especially if mobile applications with limited resources are targeted. This paper describes how this can be overcome by a hardware-implementation of the filter algorithm.

The actual implementation is preceded by an analysis of the algorithm analyzing the effects of reduced-accuracy calculus and the possibility of parallelizing the process. The target device is a Xilinx Virtex FPGA which resides on a PCI rapid-prototyping board.


Gabor Filter High Dynamic Range Virtex FPGA Gabor Filter Bank Orientational Component 
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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  1. 1.
    B.M. Baas. A 9.5 mw 330 μs 1024-point fft processor. Technical report, Department of Electrical Engineering, Stanford University, 1995.Google Scholar
  2. 2.
    Drey Inc. Jaguar 256 Interface Specification.Google Scholar
  3. 3.
    P. Duhamel and M. Vetterli. Fast fourier transforms: A tutorial review and a state of the art. Signal Processing, (19):259–299, 1990.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    D. Gabor. Theory of communication. Journal of the Institute for Electrical Engineers, 93:429–439, 1946.Google Scholar
  5. 5.
    J.P. Jones and L.A. Palmer. The two-dimensional spectral structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58(6):1187–1211, December 1987.Google Scholar
  6. 6.
    K. Konstantinides and J. R. Rasure. The khoros software development environment for image and signal processing. IEEE Transactions on Image Processing, 3(3):243–52, 1994.CrossRefGoogle Scholar
  7. 7.
    H. J. Nussbaumer. Fast Fourier Transform and Convolution Algorithms. Springer Verlag, Berlin, 1981.zbMATHGoogle Scholar
  8. 8.
    Ralph Trapp. Stereoskopische Korrespondenzbestimmung mit impliziter Detektion von Okklusionen. Ph.d. thesis, Universität-GH Paderborn, 1998.Google Scholar
  9. 9.
    Matthias Wosnitza. A high precision 1024-point fft processor for 2d convolution. In International Solid-State Circuits Conference (ISSCC), San Francisco (CA), feb 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Nikolaus Voß
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.Department of Computer Science,IMA LabUniversity of HamburgHamburgGermany

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