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Fast anisotropic Gauss filtering.

Jan-Mark Geusebroek1, Arnold W M Smeulders, Joost van de Weijer

  • 1Dept. of Comput. Sci., Univ. of Amsterdam, Netherlands. mark@science.uva.nl

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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We developed a computationally efficient method to decompose anisotropic Gaussian filters. This technique significantly speeds up image processing tasks like edge and ridge detection, making complex analyses more practical.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Mathematics

Background:

  • Anisotropic Gaussian filters are crucial for image analysis but computationally intensive.
  • Efficient decomposition methods are needed to enable real-time applications.

Purpose of the Study:

  • To derive and implement a computationally efficient decomposition of anisotropic Gaussian filters.
  • To demonstrate the performance gains and accuracy of the proposed method.

Main Methods:

  • Decomposition of anisotropic Gaussian into sequential 1-D filters in orthogonal and nonorthogonal directions.
  • Implementation schemes for normal convolution and recursive filtering.
  • Performance evaluation on a standard PC for image filtering tasks.

Related Experiment Videos

Main Results:

  • Achieved over 3x performance gain in image filtering using the proposed method.
  • Recursive implementation filtered a 512x512 image in 40 msec.
  • Demonstrated high spatial and angular accuracy for edge and ridge map calculations.

Conclusions:

  • The proposed anisotropic Gaussian filtering method offers significant computational efficiency.
  • Enables practical applicability of orientation scale-space analysis for various computer vision tasks.
  • Recursive implementation is suitable for feature detection, while normal convolution is advantageous for tracking.