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Optimally rotation-equivariant directional derivative kernels

  • Hany Farid
  • Eero P. Simoncelli
Low Level Processing I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)

Abstract

We describe a framework for the design of directional derivative kernels for two-dimensional discrete signals in which we optimize a measure of rotation-equivariance in the Fourier domain. The formulation is applicable to first-order and higher-order derivatives. We design a set of compact, separable, linear-phase derivative kernels of different orders and demonstrate their accuracy.

Keywords

Derivative Operator Fourier Domain Sinc Function Nyquist Rate Optical Flow Algorithm 
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-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Hany Farid
    • 1
  • Eero P. Simoncelli
    • 2
  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.New York UniversityNew YorkUSA

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