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)


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.


Derivative Operator Fourier Domain Sinc Function Nyquist Rate Optical Flow Algorithm 
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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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