Optimally Rotation-Equivariant Directional Derivative
Kernels
Published in:
7th Int'l Conf Computer Analysis of Images and Patterns,
Kiel, Germany. September 10-12, 1997.
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.
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