Optimally rotation-equivariant directional derivative kernels
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.
KeywordsDerivative Operator Fourier Domain Sinc Function Nyquist Rate Optical Flow Algorithm
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