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.
CITATION STYLE
Farid, H., & Simoncelli, E. P. (1997). Optimally rotation-equivariant directional derivative kernels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1296, pp. 207–214). Springer Verlag. https://doi.org/10.1007/3-540-63460-6_119
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