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.
This work was supported by ARO Grant DAAH04-96-1-0007, DARPA Grant N00014-92-J-1647, NSF Grant SBR89-20230, and NSF CAREER Grant MIP-9796040 to EPS.
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© 1997 Springer-Verlag Berlin Heidelberg
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Farid, H., Simoncelli, E.P. (1997). Optimally rotation-equivariant directional derivative kernels. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_119
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DOI: https://doi.org/10.1007/3-540-63460-6_119
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