Optimally rotation-equivariant directional derivative kernels

Hany Farid, Eero P. Simoncelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationComputer Analysis of Images and Patterns - 7th International Conference, CAIP 1997, Proceedings
EditorsGerald Sommer, Kostas Daniilidis, Josef Pauli
PublisherSpringer Verlag
Pages207-214
Number of pages8
ISBN (Print)3540634606, 9783540634607
DOIs
StatePublished - 1997
Event7th International Conference on Computer Analysis of Images and Patterns, CAIP 1997 - Kiel, Germany
Duration: Sep 10 1997Sep 12 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1296
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Computer Analysis of Images and Patterns, CAIP 1997
CountryGermany
CityKiel
Period9/10/979/12/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Farid, H., & Simoncelli, E. P. (1997). Optimally rotation-equivariant directional derivative kernels. In G. Sommer, K. Daniilidis, & J. Pauli (Eds.), Computer Analysis of Images and Patterns - 7th International Conference, CAIP 1997, Proceedings (pp. 207-214). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1296). Springer Verlag. https://doi.org/10.1007/3-540-63460-6_119