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Published October 1995 | public
Book Section - Chapter

Pyramidal Implementation of Deformable Kernels

Abstract

In computer vision and increasingly, in rendering and image processing, it is useful to filter images with continuous rotated and scaled families of filters. For practical implementations, one can think of using a discrete family of filters, and then to interpolate from their outputs to produce the desired filtered version of the image. We propose a multirate implementation of deformable kernels, capable to further reduce the computational weight. The "basis" filters are applied to the different levels of a pyramidal decomposition. The new system is not shift-invariant-it suffers from "aliasing". We introduce a new quadratic error criterion which keeps into account the inherent system aliasing. By using hypermatrix and Kronecker algebra, we are able to cast the global optimization task into a multilinear problem. An iterative procedure ("pseudo-SVD") is used to minimize the overall quadratic approximation error.

Additional Information

© 1995 IEEE. Date of Current Version: 06 August 2002. We gratefully acknowledge support from NSF Grant IRI 9306155 on "Geometry driven diffusions", NSF Research Initiation grant IRI 9211651, ONR grant N00014-93-1-0990, a NSF National Young Investigator Award to PP. This work is supported in part by the Center for Neuromorphic Systems Engineering as a part of the National Science Foundation Engineering Research Center Program; and by the California Trade and Commerce Agency, Office of Strategic Technology.

Additional details

Created:
August 20, 2023
Modified:
October 24, 2023