Deformable Kernels for Early Vision (1991)
| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 112 - 8 self |
BibTeX
@ARTICLE{Perona91deformablekernels,
author = {Pietro Perona},
title = {Deformable Kernels for Early Vision},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {1991},
volume = {17},
pages = {488--499}
}
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Abstract
Early vision algorithms often have a first stage of linear-filtering that `extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. This discretization produces anisotropies due to a loss of traslation-, rotation-, scaling-invariance that makes early vision algorithms less precise and more difficult to design. This need not be so: one can compute and store efficiently the response of families of linear filters defined on a continuum of orientations and scales. A technique is presented that allows (1) to compute the best approximation of a given family using linear combinations of a small number of `basis' functions; (2) to describe all finite-dimensional families, i.e. the families of filters for which a finite dimensional representation is p...
