Optimizing parametric deformable kernels: space-variant filtering and scaling-rotation invariance

Abstract-In this paper, we study the result of applying a lowpass variant filtering using scaling-rotating kernels to both the spatial and spatial-frequency representations of a two-dimensional (2-D) signal (image). It is shown that if we apply this transformation to a Fourier pair, the two resulting signals can also form a Fourier pair when the filters used in each domain maintain a dual relationship. For a large class of "self-dual" filters, a perfect symmetry exists, so that the lowpass scaling-rotating variant filtering (SRVF) is the same in both domains, thus commuting with the Fourier transform operator. The lowpass SRVF of an image is often referred to as a "foveated" image, whereas its Fourier pair (the lowpass SRVF of its spectrum) can be realized as a local spectrum estimation around the point of attention. This lowpass SRVF is equivalent to a log-polar warping of the image representation followed by a lowpass invariant filtering and the corresponding inverse warping. The use of the log-polar warped representation allows us to extend the one-dimensional (1-D) scale transform to higher dimensions, in particular to images, for which we have defined a scale-rotation invariant representation. We also present an efficient implementation using steerable filters to compute both the foveated image and the local spectrum.

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Duality of log-polar image representations in the space and spatial-frequency domains

Tabernero

Portilla

Navarro

1999

IEEE Trans. Signal Process.

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Optimizing parametric deformable kernels: space-variant filtering and scaling-rotation invariance

Cited by 1 publication

References 9 publications

Duality of log-polar image representations in the space and spatial-frequency domains

Duality of log-polar image representations in the space and spatial-frequency domains

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