Chapter 4: Discrete scale-space theory in higher dimensions
In chapter 4 in Scale-Space Theory in Computer Vision,
the one-dimensional scale-space theory from
to discrete signals of arbitrary dimension.
The treatment is based upon the assumptions that
the scale-space representation should be defined
by convolving the original signal with a one-parameter
family of symmetric smoothing kernels possessing a
semi-group property, and
local extrema must not be enhanced
when the scale parameter is increased continuously.
Given these requirements, the scale-space representation
must satisfy a semi-discretized
version of the
In a special case the
representation is given by convolution with the
one-dimensional discrete analogue of the Gaussian kernel
along each dimension.
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