Linear Spatio-Temporal Scale-Space

This article presents a scale-space theory for spatio-temporal data. Starting from the main assumptions that (i) the scale-space should be generated by convolution with a semi-group of filter kernels and that (ii) local extrema must not be enhanced when the scale parameter increases, a complete taxonomy is given of the linear scale-space concepts that satisfy these conditions on spatial, temporal and spatio-temporal domains, including the cases with continuous as well as discrete data.

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