Scale-space filtering: A new approach to multi-scale description
@inproceedings{Witkin1984ScalespaceFA,
title={Scale-space filtering: A new approach to multi-scale description},
author={Andrew P. Witkin},
booktitle={ICASSP},
year={1984}
}The extrema in a signal and its first few derivatives provide a useful general purpose qualitative description for many kinds of signals. A fundamental problem in computing such descriptions is scale: a derivative must be taken over some neighborhood, but there is seldom a principled basis for choosing its size. Scale-space filtering is a method that describes signals qualitatively, managing the ambiguity of scale in an organized and natural way. The signal is first expanded by convolution with…
1,142 Citations
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A new filtering kernel called Fejer kernel is proposed to try to tackle the problem of multiscale description of raw signal data and compares favorably with Gaussian scale space filtering in that it brings out the main features of the signal under large scales.
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It is shown that the Gaussian probability density function is the only kernel in a broad class for which first-order maxima and minima, respectively, increase and decrease when the bandwidth of the filter is increased.
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This paper presents the construction of a novel representation of gray-level shape called the scale-spectrum space, which makes both spatial frequency channels of specific importance as well as…
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- 1995
The authors show that only a discrete subset of filters gives rise to an evolution which can be characterized by means of a partial differential equation.
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- 2009
A brief survey of two broadly used multiscale formulations, namely, wavelets and scale-space filtering is presented and some possible applications of these approaches in image processing are presented.
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- Computer ScienceNeural Networks for Signal Processing III - Proceedings of the 1993 IEEE-SP Workshop
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- Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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