Scale-space filtering

  • Authors:
  • Andrew P. Witkin

  • Affiliations:
  • Fairchild Laboratory for Artificial Intelligence Research

  • Venue:
  • IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1983

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Abstract

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 gaussian masks over a continuum of sizes. This "scale-space" image is then collapsed, using its qualitative structure, into a tree providing a concise but complete qualitative description covering all scales of observation. The description is further refined by applying a stability criterion, to identify events that persist of large changes in scale.