---
title: "Scale-Space Filtering"
authors:
  - "Andrew P. Witkin"
doi: ""
url: "https://ijcai.org/proceedings/1983/235"
pdf: "https://ijcai.org/proceedings/1983/0235.pdf"
venue: "Proceedings of the Eighth International Joint Conference on Artificial Intelligence"
year: 1983
pages: "1019-1021"
track: "Vision"
keywords:
---

# Scale-Space Filtering

Andrew P. Witkin

*Proceedings of the Eighth International Joint Conference on Artificial Intelligence*, pages 1019-1021.

## Abstract

The extrema in a signal and its first few derivatives provide a useful general-purpose qualitativedescription 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 meth od 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.