Scale-space filtering: A new approach to multi-scale description

The extrema in a signal and its first few derivatives pro-vide a useful general purpose qualitative description for many kinds of signals. A fundamental problem in comput-ing 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 ambigu-ity 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 col-lapsed, 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. 1