On Scale Selection for Differential Operators

Tony Lindeberg

Proc. 8th Scandinavian Conference on Image Analysis, (Tromso, Norway), 857--866, May 1993,

Also available as technical report ISRN KTH/NA/P--93/12--SE.


Although traditional scale-space theory provides a well-founded framework for dealing with image structures at different scales, it does not directly address the problem of how to select appropriate scales for further analysis.

This paper introduces a new tool for dealing with this problem. A heuristic principle is proposed stating that local extrema over scales of different combinations of normalized scale invariant derivatives are likely candidates to correspond to interesting structures. Support is given by theoretical considerations and experiments on real and synthetic data.

The resulting methodology lends itself naturally to two-stage algorithms; feature detection at coarse scales followed by feature localization at finer scales. Experiments on blob detection, junction detection and edge detection demonstrate that the proposed method gives intuitively reasonable results.

Keywords: scale, scale-space, scale selection, Gaussian derivative, feature detection, edge detection, junction detection, blob detection, texture analysis

Full paper: (PostScript 0.8Mb) (PDF 0.5Mb)

Further work: (Junction detection) (Shape from texture) (Extended version) (General theory for feature detection with automatic scale selection) (Edge detection with automatic scale selection)

Responsible for this page: Tony Lindeberg