Junction detection with automatic selection of
detection scales and localization scales
Tony Lindeberg
Proc. 1st International Conference on Image Processing,
(Austin, Texas), vol. I. pp. 924-928, Nov. 1994.
Abstract
The subject of scale selection is essential to many
aspects of multi-scale and multi-resolution processing
of image data.
This article shows how a general heuristic principle for
scale selection can be applied to the problem of
detecting and localizing junctions.
In a first uncommitted processing step
initial hypotheses about interesting scale levels
(and regions of interest) are
generated from scales where normalized
differential invariants assume maxima over scales
(and space).
Then, based on this scale (and region) information,
a more refined processing stage is invoked tuned
to the task at hand.
The resulting method is the first
junction detector with automatic scale selection.
Whereas this article deals with the specific
problem of junction detection, the underlying ideas
apply also to other types of differential feature detectors,
such as blob detectors, edge detectors, and ridge detectors.
Keywords: Junction detection, junction localization, automatic scale selection,
normalized derivative, feature detection, Gaussian derivative, scale-space
PostScript:
(307 kb)
Extensive description:
Longer technical report
Applications:
Curve classification and generation of break points
for MDL curve classification.
Tony Lindeberg <tony@nada.kth.se>