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

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Extensive description: Longer technical report

Applications: Curve classification and generation of break points for MDL curve classification.