Edge detection and ridge detection with automatic scale selection
Lindeberg, T.
Computer Vision and Pattern Recognition, 1996. Proceedings CVPR apos;96, 1996 IEEE Computer Society Conference on
Volume , Issue , 18-20 Jun 1996 Page(s):465 - 470
Digital Object Identifier 10.1109/CVPR.1996.517113
Summary:When extracting features from image data, the type of information
that can be extracted may be strongly dependent on the scales at which
the feature detectors are applied. This article presents a systematic
methodology for addressing this problem. A mechanism is presented for
automatic selection of scale levels when detecting one-dimensional
features, such as edges and ridges. A novel concept of a scale-space
edge is introduced, defined as a connected set of points in scale-space
at which: (i) the gradient magnitude assumes a local maximum in the
gradient direction, and (ii) a normalized measure of the strength of the
edge response is locally maximal over scales. An important property of
this definition is that it allows the scale levels to vary along the
edge. Two specific measures of edge strength are analysed in detail. It
is shown that by expressing these in terms of γ-normalized
derivatives, an immediate consequence of this definition is that fine
scales are selected for sharp edges (so as to reduce the shape
distortions due to scale-space smoothing), whereas coarse scales are
selected for diffuse edges, such that an edge model constitutes a valid
abstraction of the intensity profile across the edge. With slight
modifications, this idea can be used for formulating a ridge detector
with automatic scale selection, having the characteristic property that
the selected scales on a scale-space ridge instead reflect the width of
the ridge
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