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Edge detection and ridge detection with automatic scale selection
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-9081-2170
1996 (English)In: Proceedings CVPR '96, 1996, 465-470 p.Conference paper, Published paper (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
1996. 465-470 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-40219DOI: 10.1109/CVPR.1996.517113OAI: oai:DiVA.org:kth-40219DiVA: diva2:452308
Conference
1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognitio, San Francisco, California, June 16{21, 1996.
Note

QC 20111028

Available from: 2013-04-19 Created: 2011-09-13 Last updated: 2013-04-19Bibliographically approved

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fulltext(1542 kB)330 downloads
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Lindeberg, Tony

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  • apa
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