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Junction detection with automatic selection of detection scales and localization scales
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-9081-2170
1994 (English)In: Proc. 1st International Conference on Image Processing: ICIP'94 (Austin, Texas), 1994, I:924-928 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
1994. I:924-928 p.
Keyword [en]
junction detection, junction localization, automatic scale selection, normalized derivative, feature detection, Gaussian derivative, scale-space
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-58589DOI: 10.1109/ICIP.1994.413244OAI: oai:DiVA.org:kth-58589DiVA: diva2:473389
Conference
Proc. 1st International Conference on Image Processing, AUSTIN, TEXAS, Nov. 1994
Note

QC 20130419

Available from: 2013-04-19 Created: 2012-01-05 Last updated: 2013-04-19Bibliographically approved

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fulltext(283 kB)139 downloads
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Lindeberg, Tony

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CiteExportLink to record
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  • apa
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More styles
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