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Proceedings Paper

Multiscale corner detection and classification using local properties and semantic patterns
Author(s): Giovanni Gallo; Alessandro Lo Giuoco
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Paper Abstract

A new technique to detect, localize and classify corners in digital closed curves is proposed. The technique is based on correct estimation of support regions for each point. We compute multiscale curvature to detect and to localize corners. As a further step, with the aid of some local features, it's possible to classify corners into seven distinct types. Classification is performed using a set of rules, which describe corners according to preset semantic patterns. Compared with existing techniques, the proposed approach inscribes itself into the family of algorithms that try to explain the curve, instead of simple labeling. Moreover, our technique works in manner similar to what is believed are typical mechanisms of human perception.

Paper Details

Date Published: 22 May 2002
PDF: 12 pages
Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); doi: 10.1117/12.467972
Show Author Affiliations
Giovanni Gallo, Univ. degli Studi di Catania (Italy)
Alessandro Lo Giuoco, Univ. degli Studi di Catania (Italy)

Published in SPIE Proceedings Vol. 4667:
Image Processing: Algorithms and Systems
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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