Enhancing curvature scale space features for robust shape classification

Kopf, Stephan ; Haenselmann, Thomas ; Effelsberg, Wolfgang

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URL:	https://ub-madoc.bib.uni-mannheim.de/38960
Weitere URL:	http://dx.doi.org/10.1109/ICME.2005.1521464
URN:	urn:nbn:de:bsz:180-madoc-389606
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2005
Buchtitel:	2005 IEEE International Conference on Multimedia and Expo (ICME) : July 6-8, 2005, Amsterdam, the Netherlands
Band/Volume:	1
Seitenbereich:	478-481
Veranstaltungsdatum:	2005
Ort der Veröffentlichung:	Piscataway, NJ
Verlag:	IEEE Operations Center
ISBN:	0-7803-9331-7
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Praktische Informatik IV (Effelsberg 1989-2017)
Fachgebiet:	004 Informatik
Freie Schlagwörter (Englisch):	character recognition , code standards , image classification , image enhancement , image representation , image segmentation , video coding , CSS , MPEG-7 standard ,automatic character recognition,concave-convex segmentation
Abstract:	The curvature scale space (CSS) technique, which is also part of the MPEG-7 standard, is a robust method to describe complex shapes. The central idea is to analyze the curvature of a shape and derive features from inflection points. A major drawback of the CSS method is its poor representation of convex segments: Convex objects cannot be represented at all due to missing inflection points. We have extended the CSS approach to generate feature points for concave and convex segments of a shape. This generic approach is applicable to arbitrary objects. In the experimental results, we evaluate as a comprehensive example the automatic recognition of characters in images and videos.