Improving the Accuracy of Skeleton-Based Vectorization

Xavier Hilaire 1 Karl Tombre 1
1 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, we present a method for correcting a skeleton-based vectorization. The method robustly segments the skeleton of an image into basic features, and uses these features to reconstruct analytically all the junctions. It corrects some of the topological errors usually brought by polygonal approximation methods, and improves the precision of the junction point detection. We first give some reminders on vectorization and explain what a good vectorization is supposed to be. We also explain the advantages and drawbacks of using skeletons. We then explain in detail our correction method, and show results on cases known to be problematic.
Mots-clés : graphics recognition vectorization reconnaissance de graphiques vectorisation
Type de document :
Chapitre d'ouvrage
Dorothea Blostein and Young-Bin Kwon. Graphics Recognition - Algorithms and Applications, Springer Verlag, pp.273-288, 2002, Lecture Notes in Computer Science
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https://hal.inria.fr/inria-00100840
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:52:19
Dernière modification le : jeudi 11 janvier 2018 - 06:19:59

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  • HAL Id : inria-00100840, version 1

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INRIA | LORIA | UNIV-LORRAINE

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Xavier Hilaire, Karl Tombre. Improving the Accuracy of Skeleton-Based Vectorization. Dorothea Blostein and Young-Bin Kwon. Graphics Recognition - Algorithms and Applications, Springer Verlag, pp.273-288, 2002, Lecture Notes in Computer Science. 〈inria-00100840〉

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