A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Segmentation of edges into lines and arcs
Image and Vision Computing
Stable and Robust Vectorization: How to Make the Right Choices
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
Stable, Robust and Off-the-Shelf Methods for Graphics Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
RANVEC and the Arc Segmentation Contest
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
A Statistical and Structural Approach for Symbol Recognition, Using XML Modelling
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graphics Recognition - from Re-engineering to Retrieval
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Robust and Accurate Vectorization of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
RANVEC and the arc segmentation contest: second evaluation
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Graphical knowledge management in graphics recognition systems
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Accurate junction detection and characterization in line-drawing images
Pattern Recognition
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In this paper, we present a method for correcting a skeleton-based vectorization. The method robustlys egments 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 byp olygonal approximation method, and improves the precision of the junction points 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.