A protocol for performance evaluation of line detection algorithms
Machine Vision and Applications - Special issue on performance evaluation
Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coarse Vectorization as an Initial Representation for the Understanding of Line Drawing Images
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Stable and Robust Vectorization: How to Make the Right Choices
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
Adding Geometric Constraints to the Vectorization of Line Drawings
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Untangling the Blum Medial Axis Transform
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
Robust and Accurate Vectorization of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of Signals and Systems Using the Web and Matlab (3rd Edition)
Fundamentals of Signals and Systems Using the Web and Matlab (3rd Edition)
Scribble Vectorization Using Concentric Sampling Circles
ADVCOMP '09 Proceedings of the 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences
Optimal line and arc detection on run-length representations
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Exploiting artistic cues to obtain line labels for free-hand sketches
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
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Vectorization algorithms described in the literature assume that the drawings being vectorized are either binary images or have a clear white background. Sketches of artistic objects however also contain shadows which help the artist to portray intent, particularly in potentially ambiguous sketches. Such sketches are difficult to binarise since the shading strokes make these sketches non bimodal. For this reason, we describe a circle-based vectorization algorithm that uses signatures obtained from sample points on the line strokes to identify and vectorize the line strokes in the sketch. We show that the proposed algorithm performs as well as other vectorization techniques described in the literature, despite the shadows present in the sketch.