Adaptive vectorization of line drawing images
Computer Vision and Image Understanding
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Algorithm Design
Robust and Accurate Vectorization of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curve-Skeleton Properties, Applications, and Algorithms
IEEE Transactions on Visualization and Computer Graphics
Image vectorization using optimized gradient meshes
ACM SIGGRAPH 2007 papers
Diffusion curves: a vector representation for smooth-shaded images
ACM SIGGRAPH 2008 papers
Scribbles to vectors: preparation of scribble drawings for CAD interpretation
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
Vectorizing Cartoon Animations
IEEE Transactions on Visualization and Computer Graphics
A Survey on Skeletons in Digital Image Processing
ICDIP '09 Proceedings of the International Conference on Digital Image Processing
Patch-based image vectorization with automatic curvilinear feature alignment
ACM SIGGRAPH Asia 2009 papers
Video codec for classical cartoon animations with hardware accelerated playback
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Ardeco: automatic region detection and conversion
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Style and abstraction in portrait sketching
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Stereoscopizing cel animations
ACM Transactions on Graphics (TOG)
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Vectorization provides a link between raster scans of pencil-and-paper drawings and modern digital processing algorithms that require accurate vector representations. Even when input drawings are comprised of clean, crisp lines, inherent ambiguities near junctions make vectorization deceptively difficult. As a consequence, current vectorization approaches often fail to faithfully capture the junctions of drawn strokes. We propose a vectorization algorithm specialized for clean line drawings that analyzes the drawing's topology in order to overcome junction ambiguities. A gradient-based pixel clustering technique facilitates topology computation. This topological information is exploited during centerline extraction by a new “reverse drawing” procedure that reconstructs all possible drawing states prior to the creation of a junction and then selects the most likely stroke configuration. For cases where the automatic result does not match the artist's interpretation, our drawing analysis enables an efficient user interface to easily adjust the junction location. We demonstrate results on professional examples and evaluate the vectorization quality with quantitative comparison to hand-traced centerlines as well as the results of leading commercial algorithms.