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Issue No.05 - Sept.-Oct. (2013 vol.33)
pp: 59-67
ABSTRACT
Many computer graphics applications must fragment freehand curves into sets of prespecified geometric primitives. For example, sketch recognition typically converts hand-drawn strokes into line and arc segments and then combines these primitives into meaningful symbols for recognizing drawings. However, current fragmentation methods' shortcomings make them impractical. For example, they require manual tuning, require excessive computational resources, or produce suboptimal solutions that rely on local decisions. DPFrag is an efficient, globally optimal fragmentation method that learns segmentation parameters from data and produces fragmentations by combining primitive recognizers in a dynamic-programming framework. The fragmentation is fast and doesn't require laborious and tedious parameter tuning. In experiments, it beat state-of-the-art methods on standard databases with only a handful of labeled examples.
INDEX TERMS
Dynamic programming, Approximation methods, Computer graphics, Heuristic algorithms, Approximation algorithms, Cost function,computer graphics, user interfaces, sketch recognition, human-computer interaction
CITATION
R. S. Tumen, T. M. Sezgin, "DPFrag: Trainable Stroke Fragmentation Based on Dynamic Programming", IEEE Computer Graphics and Applications, vol.33, no. 5, pp. 59-67, Sept.-Oct. 2013, doi:10.1109/MCG.2012.124
REFERENCES
1. D.I.S. Adi, S.M.b. Shamsuddin, and S.Z.M. Hashim, “Nurbs Curve Approximation Using Particle Swarm Optimization,” Proc. 7th Int’l Conf. Computer Graphics, Imaging, and Visualization, IEEE CS, 2010, pp. 73-79.
2. G. Orbay and L.B. Kara, “Beautification of Design Sketches Using Trainable Stroke Clustering and Curve Fitting,” IEEE Trans. Visualization and Computer Graphics, vol. 17, no. 5, 2011, pp. 694-708.
3. C. Alvarado and R. Davis, “SketchRead: A Multi-domain Sketch Recognition Engine,” Proc. 17th Ann. ACM Symp. User Interface Software and Technology (UIST 04), ACM, 2004, pp. 23-32.
4. Operations Terms and Graphics, US Army Field Manual FM 1-02, Dept. of the Army, 2004; http://armypubs.army.mil/doctrine/DR_pubs/dr_a/pdf/fm1_02.pdf.
5. J. Herold and T.F. Stahovich, “ClassySeg: A Machine Learning Approach to Automatic Stroke Segmenta-tion,” Proc. 8th Eurographics Symp. Sketch-Based Inter-faces and Modeling (SBIM 11), ACM, 2011, pp. 109-116.
6. J. Herold and T.F. Stahovich, “SpeedSeg: A Technique for Segmenting Pen Strokes Using Pen Speed,” Computers & Graphics, vol. 35, no. 2, 2011, pp. 250-264.
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