Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sketch-Based User Interface for Inputting Graphic Objects on Small Screen Devices
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Finding Perceptually Closed Paths in Sketches and Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust sketched symbol fragmentation using templates
Proceedings of the 9th international conference on Intelligent user interfaces
Sketch based interfaces: early processing for sketch understanding
Proceedings of the 2001 workshop on Perceptive user interfaces
An online composite graphics recognition approach based on matching of spatial relation graphs
International Journal on Document Analysis and Recognition
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
Informal user interface for graphical computing
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Sketch parameterization using curve approximation
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Adaptive online multi-stroke sketch recognition based on hidden markov model
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
A framework for sketch-based cooperative design
CSCWD'06 Proceedings of the 10th international conference on Computer supported cooperative work in design III
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This paper presents a solution for online composite sketchy shape recognition. The kernel of the strategy treats both stroke segmentation and sketch recognition as an optimization problem of “fitting to a template”. A nested recursive optimization process is then designed by means of dynamic programming to do stroke segmentation and symbol recognition cooperatively by minimizing the fitting errors between inputting patterns and templates. Experimental results prove the effectiveness of the proposed method.