Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Recognizing and interpreting diagrams in design
AVI '94 Proceedings of the workshop on Advanced visual interfaces
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Efficient Subgraph Isomorphism Detection: A Decomposition Approach
IEEE Transactions on Knowledge and Data Engineering
Introduction to the Special Section on Graph Algorithms in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
A Shape Analysis Model with Applications to a Character Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbol Recognition: Current Advances and Perspectives
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Hierarchical parsing and recognition of hand-sketched diagrams
Proceedings of the 17th annual ACM symposium on User interface software and technology
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
An image-based, trainable symbol recognizer for hand-drawn sketches
Computers and Graphics
LADDER, a sketching language for user interface developers
Computers and Graphics
Recognition and beautification of multi-stroke symbols in digital ink
Computers and Graphics
Combining geometry and domain knowledge to interpret hand-drawn diagrams
Computers and Graphics
Affine-invariant B-spline moments for curve matching
IEEE Transactions on Image Processing
Cascading recognizers for ambiguous calligraphic interaction
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
Computers and Graphics
Gesture Recognition Based on Manifold Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Sketch-based subdivision models
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Computing confidence values for geometric constraints for use in sketch recognition
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
Sketch-based recognition system for general articulated skeletal figures
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
Technical Section: SpeedSeg: A technique for segmenting pen strokes using pen speed
Computers and Graphics
Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling
ClassySeg: a machine learning approach to automatic stroke segmentation
Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
Technical Section: A machine learning approach to automatic stroke segmentation
Computers and Graphics
Hi-index | 0.00 |
We describe a trainable, multi-stroke symbol recognizer for pen-based user interfaces. The approach is insensitive to orientation, non-uniform scaling, and drawing order. Symbols are represented internally as attributed relational graphs describing both the geometry and topology of the symbols. Symbol definitions are statistical models, which makes the approach robust to variations common in hand-drawn shapes. Symbol recognition requires finding the definition symbol whose attributed relational graph best matches that of the unknown symbol. Much of the power of the approach derives from the particular set of attributes used, and our metrics for measuring similarity between graphs. One challenge addressed in the current work is how to perform the graph matching efficiently. We present five approximate matching techniques: stochastic matching, which is based on stochastic search; error-driven matching, which uses local matching errors to drive the solution to an optimal match; greedy matching, which uses greedy search; hybrid matching, which uses exhaustive search for small problems and stochastic matching for larger ones; and sort matching, which relies on geometric information to accelerate the matching. Finally, we present the results of a user study, and discuss the tradeoffs between the various matching techniques.