Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Model-based clustering algorithms, performance and application
Model-based clustering algorithms, performance and application
Automatically transforming symbolic shape descriptions for use in sketch recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Perceptually based learning of shape descriptions for sketch recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
LADDER: a language to describe drawing, display, and editing in sketch recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
ACM SIGGRAPH 2007 courses
An efficient graph-based recognizer for hand-drawn symbols
Computers and Graphics
Sketch recognition in interspersed drawings using time-based graphical models
Computers and Graphics
Games for sketch data collection
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Computational Support for Sketching in Design: A Review
Foundations and Trends in Human-Computer Interaction
QuickDiagram: a system for online sketching and understanding of diagrams
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Sketch-based recognition system for general articulated skeletal figures
Proceedings of the Seventh Sketch-Based Interfaces and Modeling Symposium
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Sketch interfaces provide more natural interaction than the traditional mouse and palette tool, but can be time consuming to build if they have to be built anew for each new domain. A shape description language, such as the LADDER language we created, can significantly reduce the time necessary to create a sketch interface by enabling automatic generation of the interface from a domain description. However, structural shape descriptions, whether written by users or created automatically by the computer, are frequently over- or under- constrained. We present a technique to debug over- and under-constrained shapes using a novel form of active learning that generates its own suspected near-miss examples. Using this technique we implemented a graphical debugging tool for use by sketch interface developers.