Towards a computational model of sketching
Proceedings of the 6th international conference on Intelligent user interfaces
Image Editing in the Contour Domain
IEEE Transactions on Pattern Analysis and Machine Intelligence
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GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualitative spatial reasoning about sketch maps
AI Magazine
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International Journal of Computer Vision
Resolving ambiguities to create a natural computer-based sketching environment
ACM SIGGRAPH 2006 Courses
Resolving ambiguities to create a natural computer-based sketching environment
ACM SIGGRAPH 2007 courses
Perceptual-based region extraction from hand drawn sketches
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Resolving ambiguities to create a natural computer-based sketching environment
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Recognizing sketched multistroke primitives
ACM Transactions on Interactive Intelligent Systems (TiiS)
Design and evaluation of a perceptual-based object group selection technique
BCS '10 Proceedings of the 24th BCS Interaction Specialist Group Conference
Using scribble gestures to enhance editing behaviors of sketch recognition systems
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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The paper shows how techniques from computational vision can be deployed to support interactive sketch editing. While conventional computer supported drawing tools give users access to visible marks or image objects at a single level of abstraction, a human user's visual system rapidly constructs complex groupings and associations among image elements according to his or her immediate purposes. We have been exploring perceptually supported sketch editors in which computer vision algorithms run continuously, behind the scenes, to afford users efficient access to emergent visual objects in a drawing. We employ a flexible image interpretation architecture based on token grouping in a multiscale blackboard data structure. This organization supports multiple perceptual interpretations of line drawing data, domain specific knowledge bases for interpreting visual structures, and natural gesture based selection of visual objects.