Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Combining gestures and direct manipulation
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The automatic recognition of gestures
The automatic recognition of gestures
Pen computing for air traffic control
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The immediate usability of graffiti
Proceedings of the conference on Graphics interface '97
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust sketched symbol fragmentation using templates
Proceedings of the 9th international conference on Intelligent user interfaces
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
User-defined gestures for surface computing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SOMM: Self organizing Markov map for gesture recognition
Pattern Recognition Letters
Trainable sketch recognizer for graphical user interface design
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction
A lightweight multistroke recognizer for user interface prototypes
Proceedings of Graphics Interface 2010
Enhancing single touch gesture classifiers to multitouch support
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs
Recognition of multi-touch drawn sketches
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
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We propose a probabilistic classifier for multi-touch gestures specified by users themselves. The template-based gesture classifier allows selecting gesture types more freely without constraints regarding implementation issues and considers multi-finger or bi-manual operations. The statistical approaches to the classification scheme are presented. The basic concepts of separating input into tokens, retrieving local features and applying a new method of sensor fusion under uncertainty are adaptive to broader application ranges. Results from testing against a set of sophisticated samples show that this approach performs well and, while recognition benefits from more complex gestures, it also distinguishes subtly different gestures.