A framework for recognizing the simultaneous aspects of American sign language
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Speeding Up Bresenham's Algorithm
IEEE Computer Graphics and Applications
C# Language Specification
Signer-Independent Sign Language Recognition Based on SOFM/HMM
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Fast Hand Gesture Recognition for Real-Time Teleconferencing Applications
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Georgia tech gesture toolkit: supporting experiments in gesture recognition
Proceedings of the 5th international conference on Multimodal interfaces
Bare-hand human-computer interaction
Proceedings of the 2001 workshop on Perceptive user interfaces
On-line cursive handwriting recognition using hidden Markov models and statistical grammars
HLT '94 Proceedings of the workshop on Human Language Technology
Hand gesture recognition with a novel IR time-of-flight range camera: a pilot study
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
GART: the gesture and activity recognition toolkit
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
A gestural approach to presentation exploiting motion capture metaphors
Proceedings of the International Working Conference on Advanced Visual Interfaces
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Real-time, static and dynamic hand gesture learning and recognition makes it possible to have computers recognize hand gestures naturally. This creates endless possibilities in the way humans can interact with computers, allowing a human hand to be a peripheral by itself. The software framework developed provides a lightweight, robust, and practical application programming interface that helps further research in the area of human-computer interaction. Approaches that have proven in analogous areas such as speech and handwriting recognition were applied to static and dynamic hand gestures. A semi-supervised Fuzzy ARTMAP neural network was used for incremental online learning and recognition of static gestures; and, Hidden Markov models for online recognition of dynamic gestures. A simple anticipatory method was implemented for determining when to update key frames allowing the framework to work with dynamic backgrounds.