Recognition of sign language motion images
Pattern Recognition
Foundations of robotics: analysis and control
Foundations of robotics: analysis and control
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Spectrum
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Real Time Gesture Recognition Using Eigenspace from Multi Input Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
DigitEyes: Vision-Based Human Hand Tracking
DigitEyes: Vision-Based Human Hand Tracking
Multi-agent System for Recognition of Hand Postures
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
The Leap Motion controller: a view on sign language
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
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Automatic gesture recognition systems generally require two separate processes: a motion sensing process where some motion features are extracted from the visual input; and a classification process where the features are recognised as gestures. We have developed the Hand Motion Understanding (HMU) system that uses the combination of a 3D model-based hand tracker for motion sensing and an adaptive fuzzy expert system for motion classification. The HMU system understands static and dynamic hand signs of the Australian Sign Language (Auslan). This paper presents the hand tracker that extracts 3D hand configuration data with 21 degrees-of-freedom (DOFs) from a 2D image sequence that is captured from a single viewpoint, with the aid of a colour-coded glove. Then the temporal sequence of 3D hand configurations detected by the tracker is recognised as a sign by an adaptive fuzzy expert system. The HMU system was evaluated with 22 static and dynamic signs. Before training the HMU system achieved 91% recognition, and after training it achieved over 95% recognition.