Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
IEEE Computer Graphics and Applications
Ligature Modeling for Online Cursive Script Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Velocity Profile Based Recognition of Dynamic Gestures with Discrete Hidden Markov Models
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
Hidden Markov Model Based Continuous Online Gesture Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Human action learning via hidden Markov model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
On the structure of hidden Markov models
Pattern Recognition Letters
Airwriting recognition using wearable motion sensors
Proceedings of the 1st Augmented Human International Conference
An evaluation of a low-cost 3-dimensional gestural interface: Wii3D
Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment
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We present a glove-based hand gesture recognition system using hidden Markov models (HMMs) for recognizing the unconstrained 3D trajectory gestures of operators in a remote work environment. A Polhemus sensor attached to a PinchGlove is employed to obtain a sequence of 3D positions of a hand trajectory. The direct use of 3D data provides more naturalness in generating gestures, thereby avoiding some of the constraints usually imposed to prevent performance degradation when trajectory data are projected into a specific 2D plane. We use two kinds of HMMs according to the basic units to be modeled: gesture-based HMM and stroke-based HMM. The decomposition of gestures into more primitive strokes is quite attractive, since reversely concatenating stroke-based HMMs makes it possible to construct a new set of gesture-based HMMs. Any deterioration in performance and reliability arising from decomposition can be remedied by a fine-tuned relearning process for such composite HMMs. We also propose an efficient method of estimating a variable threshold of reliability for an HMM, which is found to be useful in rejecting unreliable patterns. In recognition experiments on 16 types of gestures defined for remote work, the fine-tuned composite HMM achieves the best performance of 96.88% recognition rate and also the highest reliability.