Dimension reduction by local principal component analysis
Neural Computation
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A framework for motion recognition with applications to American sign language and gait recognition
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
View-Based Detection and Analysis of Periodic Motion
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Wearable Computer Based American Sign Language Recognizer
ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
Motion-Based Recognition of People in EigenGait Space
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A SRN/HMM System for Signer-Independent Continuous Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Finding Periodicity in Space and Time
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Parameterized Modeling and Recognition of Activities
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Gesture driven systems in mobile environments for telecare applications
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
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This paper presents a hierarchical approach to recognize isolated 3-D hand gesture trajectories for Signing Exact English (SEE). SEE hand gestures can be periodic as well as non-periodic. We first differentiate between periodic and non-periodic gestures followed by recognition of individual gestures. After periodicity detection, non-periodic trajectories are classified into 8 classes and periodic trajectories are classified into 4 classes. A Polhemus tracker is used to provide the input data. Periodicity detection is based on Fourier analysis and hand trajectories are recognized by Vector Quantization Principal Component Analysis (VQPCA). The average periodicity detection accuracy is 95.9%. The average recognition rates with VQPCA for nonperiodic and periodic gestures are 97.3% and 97.0% respectively. In comparison, k-means clustering yielded 87.0% and 85.1 %, respectively.