Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture spotting from continuous hand motion
Pattern Recognition Letters
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
Sequence Learning - Paradigms, Algorithms, and Applications
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An Interweaved HMM/DTW Approach to Robust Time Series Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
STARS: Sign tracking and recognition system using input-output HMMs
Pattern Recognition Letters
Clustering of time series data-a survey
Pattern Recognition
IEEE Transactions on Signal Processing
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Human behavior understanding for inducing behavioral change: application perspectives
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Understanding the consistency of users' pen and finger stroke gesture articulation
Proceedings of Graphics Interface 2013
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Vision based hand gesture recognition systems track the hands and extract their spatial trajectory and shape information, which are then classified with machine learning methods. In this work, we propose a dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) literature. A dataset of 1200 samples consisting of the ten digits written in the air by 12 people is used to show the efficiency of the method. Hidden Markov model (HMM), input-output HMM (IOHMM), hidden conditional random field (HCRF) and explicit duration model (EDM), which is a type of hidden semi Markov model (HSMM) are trained on the raw dataset and the clustered dataset. Optimal model complexities and recognition accuracies of each model for both cases are compared. Experiments show that the recognition rates undergo substantial improvement, reaching perfect accuracy for most of the models, and the optimal model complexities are significantly reduced.