A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
C4.5: programs for machine learning
C4.5: programs for machine learning
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Survey of Gesture RecognitionTechniques.
A Survey of Gesture RecognitionTechniques.
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Visual Recognition of Similar Gestures
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Modeling and Recognition of Gesture Signals in 2D Space: A Comparison of NN and SVM Approaches
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Implementation and comparison of three architectures for gesture recognition
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
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Previous evaluations of gesture recognition techniques have been focused on classification performance, while ignoring other relevant issues such as knowledge description, feature selection, error distribution and learning performance. In this paper, we present an empirical comparison of decision trees, neural networks and hidden Markov models for visual gesture recognition following these criteria. Our results show that none of these techniques is a definitive alternative for all these issues. While neural nets and hidden Markov models show the highest recognition rates, they sacrifice clarity of its knowledge; decision trees, on the other hand, are easy to create and analyze. Moreover, error dispersion is higher with neural nets. This information could be useful to develop a general computational theory of gestures. For the experiments, a database of 9 gestures with more than 7000 samples taken from 15 people was used.