Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Hierarchical Mixtures of Experts and the EM Algorithm
Hierarchical Mixtures of Experts and the EM Algorithm
Using Multiple Sensors for Mobile Sign Language Recognition
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier hierarchy learning by means of genetic algorithms
Pattern Recognition Letters
Robotics and Autonomous Systems
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer
IEEE Transactions on Neural Networks
Thai sign language translation using Scale Invariant Feature Transform and Hidden Markov Models
Pattern Recognition Letters
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Sign and gesture recognition offers a natural way for human-computer interaction. This paper presents a real time sign recognition architecture including both gesture and movement recognition. Among the different technologies available for sign recognition data gloves and accelerometers were chosen for the purposes of this research. Due to the real time nature of the problem, the proposed approach works in two different tiers, the segmentation tier and the classification tier. In the first stage the glove and accelerometer signals are processed for segmentation purposes, separating the different signs performed by the system user. In the second stage the values received from the segmentation tier are classified. In an effort to emphasize the real use of the architecture, this approach deals specially with problems like sensor noise and simplification of the training phase.