Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Learning the basic units in American Sign Language using discriminative segmental feature selection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
GART: the gesture and activity recognition toolkit
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Large lexicon detection of sign language
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
American sign language recognition with the kinect
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
One-shot learning gesture recognition from RGB-D data using bag of features
The Journal of Machine Learning Research
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This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.