Book ChapterDOI
Sign language recognition using sub-units
Reads0
Chats0
TLDR
This paper discusses sign language recognition using linguistic sub-units, presenting three types of sub- units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data.Abstract:
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%.read more
Citations
More filters
Book ChapterDOI
Human Hand Motion Recognition Using an Extended Particle Filter
TL;DR: A deterministic clustering algorithm and particle filter together are integrated together to recognize hand motion using an extended particle filter in real-time and are able to cope extremely well with luminance changes.
Journal ArticleDOI
EIS: A Wearable Device for Epidermal American Sign Language Recognition
TL;DR: An epidermal-iontronic sensing (EIS)-based wearable device that wears on finger joints for 35 fingerspelling ASL recognitions and uses machine learning methods, such as neural networks, to track and perform ASL recognition using the signals obtained from the designed device.
Journal ArticleDOI
A Novel Mathematical Modeling and Parameterization for Sign Language Classification
TL;DR: Empirical analysis of different mathematical models for Pakistan SLR (PSLR) is presented using the parameterization of sign signature, which results in a very small feature vector and hence to a very efficient system.
Proceedings ArticleDOI
Real-Time Chinese Sign Language Recognition Based on Artificial Neural Networks *
Zhen Zhang,Ziyi Su,Ge Yang +2 more
TL;DR: A real-time Chinese Sign Language (CSL) recognition model based on surface electromyographic (sEMG) signals and Artificial Neural Networks (ANN) is proposed and achieves an average accuracy at 88.7% on 15 CSL gestures.
Proceedings ArticleDOI
Unsupervised Feature Learning for Visual Sign Language Identification
TL;DR: Given that sign languages are underresourced, unsupervised feature learning techniques are the right tools and the results indicate that this is realistic for sign language identification.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal ArticleDOI
Visual pattern recognition by moment invariants
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Journal ArticleDOI
Shape quantization and recognition with randomized trees
Yali Amit,Donald Geman +1 more
TL;DR: A new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity, and a comparison with artificial neural networks methods is presented.