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
Proceedings ArticleDOI
Applying (3+2+1)D Residual Neural Network with Frame Selection for Hong Kong Sign Language Recognition
TL;DR: A novel method called (3+2+1)D ResNet Model with Frame Selection which adopts blurriness detection with Laplacian kernel to construct high-quality video clips and also combines both (2+ 1)D and 3D Res net for recognizing the sign language is proposed.
Journal ArticleDOI
Supporting One-Time Point Annotations for Gesture Recognition
TL;DR: A new annotation technique that reduces significantly the amount of time to annotate training data for gesture recognition, and a novel BoundarySearch algorithm to find automatically the correct temporal boundaries of gestures by discovering data patterns around their given one-time point annotations.
Book ChapterDOI
Sign Languages Recognition Based on Neural Network Architecture
TL;DR: The system is a first step for extending the possibility of using virtual assistants to speech impaired people by providing an artificial sign languages recognition based on neural network architecture.
Journal ArticleDOI
LBPV for Recognition of Sign Language at Sentence Level: An Approach Based on Symbolic Representation
TL;DR: An approach that exploits the texture description technique and symbolic data analysis concept to characterize and effectively represent a sign, taking into account the intra-class variations due to different signers or the same signers at different instances of time is proposed.
Posted Content
Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network.
TL;DR: Wang et al. as discussed by the authors formulated the unique characteristics of sign languages as hierarchical spatio-temporal graph representations, including high-level and fine-level graphs of which a vertex characterizes a specified body part and an edge represents their interactions.
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.