Book ChapterDOI
Sign language recognition using sub-units
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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
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Proceedings ArticleDOI
Semi-automatic annotation tool for sign languages
TL;DR: The semi-automatic web-based annotation tool based on second technique, which uses hand and face movement detection algorithms, could be used not only for annotating clean training data, but also for automatic sign language recognition, as it is works in real time and quite robust to variability in intensity and background.
Proceedings ArticleDOI
Leveraging intra-class variations to improve large vocabulary gesture recognition
TL;DR: This paper introduces Multiple-Pass DTW (MP-DTW), a method in which scores from multiple DTW passes focusing on different gesture properties are combined, and introduces a new set of features modeling intra-class variation of several gesture properties that can be used in conjunction with MP- DTW or DTW.
Proceedings ArticleDOI
FineHand: Learning Hand Shapes for American Sign Language Recognition
TL;DR: In this article, a hand shape embedding is used for ASL gesture recognition and the sequential gesture component is captured by recursive neural network (RNN) trained on the embeddings learned in the first stage.
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A Dictionary Approach to Identifying Transient RFI
TL;DR: An automated method of extracting and labeling subevents using a data set of transient RFI and achieves improved classification accuracy over traditional approaches such as support vector machines or a naïve k‐Nearest Neighbor classifier.
Journal ArticleDOI
(2+1)D-SLR: an efficient network for video sign language recognition
TL;DR: A (2+1)D-SLR network based on (2+)D convolution, which is different from other methods in that the proposed network can achieve higher accuracy with a faster speed, and can not only achieve competitive accuracy but be much faster than current well-known sign language recognition methods.
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Yali Amit,Donald Geman +1 more
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