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
Journal ArticleDOI
How important is motion in sign language translation
Journal ArticleDOI
Evaluation of hidden Markov models using deep CNN features in isolated sign recognition
Anil Osman Tur,Hacer Yalim Keles +1 more
TL;DR: This study provides a framework that is composed of three modules to solve isolated sign recognition problem using different sequence models, and proposes two alternative CNN based architectures as the second module in the authors' framework, to reduce deep feature dimensions effectively.
Proceedings ArticleDOI
Towards Multilingual Sign Language Recognition
TL;DR: This paper develops a multilingual sign language approach, where hand movement modeling is also done with target sign language independent data by derivation of hand movement subunits, and demonstrates that sign language recognition systems can be effectively developed by using mult bilingual sign language resources.
Journal ArticleDOI
The Application of Cloud Computing Intelligent Optimization Algorithm in the Investigation of College Students’ English Autonomous Learning under the Multimedia Teaching Mode
Hengxi Wang,Jing Xu +1 more
TL;DR: In this paper, a cloud computing intelligent optimization algorithm in the multimedia teaching mode of college students' English autonomous learning system is developed to help self-learners learn translation, listening, speaking, and other skills.
Posted Content
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
TL;DR: This paper introduces a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers, and proposes a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose- based method.
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.