- Home
- Publications
- Publication Search
- Publication Details
Title
Understanding vision-based continuous sign language recognition
Authors
Keywords
-
Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-05-17
DOI
10.1007/s11042-020-08961-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images
- (2019) Dawid Warchoł et al. SENSORS
- Isolated sign language recognition using Convolutional Neural Network hand modelling and Hand Energy Image
- (2019) Kian Ming Lim et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Deep Forest-Based Monocular Visual Sign Language Recognition
- (2019) Qifan Xue et al. Applied Sciences-Basel
- A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
- (2019) Runpeng Cui et al. IEEE TRANSACTIONS ON MULTIMEDIA
- Deepside: A general deep framework for salient object detection
- (2019) Keren Fu et al. NEUROCOMPUTING
- A Novel Sign Language Recognition Framework Using Hierarchical Grassmann Covariance Matrix
- (2019) Hanjie Wang et al. IEEE TRANSACTIONS ON MULTIMEDIA
- Hand Sign Recognition for Thai Finger Spelling: an Application of Convolution Neural Network
- (2018) Pisit Nakjai et al. Journal of Signal Processing Systems for Signal Image and Video Technology
- Indian sign language recognition using graph matching on 3D motion captured signs
- (2018) D. Anil Kumar et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017
- (2018) Mohamed Aktham Ahmed et al. SENSORS
- Deep Sign: Enabling Robust Statistical Continuous Sign Language Recognition via Hybrid CNN-HMMs
- (2018) Oscar Koller et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
- (2016) Lionel Pigou et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- An algorithm on sign words extraction and recognition of continuous Persian sign language based on motion and shape features of hands
- (2016) Masoud Zadghorban et al. PATTERN ANALYSIS AND APPLICATIONS
- Continuous sign language recognition using level building based on fast hidden Markov model
- (2016) Wenwen Yang et al. PATTERN RECOGNITION LETTERS
- Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers
- (2015) Oscar Koller et al. COMPUTER VISION AND IMAGE UNDERSTANDING
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Towards subject independent continuous sign language recognition: A segment and merge approach
- (2013) W.W. Kong et al. PATTERN RECOGNITION
- Arabic sign language continuous sentences recognition using PCNN and graph matching
- (2012) M. F. Tolba et al. NEURAL COMPUTING & APPLICATIONS
- Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings
- (2010) Hee-Deok Yang et al. PATTERN RECOGNITION
- Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
- (2009) H.-D. Yang et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming
- (2009) R. Yang et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Modelling and segmenting subunits for sign language recognition based on hand motion analysis
- (2009) Junwei Han et al. PATTERN RECOGNITION LETTERS
- Sign Language Recognition by Combining Statistical DTW and Independent Classification
- (2008) J.F. Lichtenauer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More