Journal ArticleDOI
Dynamic-static unsupervised sequentiality, statistical subunits and lexicon for sign language recognition
TLDR
A new computational phonetic modeling framework for sign language (SL) recognition based on dynamic-static statistical subunits and provides sequentiality in an unsupervised manner, without prior linguistic information is introduced.About:
This article is published in Image and Vision Computing.The article was published on 2014-08-01. It has received 46 citations till now. The article focuses on the topics: Handshape & Sign language.read more
Citations
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Journal ArticleDOI
Recent methods and databases in vision-based hand gesture recognition
TL;DR: A review of vision-based hand gesture recognition algorithms reported in the last 16 years using RGB and RGB-D cameras and qualitative and quantitative comparisons of algorithms are provided.
Journal ArticleDOI
Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition
TL;DR: The construction of a more robust system-an accelerometer glove-as well as its application in the recognition of sign language gestures with a described method based on Hidden Markov Model (HMM) and parallel HMM approaches are presented.
Posted Content
Quantitative Survey of the State of the Art in Sign Language Recognition.
TL;DR: This study compiles the state of the art in a concise way to help advance the field and reveal open questions, such as shifts in the field from intrusive to non-intrusive capturing, from local to global features and the lack of non-manual parameters included in medium and larger vocabulary recognition systems.
Book ChapterDOI
Multimodal gesture recognition via multiple hypotheses rescoring
TL;DR: The overall approach achieves 93.3% gesture recognition accuracy in the ChaLearn Kinect-based multimodal data set, significantly outperforming all recently published approaches on the same challenging multi-modalities gesture recognition task, providing a relative error rate reduction of at least 47.6%.
Journal ArticleDOI
Continuous sign language recognition using level building based on fast hidden Markov model
Wenwen Yang,Jinxu Tao,Zhongfu Ye +2 more
TL;DR: Hidden Markov model (HMM) is used to calculate the similarity between the sign model and testing sequence, and a fast algorithm for computing the likelihood of HMM is proposed to reduce the computation complexity.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Hierarchical Grouping to Optimize an Objective Function
TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.