A Framework for Recognizing the Simultaneous Aspects of American Sign Language

By Christian Vogler, Dimitris N. Metaxas. Computer Vision and Image Understanding, 2001

364 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Developmental and Educational Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/b91b70dec0abfcd70889b4dd0cd45aede00500b5

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In Biomedical Engineering

In Developmental and Educational Psychology

In Human-Computer Interaction

Citations in the corpus

Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition (2002, methodology): “Subsequently, they proposed a method with HMM extensions to address the scale problem when the vocabulary size is increased [41].”
A review of hand gesture and sign language recognition techniques (2017, background): “An average accuracy of 94.23 and 84.85% for sign and sentence accuracy respectively are achieved [32].”
Segmenting sign language into motor primitives with Bayesian binning (2013, methodology): “For American Sign Language recognition, Vogler and Metaxas (1998) used a semi-supervised training scheme, which was extended to deal with two-hand signing using parallel HMMs in Vogler and Metaxas (1999, 2001).”
A REVIEW OF VISION BASED HAND GESTURES RECOGNITION (2009, background): “Sign language for the deaf (e.g. American Sign Language) is an example that has received significant attention in the gesture literature [13, 14, 15 and 16].”
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning (2005, background): “S denotes word start nodes (reprinted from [143] Copyright (2001), with permission from Elsevier).”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, background): “In parallel HMMs (PaHMMs) [75], the multiple chains of the state are modeled as separate HMMs, each with its own observation sequence.”
Recognition of group activities using dynamic probabilistic networks (2003, background): “Probabilistic graph models including both temporal sequential models such as Hidden Markov Models (HMMs) and static causal models such as Bayesian Belief Networks (BBNs) have received enormous attention in recent years for modelling and recognising visual behaviours of activities captured in video, ”