Real-time American Sign Language recognition from video using hidden Markov models
Thad Starner,Alex Pentland +1 more
- pp 265-270
TLDR
A real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers.Abstract:
Hidden Markov models (HMMs) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. We describe a real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers.read more
Citations
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Human activity analysis: A review
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TL;DR: This article provides a detailed overview of various state-of-the-art research papers on human activity recognition, discussing both the methodologies developed for simple human actions and those for high-level activities.
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TL;DR: A number of promising applications are identified and an overview of recent developments in this domain is provided, including work on whole-body or hand motion and the various methodologies.
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Pfinder: real-time tracking of the human body
TL;DR: Pfinder uses a multi-class statistical model of color and shape to obtain a 2-D representation of head and hands in a wide range of viewing conditions, useful for applications such as wireless interfaces, video databases, and low-bandwidth coding.
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Gesture Recognition: A Survey
Sushmita Mitra,T. Acharya +1 more
TL;DR: A survey on gesture recognition with particular emphasis on hand gestures and facial expressions is provided, and applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail.
References
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An introduction to hidden Markov models
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Book
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TL;DR: Robot Vision as discussed by the authors is a broad overview of the field of computer vision, using a consistent notation based on a detailed understanding of the image formation process, which can provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.
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Proceedings ArticleDOI
Recognizing human action in time-sequential images using hidden Markov model
Junji Yamato,J. Ohya,K. Ishii +2 more
TL;DR: The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.
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