Journal ArticleDOI
Recognition of dynamic hand gestures
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TLDR
A recognition engine is developed which can reliably recognize these gestures despite individual variations and has the ability to detect start and end of gesture sequences in an automated fashion.About:
This article is published in Pattern Recognition.The article was published on 2003-09-01. It has received 167 citations till now. The article focuses on the topics: Gesture recognition & Pattern recognition (psychology).read more
Citations
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Proceedings ArticleDOI
Dynamic gesture recognition using machine learning techniques and factors affecting its accuracy
TL;DR: This work uses Kinect, a motion sensing input device for gaming consoles, to make learning a fun activity for children by automatically recognizing and classify their dynamic hand gestures into predefined shapes, namely; rectangles, triangles, and circles.
Dissertation
Learning, Recognizing and Early Classification of Spatio-Temporal Patterns using Spike Timing Neural Networks
TL;DR: Although the proposed approaches in this dissertation are unsupervised, they outperform other state-of-the-art and in some cases, provide comparable results with other methods.
Modèles de Markov Cachés (HMM) pour de la reconnaissance de gestes humains
TL;DR: Dans le cadre of ce stage, nous avons compare differents modeles de Markov caches (HMM) dont un modele experimental avec fonction de densite non parametrique avec ceux obtenus via un autre type of classifieur (SVM).
Journal ArticleDOI
Dynamic and Static Gesture Recognition System Using Moments
TL;DR: This project strives to enhance the reliability and efficiency by using faster static gesture recognition algorithm by using SPHINX parser to form word from set of letters.
Dissertation
A fuzzy framework for human hand motion recognition
TL;DR: A novel fuzzy framework of three proposed recognition algorithms, using numerical values, Gaussian pattern and data dependency structure respectively in the context of optimal real-time human hand motion recognition, which outperform Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM) in terms of both effectiveness and efficiency criteria.
References
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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
Active shape models—their training and application
TL;DR: This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
Journal ArticleDOI
Visual interpretation of hand gestures for human-computer interaction: a review
TL;DR: A fraction of the recycle slurry is treated with sulphuric acid to convert at least some of the gypsum to calcium sulphate hemihydrate and the slurry comprising hemihYDrate is returned to contact the mixture of phosphate rock, phosphoric acid and recycle Gypsum slurry.
Proceedings ArticleDOI
Real-time American Sign Language recognition from video using hidden Markov models
Thad Starner,Alex Pentland +1 more
TL;DR: 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.
Journal ArticleDOI
An HMM-based threshold model approach for gesture recognition
Hyeon-Kyu Lee,Jin Hyung Kim +1 more
TL;DR: A new method is developed using the hidden Markov model (HMM) based technique that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns.