Hand gesture recognition using a real-time tracking method and hidden Markov models

By Feng-Sheng Chen, Chih-Ming Fu, Chung-Lin Huang. Image and Vision Computing, 2003

558 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/382b606bf3b421a7cd3181c78abb65925367c2d9

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation (2009, methodology): “…[26], [27], [29] are based on extensions of DP-based methods for isolated gestures, e.g., Dynamic Time Warping (DTW) [34], [35], Continuous DP (CDP) [29], various forms of hidden Markov models (HMMs) [6], [13], [16], [27], [36], [37], [38], and, most recently, Conditional Random Fields [39], [40].”
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning (2005, methodology): “Two-dimensional tracking can be performed using blobbased ([67], [123], [135]), view-based ([66]), or hand contour/ boundary models ([25], [33], [65]), or by matching motion segmented regions ([158]).”
A review of hand gesture and sign language recognition techniques (2017, methodology): “Chen et al. [33] performed skin color segmentation in RGB color space, using the rule of R > G > B and matching with pre-stored sample skin color to find the skin color.”
Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network (2016, background): “Using a camera system, overall accuracies of more than 90% were obtained for 20 gestures [2].”
3D Gestural Interaction: The State of the Field (2013, background): “Other work on 3D gesture recognizers that incorporate HMMs include [62–69].”
One-shot learning gesture recognition from RGB-D data using bag of features (2013, methodology): “Next, to speed up processing time, we can achieve fast feature extraction on a Graphics Processing Unit (GPU) (Chen et al., 2003).”
Recent methods and databases in vision-based hand gesture recognition: A review (2015, background): “[14] tilized Fourier descriptor and optical flow based motion analysis o characterize spatial and temporal features respectively.”
Challenges in multimodal gesture recognition (2016, background): “, 1996; Kruskal and Liberman, 1983), Continuous Dynamic Programming (CDP) (Oka, 1998), various forms of Hidden Markov Models (HMMs) (Brand et al., 1997; Chen et al., 2003; Stefanov et al., 2005; Lee and Kim, 1999; Starner and Pentland, 1998; Vogler and Metaxas, 1999; Wilson and Bobick, 1999), and mo”
A REVIEW OF VISION BASED HAND GESTURES RECOGNITION (2009, background): “Chen et al. [44] introduce a hand gesture recognition system to recognize continuous gestures before stationary background.”