2013
DOI: 10.1007/978-3-642-32335-5_8
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Examining Classifiers Applied to Static Hand Gesture Recognition in Novel Sound Mixing System

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Cited by 7 publications
(4 citation statements)
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“…These features are then fed into a SVM for gesture recognition. Other work that has explored the use of decision forests for 3D gesture recognition include [Fothergill et al 2012;Keskin et al 2012;Lech et al 2013]. …”
Section: Decision Trees and Forestsmentioning
confidence: 99%
“…These features are then fed into a SVM for gesture recognition. Other work that has explored the use of decision forests for 3D gesture recognition include [Fothergill et al 2012;Keskin et al 2012;Lech et al 2013]. …”
Section: Decision Trees and Forestsmentioning
confidence: 99%
“…Wu et al compared their frame-based descriptor and multi-class SVM to dynamic time warping, a naive Bayes classifier, C4.5 decision trees, and HMMs and showed their approach has better performance compared to the other methods for both user dependent (95.2%) and user independent cases (89.3%) for 12 gestures [Wu et al 2009]. Lech et al compared a variety of different recognition systems for building a sound mixing gestural interface [Lech et al 2013]. They compared a nearest neighbor algorithm with nested generalization, naive Bayes, C4.5 decision trees, random trees, decision forests, neural networks, and SVMs on a set of four gestures and found the SVM to be the best approach for their application.…”
Section: Experimentation and Accuracymentioning
confidence: 99%
“…These features are then fed into a SVM for gesture recognition. Other work that has explored the use of decision forests for 3D gesture recognition include [110,117,118].…”
Section: Zhang Et Al Combined Decision Trees With Multistreammentioning
confidence: 99%
“…Wu et al compared their frame-based descriptor and multiclass SVM to dynamic time warping, a naive Bayes classifier, C4.5 decision trees, and HMMs and showed their approach has better performance compared to the other methods for both user dependent (95.2%) and user independent cases (89.3%) for 12 gestures [100]. Lech et al compared a variety of different recognition systems for building a sound mixing gestural interface [118]. They compared the nearest neighbor algorithm with nested generalization, naive Bayes, C4.5 decision trees, random trees, decision forests, neural networks, and SVMs on a set of four gestures and found the SVM to be the best approach for their application.…”
Section: Authormentioning
confidence: 99%