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Abstract
The main objective of the chapter is to present the methodology and results of examining various classifiers (Nearest Neighbor-like algorithm with non-nested generalization (NNge), Naive Bayes, C4.5 (J48), Random Tree, Random Forests, Artificial Neural Networks (Multilayer Perceptron), Support Vector Machine (SVM) used for static gesture recognition. A problem of effective gesture recognition is outlined in the context of the system based on a camera and a multimedia projector enabling a user to process sound in audio mixing domain by hand gestures. The image processing method and hand shape parameterization method are described in relation to the specificity of the input and data classifiers. The SVM classifier is considered the optimum choice for the engineered gesture-based sound mixing system.
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Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Published in:
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Advances in Intelligent Systems and Computing
no. 183,
pages 77 - 86,
ISSN: 2194-5357 - Title of issue:
- Multimedia and Internet systems : Theory and Practice strony 77 - 86
- Language:
- English
- Publication year:
- 2013
- Bibliographic description:
- Lech M., Kostek B., Czyżewski A.: Examining Classifiers Applied to Static Hand Gesture Recognition in Novel Sound Mixing System// Multimedia and Internet systems : Theory and Practice/ ed. A. Zgrzywa, K. Choroś, A. Siemiński : Springer, 2013, s.77-86
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-642-32335-5_8
- Verified by:
- Gdańsk University of Technology
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