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Examining Classifiers Applied to Static Hand Gesture Recognition in Novel Sound Mixing System

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:
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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