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.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-642-32335-5_8
- Category
- Publikacja monograficzna
- Type
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language
- angielski
- Publication year
- 2013
