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Neural Computing and Applications

Erschienen in:

01.09.2014 | Original Article

RETRACTED ARTICLE: Feature extraction and ML techniques for static gesture recognition

verfasst von: Haitham Badi, Sabah Hasan Hussein, Sameem Abdul Kareem

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2014

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Abstract

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.

Vorheriger Artikel A developed distance method for ranking generalized fuzzy numbers

Nächster Artikel Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances

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Titel: RETRACTED ARTICLE: Feature extraction and ML techniques for static gesture recognition
verfasst von: Haitham Badi
Sabah Hasan Hussein
Sameem Abdul Kareem
Publikationsdatum: 01.09.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1540-6

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