Abstract
This article describes the benefit of Contourlet transform and discrete wavelet for hand gesture recognition system applied for static and dynamic data sets. For this purpose, One-against-all SVM and RBF neural network classifiers are applied, where several tests are performed for recognition process to improve the global efficiency. Three data set are used to demonstrate our study. Good recognition rate was obtained for the different architectures where 93% of efficiency was achieved.
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Ferhat, R., Chelali, F.Z., Agab, S.e. (2021). Static and Dynamic Hand Gesture Recognition System Using Contourlet Transform. In: Ben Ahmed, M., Rakıp Karaș, İ., Santos, D., Sergeyeva, O., Boudhir, A.A. (eds) Innovations in Smart Cities Applications Volume 4. SCA 2020. Lecture Notes in Networks and Systems, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-66840-2_64
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DOI: https://doi.org/10.1007/978-3-030-66840-2_64
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