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A Practical Approach for Sign Language’s Static Gesture Recognition: Using Backpropagation Training Algorithm and Skin Color Modeling Method - Couverture souple
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Présentation de l'éditeur :
This book discussed an artificial neural network based approach for static hand gestures recognition of a sign language system. The backpropagation learning algorithm is used to train the multilayer perceptron Perceptron network.The skin color modeling method is used for differentiating between the skin and non-skin colors. The main objective of this book is to present the systematic and easy to understand implementation details of the development of new perceptual interfaces for human-computer-interaction based on visual input captured by computer vision systems.The whole concept of static gesture recognition system is explained using image data set of hand gestures used for the representation of numeric digits.
Biographie de l'auteur :
Dr. Dilip Singh Sisodia received the BE and M.Tech. degrees respectively in computer science & engineering and information technology from the RGPV, Bhopal, India. He received the Ph.D. degree in computer science and engineering from NIT Raipur.
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- ÉditeurScholars' Press
- Date d'édition2017
- ISBN 10 6202304464
- ISBN 13 9786202304467
- ReliureBroché
- Nombre de pages100
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A Practical Approach for Sign Language¿s Static Gesture Recognition
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Scholars' Press Dez 2017
(2017)
ISBN 10 : 6202304464
ISBN 13 : 9786202304467
Neuf
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Description du livre Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book discussed an artificial neural network based approach for static hand gestures recognition of a sign language system. The backpropagation learning algorithm is used to train the multilayer perceptron Perceptron network.The skin color modeling method is used for differentiating between the skin and non-skin colors. The main objective of this book is to present the systematic and easy to understand implementation details of the development of new perceptual interfaces for human-computer-interaction based on visual input captured by computer vision systems.The whole concept of static gesture recognition system is explained using image data set of hand gestures used for the representation of numeric digits. 100 pp. Englisch. N° de réf. du vendeur 9786202304467
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EUR 59,90
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A Practical Approach for Sign Language¿s Static Gesture Recognition : Using Backpropagation Training Algorithm and Skin Color Modeling Method
Edité par
Scholars' Press
(2017)
ISBN 10 : 6202304464
ISBN 13 : 9786202304467
Neuf
Taschenbuch
Quantité disponible : 1
Vendeur :
Evaluation vendeur
Description du livre Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book discussed an artificial neural network based approach for static hand gestures recognition of a sign language system. The backpropagation learning algorithm is used to train the multilayer perceptron Perceptron network.The skin color modeling method is used for differentiating between the skin and non-skin colors. The main objective of this book is to present the systematic and easy to understand implementation details of the development of new perceptual interfaces for human-computer-interaction based on visual input captured by computer vision systems.The whole concept of static gesture recognition system is explained using image data set of hand gestures used for the representation of numeric digits. N° de réf. du vendeur 9786202304467
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A Practical Approach for Sign Language\ s Static Gesture Recognition
Edité par
Scholars\' Press
(2017)
ISBN 10 : 6202304464
ISBN 13 : 9786202304467
Neuf
Couverture souple
Quantité disponible : > 20
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Evaluation vendeur
Description du livre Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sisodia Dilip SinghDr. Dilip Singh Sisodia received the BE and M.Tech. degrees respectively in computer science & engineering and information technology from the RGPV, Bhopal, India. He received the Ph.D. degree in computer science . N° de réf. du vendeur 385940758
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