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2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011) Max-Pooling Convolutional Neural Networks for Vision-based Hand Gesture Recognition Jawad Nagi∗ , Frederick Ducatelle∗ , Gi
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Document Date: 2012-10-05 08:46:42

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File Size: 3,74 MB

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Beijing / Amsterdam / Applications / Brussels / Kuala Lumpur / /

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Bayer / Neural Information Processing Systems / Neural Networks / 1st ACM Intl / Industrial Electronics / IEEE Intl / Embedded Software / Convolutional Neural Networks / Artificial Neural Networks / E. Tuci A. E. / /

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Belgium / Singapore / /

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Spatial Pyramid / University Tenaga Nasional Putrajaya / Dalle Molle Institute / Pyramid of Histogram Oriented Gradients / University of Lugano / /

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ubiquitous computing / online testing / real-time gesture recognition performance / real-time performance / on-line gradient descent / minimal pre-processing / handgesture applications / real-world applications / swarm robotic systems / realworld gesture recognition applications / 533MHz processor / morphological image processing / classification tools / real-time gesture recognition / trained using on-line gradient descent / /

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Linux / /

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World Academy of Science / Engineering and Technology / Department of Mechanical Engineering / Dalle Molle Institute / National Centre of Competence / European Union / Swiss National Science Foundation / University of Lugano / /

Person

J. Roberts / V / Y. Wu / T. St¨utzle / V / Frederick Ducatelle / W. Xu / Y. Gong / Luca Maria Gambardella / Max-Pooling Convolutional / Jawad Nagi / J. Guzzi / V / J. Fan / Alessandro Giusti / Dan Cires / A. Stefan / V / /

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plain feed-forward / /

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SVMs / /

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R / J / C++ / /

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RAM / virtual reality / Linux / Information Technology / Image Processing / 11 533MHz processor / PDF / Knowledge Management / gesture recognition system / neural network / digital cameras / Gesture Recognition / /

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