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Static Hand Gesture Recognition for Human Computer Interaction

机译:人机交互的静态手势识别

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摘要

This study presents a novel algorithm to recognize a set of static hand gestures for the Human-Computer Interaction (HO), based on hand segmentation using both wavelet network for images feature extraction, and supervised feed-forward neural network with back propagation training algorithm for recognition. One hundred and twenty hand gesture images were used for training and 60 for testing. The best classification rate of 97% was obtained for the testing set.
机译:这项研究提出了一种新的算法,该算法基于小波网络用于图像特征提取的手分割以及带反向传播训练算法的有监督前馈神经网络进行手分割,从而识别人机交互(HO)的一组静态手势。承认。一百二十个手势图像用于训练,六十个图像用于测试。测试集的最佳分类率为97%。

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