摘要
提出一种用于手势识别的新算法,使用卷积神经网络来进行手势的识别.该算法避免了手势复杂的前期预处理,可以直接输入原始的手势图像.卷积神经网络具有局部感知区域、层次结构化、特征抽取和分类过程等特点,在图像识别领域获得广泛的应用.试验结果表明,该方法能识别多种手势,精度较高且复杂度较小,具有很好的鲁棒性,也克服传统算法的诸多固有缺点.
The paper proposed a new algorithm used for hand gesture recognition which based on the convolutional neural network. The method not only avoids the hand gesture in the early period of the complex pretreatment, but also can directly input the gesture of original image. The convolutional neural network is characterized by local receptive field, hierarchical structure, global learning for feature extraction and classical. It has been applied to many image recognition tasks. Experimental results showed that the multi-class hand gestures can be recognized with high accuracy, small complexity and good robustness, while the inherent shortcomings of the traditional algorithm are overcame.
出处
《计算机系统应用》
2015年第4期113-117,共5页
Computer Systems & Applications
关键词
手势识别
卷积神经网络
局部感知
特征抽取
鲁棒性
hand gesture recognition
convolutional neural network
local receptive
feature extraction
good robustness
