基于PCA降维结合机器学习算法的人机交互手势识别研究

更加自然和灵活的手势识别技术正逐渐成为智能移动机器人控制的重要人机接口。为了进一步提高基于计算机视觉的机器人导航控制的实时性和精度,提出了一种基于主成分分析(Principal Component Analysis,PCA)降维结合机器学习算法的手势识别方法。首先,对视觉摄像头捕获的手势图像进行预处理,具体包括图像二值化、中值滤波和形态学变换。然后通过PCA提取主要特征并对数据进行降维。最后结合机器学习中自组织神经网络(Self-Organizing Feature Maps,SOM)作为分类器应用于手势识别,具体采用的是学习向量量化(Learning Vector Quantization,LVQ)神经网络。静态手势实验测试结果表明:相比网络和K-means算法,提出方法缩短了手势识别时间,且识别准确率得到有效提高,验证了方法的有效性。

Research on human-computer interaction gesture recognition based on PCA dimensionality reduction and machine learning algorithm

More natural and flexible gesture recognition technology is gradually becoming an important human-machine interface for intelligent mobile robot control.In order to further improve the real-time and accuracy of robot vision control based on computer vision,a gesture recognition method based on Principal Component Analysis(PCA)dimension reduction combined with machine learning algorithm is proposed.First,the gesture image captured by the vision camera is preprocessed,including image binarization,median filtering,and morphological transformation.Then the background subtraction is used for feature extraction,and then the main features will be extracted by PCA and the data is reduced.Finally,combined with the more advanced self-organizing neural network in machine learning as a classifier,it is applied to gesture recognition.The static gesture experiment results show that compared with BP neural network and K-means algorithm,the proposed method could shorten the gesture recognition time and the recognition accuracy will be effectively improved.Therefore,the effectiveness of the proposed method has been verified.