摘要
针对手势受缩放、旋转的影响导致识别率低的问题,提出了一种基于手势几何分布的特征提取方法用于手势识别。首先对分割后的手势图像进行归一化,并计算手势主方向和手势轮廓的最小外接矩形的宽长比,利用相似度函数进行初步识别,筛选出部分候选手势;再利用轮廓分割法统计手势轮廓点在极坐标内的分布情况,使用修正Hausdorff距离作为相似性度量的方法识别出最终手势。实验结果表明,所提方法能够快速且准确地识别各类手势,平均识别率达到92.89%,误识率降低到3.53%,识别速度较同类算法提高了4.2倍。
Aiming at the problem that gestures are affected by scaling and rotation,resulting in low recognition rate,this paper proposed a feature extraction method based on hand geometric distribution for gesture recognition.Firstly,the segmented gesture image is normalized.Secondly,width-to-length ratio of the minimum circumscribed rectangle of gesture main direction and gesture contour is calculated,and the similarity function is used as preliminary recognition to select some candidate gestures.Finally,contour segmentation method is used to estimate the distribution of gesture contour points in polar coordinates and the modified Hausdorff distance is used as a similarity measure method to identify the final gesture.The experimental results show that the proposed method can identify various gestures quickly and accurately,the average recognition rate reaches 92.89%,the false recognition rate is reduced to 3.53%,and the recognition speed is 4.2 times higher than that of similar algorithms.
作者
韩笑
张晶
李月龙
HAN Xiao;ZHANG Jing;LI Yue-long(School of Computer Science and Software,Tianjin Polytechnic University,Tianjin 300387,China)
出处
《计算机科学》
CSCD
北大核心
2019年第B06期246-249,262,共5页
Computer Science
基金
中国博士后科学基金(2015M570228)资助
关键词
手势主方向
几何分布
特征提取
轮廓分割
修正Hausdorff距离
手势识别
Gesture main direction
Geometric distribution
Feature extraction
Contour segmentation
Modified Hausdorff distance
Gesture recognition
