摘要
基于计算机视觉的手势识别是当前人机交互领域的热门研究,但由于受光照、环境等因素的影响,使得采用单一特征描述手势的方法不能很好地识别手势,因此提出一种将Hu不变矩和指尖个数特征相结合的静态手势识别方法,对采集的手势图像进行预处理,再使用肤色模型分割出手势,并采用重心距离法检测指尖个数,进而对提取的手势轮廓进行Hu值的计算,最后采用模板匹配法对特征距离进行加权和融合来识别手势。实验结果表明,与采取单一的Hu矩或指尖个数作为手势特征的方法相比,该方法可以获得更高的识别率。
Gesture recognition based on computer vision has become a hot research topic.But due to the influence of illumination,environment and other factors,method based on single feature can't identify gestures well.Therefore a method combined Hu invariant moments with number of fingertips as features of static gestures was proposed.After preprocessing the collected static gesture images,a kind of skin color model was applied to segment gesture,then the numbers of fingertips were detected by centroid distance method,and then the Hu values of the extracted gesture contour were calculated.Next the number of fingertips and Hu values were weighted respectively as gesture features.Finally the template matching was used to recognize gestures by weighting and fusing feature distance.Experimental results show that the proposed way can obtain a higher recognition rate of ten kinds of gestures than traditional Hu invariant moments feature recognition method and fingertip detection recognition method.
出处
《计算机科学》
CSCD
北大核心
2017年第S1期220-223,共4页
Computer Science
基金
四川省科技厅应用基础项目(2014JY0208)
西南石油大学科研起航计划(2014QHZ026)
四川省教育厅科研项目(14ZB0051)资助
关键词
静态手势
肤色模型
重心距离法
指尖
HU不变矩
模板匹配
Static gesture
Skin color model
Centroid distance method
Fingertip
Hu invariant moments
Template matching
