CAJ | 학술논문

针对目前复杂背景下手势图像识别率不高、识别困难等问题,基于ELM( extreme learning machine),提出了一种快速手势识别方法。结合RGB与HSV两种颜色空间模型,从复杂背景中去除大部分类肤色的干扰,实现手势分割；采用改进的Hu不变矩以及指尖个数对获取的手势轮廓进行描述；利用ELM进行特征数据分类,从而实现实验所采用手势的识别,其中ELM是在单隐层神经网络的基础上提出来的一种新型前馈神经网络,网络结构比较简单,输入权值和偏差随机给定的。在采用ELM识别的同时又用传统的BP网络进行了识别,结果表明：相对于BP网络,ELM具有较快的学习速度和良好的抗差能力,同时识别率比较高,适合静态手势识别。
침대목전복잡배경하수세도상식별솔불고、식별곤난등문제,기우ELM( extreme learning machine),제출료일충쾌속수세식별방법。결합RGB여HSV량충안색공간모형,종복잡배경중거제대부분류부색적간우,실현수세분할；채용개진적Hu불변구이급지첨개수대획취적수세륜곽진행묘술；이용ELM진행특정수거분류,종이실현실험소채용수세적식별,기중ELM시재단은층신경망락적기출상제출래적일충신형전궤신경망락,망락결구비교간단,수입권치화편차수궤급정적。재채용ELM식별적동시우용전통적BP망락진행료식별,결과표명：상대우BP망락,ELM구유교쾌적학습속도화량호적항차능력,동시식별솔비교고,괄합정태수세식별。
Aiming at the problems of low image recognition rate and difficulty in gesture recognition under complicated background, a rapid gesture recognition method based on ELM ( extreme learning machine) is proposed. Firstly, by combining RGB and HSV color space model and removing most classification from complex background color interference, gesture segmentation is achieved. Then, the improved geometric moment invariants and the number of fingers are used to describe the ob-tained gesture contour. Finally the generated characteristics data are classified by using ELM, so as to realize gesture recognition. ELM is a new type of feed forward neural networks which is proposed on the basis of single-hidden layer neural network. ELM's network structure is simpler, and the in-put weights and bias are randomly given. Recognition using ELM and recognition using traditional BP network are experimented respectively. The results shows that ELM is suitable for static gesture recognition, for it has faster learning speed and better resistance, and the recognition rate is higher, compared with BP network.