CAJ | 학술논문

提出了一种基于混合高斯模型的隐马尔科夫模型（ GMM－HMM ）与手势轮廓特征的单幅手势图像识别方法，该方法采用YCrCb空间阈值处理对手势图像二值化处理，针对理想感兴趣区域提出了一种还原最上层轮廓的新型轮廓算法。将每类手势轮廓特征作为HMM的观察值分别训练对应手势的HMM参数，建立所有手势的HMM模型。分别用Viterbi算法计算测试集数据与每个模型的条件概率来获得识别结果。实验结果表明，该方法不仅对手势库内的特定人的静态手势识别具有较好的效果，且对提取的其他人的静态手势图像识别率也较高。
제출료일충기우혼합고사모형적은마이과부모형（ GMM－HMM ）여수세륜곽특정적단폭수세도상식별방법，해방법채용YCrCb공간역치처리대수세도상이치화처리，침대이상감흥취구역제출료일충환원최상층륜곽적신형륜곽산법。장매류수세륜곽특정작위HMM적관찰치분별훈련대응수세적HMM삼수，건립소유수세적HMM모형。분별용Viterbi산법계산측시집수거여매개모형적조건개솔래획득식별결과。실험결과표명，해방법불부대수세고내적특정인적정태수세식별구유교호적효과，차대제취적기타인적정태수세도상식별솔야교고。
Based on Gaussian -mixture Hidden Markov Model ( GMM-HMM) and gesture contour feature , this paper proposed a method to recognize gesture from a single image .The YCrCb color space threshold processing was adopted to get the binary image of the original RGB image , and then a new algorithm was presented to restore the most ideal contour .As an observation vector , it is used to train hidden Markov model parameters related to the corre-sponding gesture , so all the gestures ’ HMM model can be established .The gesture can be recognized by the Viterbi algorithm through calculating the conditional probability which de-scribes the relationship between test set and each gesture ’ s model.The experimental results showed that the method performed well for the specific static gesture images from the gesture library, and it was also effective for the images taken by ourselves .