融合Hu矩与BoF-SURF支持向量机的手势识别OA北大核心CSCDCSTPCD
Hand gesture recognition based on combining Hu moments and BoF-SURF support vector machine
基于尺度不变特征变换的特征包（BoF-SIFT）支持向量机的分类方法具有较好的手势识别效果，但是计算复杂度高、实时性较差。为此，提出了融合Hu矩与基于快速鲁棒特征的特征包（BoF-SURF）支持向量机（SVM）的手势识别方法。特征包模型中用快速鲁棒性特征（SURF）算法替换尺度不变特征变换（SIFT）算法提取特征，提高了实时性，并引入Hu矩描述手势全局特征，进一步提高识别率。实验结果表明，算法无论是实时性还是识别率都要高于BoF-SIFT支持向量机方法。
The classification method using bag of features-scale invariant feature transformation(BoF-SIFT) support vector machine got a better result on hand gesture recognition. However， it had a high computational complexity which results in the worse real-time p
SUI Yun-heng;GUO Yuan-shu
College of Information Engineering，Chang'an University，Xi'an 710064，ChinCollege of Information Engineering，Chang'an University，Xi'an 710064，Chin
信息技术与安全科学
手势识别特征包模型快速鲁棒特征Hu不变矩支持向量机
hand gesture recognitionbag-of-features modelspeeded up robust feature(SURF)Hu invariant momentssupport vector machine(SVM)
《计算机应用研究》 2014 (3)
953-956,960,5
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