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方亦凯1, 程 健1, 汪孔桥2, 卢汉清1(1.中国科学院自动化研究所模式识别国家重点实验室, 北京 100190;2.诺基亚中国研究中心, 北京 100176)

摘 要
A Hand Gesture Recognition Method with Fast Scale-space Feature Detection

FANG Yikai1, CHENG Jian1, WANG Kongqiao2, LU Hanqing1(1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190;2.Nokia Research Center, Beijing 100176)

Scale-space feature detection is one of the most frequently used method in hand gesture recognition based on geometric model. However, the traditional method of scale-space feature detection involves heavy computation of Gaussian convolution, which makes the detection and recognition time-costly. In this paper, a fast scale-space feature detection method is proposed. First, a series of simple rectangular feature templates are used to approximate the complicated Gaussian derivatives convolution templates, with which the fast detectors of scale-space geometric features are obtained. After the detection of blob and ridge structures in gesture image, palm and finger structures are described and then gesture recognition is performed according to the configuration of palm and fingers. Then, integral image is used to rapidly calculate the convolution of rectangular feature templates, so the detection of scale-space geometric features is greatly accelerated in the method. Experiments on the standard dataset and the natural scene dataset show that the proposed method significantly reduces the time cost of gesture recognition while keeping comparable accuracy with traditional method.