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A fast static gesture recognition method

机译:一种快速的静态手势识别方法

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摘要

Gesture recognition has become a hot spot in human interaction technology. But the difference of skin colour, the complexity of the background and the rotation of the gesture make it difficult to complete gesture segmentation and recognition. To overcome the impact of the difference of skin colour, in this paper gesture images are split out based on skin colour in the hue saturation value (HSV) colour space combined with the mean-shift algorithm. Then the Hu invariable moments of the gesture images are calculated as the feature vector to overcome the impact of the rotation of gesture. In the experiment 660 images were collected from 10 experimenters. Three hundred and thirty images of those were made up by left-hand gestures and the rest were made up by right-hand gestures. The experimental results show the accuracy of this algorithm is from 90% to 100% with single-hand static gestures.
机译:手势识别已成为人类交互技术的热点。但是,肤色的差异,背景的复杂性以及手势的旋转使得难以完成手势分割和识别。为了克服肤色差异的影响,在本文中,基于肤色的色调饱和度值(HSV)颜色空间中的肤色与均值平移算法相结合,对手势图像进行了分割。然后将手势图像的Hu不变矩计算为特征向量,以克服手势旋转的影响。在实验中,从10位实验者那里收集了660张图像。其中的330张图像由左手手势组成,其余的则由右手手势组成。实验结果表明,该算法在单手静态手势下的准确度为90%至100%。

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