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Recognition of separate hand gestures by Time-Delay Neural Networks based on multi-state spectral image patterns from cyclic hand movements

机译:时延神经网络基于来自周期性手部运动的多状态光谱图像模式识别单独的手势

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In recent years video-basedanalysis of human motion has gained increased interest, which for a large part is due to the ongoing rapid developments of computer and camera hardware, such as increased CPU power, fast and modular interfaces and high-quality image digitisation. A similar important role is played by the development of powerful approaches for the analysis of visual data from video sources. In computer music this development is reflected in a row of applications approaching the analysis of video and image data for gestural control of music and sound, such as Eyesweb, Jitter, openCV, Gem, [1], [2], [3], [4].
机译:近年来,基于视频的人体运动分析越来越引起人们的兴趣,这在很大程度上是由于计算机和相机硬件的持续快速发展,例如CPU功能的增强,快速和模块化的接口以及高质量的图像数字化。类似的重要作用是通过开发强大的方法来分析来自视频源的视觉数据而发挥的。在计算机音乐中,这种发展体现在处理视频和图像数据以进行音乐和声音手势控制的一系列应用程序中,例如Eyesweb,Jitter,openCV,Gem,[1],[2],[3], [4]。

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