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The Literature Survey on Virtual Piano
2020
International Journal of Engineering Research and
This paper presents an efficient data-driven approach to track fingertip and detect finger tapping for virtual piano using an RGB-D camera. We collect 7200 depth images covering the most common finger articulation for playing piano, and train a random regression forest using depth context features of randomly sampled pixels in training images. In the online tracking stage, we firstly segment the hand from the plane in contact by fusing the information from both color and depth images. Then we
doi:10.17577/ijertv9is040405
fatcat:cohbnqipr5ftdjulci2nkgqspa