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Janoch, A., Karayev, S., Jia, Y., Barron, J., Fritz, M., Saenko, K. and Darrell, T. (2013) A Category-Level 3D Object Dataset: Putting the Kinect to Work. In: Fossati, A., Gall, J., Grabner, H., Ren, X.F. and Konolige, K., Eds., Consumer Depth Cameras for Computer Vision, Springer London, London, 141-165.
http://dx.doi.org/10.1007/978-1-4471-4640-7_8

has been cited by the following article:

• TITLE: Hand Gesture Recognition Using Appearance Features Based on 3D Point Cloud

  AUTHORS: Yanwen Chong, Jianfeng Huang, Shaoming Pan

  KEYWORDS: Human-Computer-Interaction, Gesture Recognition, 3D Point Cloud, Depth Image

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.9 No.4, April 18, 2016

  ABSTRACT: This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.