• Corpus ID: 113442873

Neural Learning Methods for Human-Computer Interaction

@inproceedings{Kopinski2016NeuralLM,
  title={Neural Learning Methods for Human-Computer Interaction},
  author={Thomas Kopinski},
  year={2016}
}
This thesis aims at improving the complex task of hand gesture recognition by utilizing machine learning techniques to learn from features calculated from 3D point cloud data. The main contributions of this work are embedded in the domains of machine learning and in the human-machine interaction. Since the goal is to demonstrate that a robust real-time capable system can be set up which provides a supportive means of interaction, the methods researched have to be light-weight in the sense that… 

Hand Gesture Recognition in Automotive Human–Machine Interaction Using Depth Cameras

This review describes current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors and confirms that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results.

References

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  • Eshed Ohn-BarM. Trivedi
  • Computer Science
    2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
  • 2013
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