Raydiance: A Tangible Interface for Teaching Computer Vision

  • Paul Reimer
  • Alexandra Branzan Albu
  • George Tzanetakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)


This paper presents a novel paradigm for prototyping Computer Vision algorithms; this paradigm is suitable for students with very limited programming experience. Raydiance includes a tangible user interface controlled by a spatial arrangement of physical tokens which are detected using computer vision techniques. Constructing an algorithm is accomplished by creating a directed graph of token connections. Data is processed, then propagated from one token to another by using a novel Light Ray metaphor. Our case study shows how Raydiance can be used to construct a computer vision algorithm for a particular task.


Computer Vision Computer Vision System Tangible Interface Computer Vision Technique Computer Vision Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
  2. 2.
  3. 3.
    Bencina, R., Kaltenbrunner, M.: The design and evolution of fiducials for the reactivision system. In: Proceedings of the 3rd International Conference on Generative Systems in the Electronic Arts (3rd Iteration 2005), Melbourne, Australia (2005)Google Scholar
  4. 4.
    Bencina, R., Kaltenbrunner, M.: libfidtrack fiducial tracking library (2009),
  5. 5.
    Bencina, R., Kaltenbrunner, M., Jorda, S.: Improved topological fiducial tracking in the reactivision system. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) - Workshops. IEEE Computer Society, Washington, DC (2005)Google Scholar
  6. 6.
    Costanza, E., Robinson, J.: A region adjacency tree approach to the detection and design of fiducials. In: Video, Vision and Graphics, pp. 63–69 (2003)Google Scholar
  7. 7.
    Costanza, E., Shelley, S.B., Robinson, J.: Introducing audio d-touch: A tangible user interface for music composition. In: 6th Intl. Conference on Digital Audio Effects, (DAFX-03) (2003)Google Scholar
  8. 8.
    Gomez, G., Morales, E.F.: Automatic feature construction and a simple rule induction algorithm for skin detection. In: Proc. of the ICML Workshop on Machine Learning in Computer Vision, pp. 31–38 (2002)Google Scholar
  9. 9.
    Johnston, W.M., Hanna, J.R.P., Millar, R.J.: Advances in dataflow programming languages. ACM Computer Survey 36(1), 1–34 (2004)CrossRefGoogle Scholar
  10. 10.
    Jordà, S., Geiger, G., Alonso, M., Kaltenbrunner, M.: The reactable: Exploring the synergy between live music performance and tabletop tangible interfaces. In: Proceedings Intl. Conf. Tangible and Embedded Interaction, TEI (2007)Google Scholar
  11. 11.
    Lomker, F., Wrede, S., Hanheide, M., Fritsch, J.: Building modular vision systems with a graphical plugin environment. In: International Conference on Computer Vision Systems, p. 2 (2006)Google Scholar
  12. 12.
    Morrison, J.P.: Data responsive modular, interleaved task programming system vol. 13(8) (January 1971)Google Scholar
  13. 13.
    toxmeister. Fid.gen reactivision fiducial generator (2009),
  14. 14.
    Zhang, K., Song, G.-L., Kong, J.: Rapid software prototyping using visual language techniques. In: IEEE International Workshop on Rapid System Prototyping, pp. 119–126 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paul Reimer
    • 1
  • Alexandra Branzan Albu
    • 1
  • George Tzanetakis
    • 1
  1. 1.University of VictoriaVictoriaCanada

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