Andreas Schmid
Raphael Wimmer
Stefan Lippl
ABSTRACT
Inside-out optical 2D tracking of tangible objects on a surface oftentimes uses a high-resolution pattern printed on the surface. While De-Bruijn-torus patterns offer maximum information density, their orientation must be known to decode them. Determining the orientation is challenging for patterns with very fine details; traditional algorithms, such as Hough Lines, do not work reliably. We show that a convolutional neural network can reliably determine the orientation of quasi-random bitmaps with 6 × 6 pixels per block within 36 × 36 pixel images taken by a mouse sensor. Mean error rate is below 2°. Furthermore, our model outperformed Hough Lines in a test with arbitrarily rotated low-resolution rectangles. This implies that CNN-based rotation-detection might also be applicable for more general use cases.
Supplemental Material
Available for Download
- Richard O. Duda and Peter E. Hart. 1972. Use of the Hough Transformation to Detect Lines and Curves in Pictures. Commun. ACM 15, 1 (jan 1972), 11–15. https://doi.org/10.1145/361237.361242Google Scholar
Digital Library
- C. T. Fan, S. M. Fan, S. L. Ma, and M. K. Siu. 1985. On De Bruijn arrays. Ars Combinatoria 19, MAY (1985), 205–213.Google Scholar
- Dennis Schüsselbauer, Andreas Schmid, and Raphael Wimmer. 2021. Dothraki: Tracking Tangibles Atop Tabletops Through De-Bruijn Tori. In Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction(Salzburg, Austria) (TEI ’21). Association for Computing Machinery, New York, NY, USA, Article 37, 10 pages. https://doi.org/10.1145/3430524.3440656Google ScholarDigital Library
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