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Stereophotogrammetric real-time 3D machine vision

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Abstract

Signal-processing algorithms often have to be strongly modified in case of hardware implementation. Especially, continuous real-time image processing at high speed is a challenging task. In this paper, a hardware/software codesign is applied to a stereophotogrammetric system. For calculations of a depth map, an optimized algorithm is implemented as a hierarchical parallel hardware solution. By dividing the measuring range into subranges and switching between them, real-time 3D measurements within a wide measuring range is possible. Object clustering and tracking are realized in a processor. The density distribution of the disparity in the depth map (disparity histogram) is used for object detection. A Kalman filter stabilizes the parameters of the results.

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Authors and Affiliations

  1. Otto-von-Guericke University Magdeburg, FEIT/IESK, P.O. Box 4120, D-39106, Magdeburg, Germany

    M. Tornow, J. Kaszubiak, R. W. Kuhn, B. Michaelis & G. Krell

Authors
  1. M. Tornow
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  2. J. Kaszubiak
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  3. R. W. Kuhn
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  4. B. Michaelis
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  5. G. Krell
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The text was submitted by the authors in English.

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Tornow, M., Kaszubiak, J., Kuhn, R.W. et al. Stereophotogrammetric real-time 3D machine vision. Pattern Recognit. Image Anal. 16, 100–103 (2006). https://doi.org/10.1134/S1054661806010317

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  • DOI: https://doi.org/10.1134/S1054661806010317

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