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Efficient migration of complex off-line computer vision software to real-time system implementation on generic

James Alexander Tyrrell1, Justin M LaPre, Christopher D Carothers

  • 1Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA.

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|June 26, 2004
PubMed
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This study presents a novel method for real-time computer vision systems by integrating code into the Linux kernel. This approach enhances system predictability and reduces processing variations for efficient frame-rate performance.

Area of Science:

  • Computer Vision
  • Real-time Systems
  • Operating System Kernels

Background:

  • Migrating complex, offline computer vision codebases to real-time systems is challenging and costly.
  • Existing methods often require complete software rewrites, increasing development time and expense.

Purpose of the Study:

  • To develop a method for migrating large computer vision codebases to efficient real-time implementations without rewriting.
  • To enable simultaneous real-time and offline frame-rate operation from a single codebase.

Main Methods:

  • Utilizing Linux loadable kernel modules for creative linking strategies.
  • Inserting time-critical components directly into the kernel as virtual device drivers.
  • Emulating a nonpreemptable, nonpageable single process space with direct system-level access.

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Main Results:

  • Achieved systemic predictability in frame-rate computer vision systems.
  • Demonstrated the feasibility using commercial off-the-shelf hardware and a standard Linux OS.
  • Reduced variance in per-frame CPU cycle counts by two orders of magnitude in a laser retinal surgery system.

Conclusions:

  • Efficient migration to predictable frame-rate computer vision systems is possible.
  • The presented method provides a basis for building predictable vision systems.
  • Predictable application algorithms are key to achieving efficient, real-time performance.