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Related Experiment Videos

Minimal-memory bit-vector architecture for computational mathematical morphology using subspace projections.

John C Handley1

  • 1Xerox Corporation, Webster, NY 14580-9701, USA. jhandley@crt.xerox.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 27, 2005
PubMed
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Computational mathematical morphology (CMM) offers efficient real-time image processing. This study presents a novel architecture reducing memory dependence on gray levels, enhancing filter performance.

Area of Science:

  • Computer Vision
  • Image Processing
  • Digital Signal Processing

Background:

  • Computational mathematical morphology (CMM) is a nonlinear filter representation suitable for real-time image processing.
  • Current CMM implementations require memory proportional to image gray levels, limiting scalability.
  • Windowed, translation-invariant filters rely on pixel comparisons and lookup tables.

Purpose of the Study:

  • To introduce a new CMM architecture minimizing memory requirements.
  • To reduce the sensitivity of filter memory to the number of image gray levels.
  • To demonstrate memory savings in practical applications like digital photocopiers.

Main Methods:

  • Proposed a CMM architecture projecting basis elements to subspaces.
  • Stored only unique bit vectors for each subspace, reducing memory footprint.

Related Experiment Videos

  • Investigated fixed, singleton, and minimal architecture projection strategies.
  • Utilized intermediate lookup tables for mapping observations to unique bit vectors.

    • Main Results:

    • Achieved memory reduction insensitive to the number of image gray levels.
    • Demonstrated memory savings through simulated random-image space tessellations.
    • Quantified memory savings in a digital photocopier application.

    Conclusions:

    • The proposed CMM architecture significantly reduces memory usage.
    • This approach enhances the efficiency and applicability of CMM in real-time image processing.
    • The method offers practical benefits for image processing hardware and applications.