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Scalable data parallel algorithms for texture synthesis using Gibbs random fields.

D A Bader1, J Jaja, R Chellappa

  • 1Dept. of Electr. Eng., Maryland Univ., College Park, MD.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study presents scalable data parallel algorithms for image processing, enabling real-time texture synthesis and compression. These methods significantly outperform sequential approaches for texture analysis and parameter estimation.

Area of Science:

  • Computer Vision
  • Image Processing
  • Parallel Computing

Background:

  • Texture analysis is crucial in image processing.
  • Gibbs and Markov random fields are effective models for texture representation.
  • Existing sequential algorithms for texture synthesis and compression are computationally intensive.

Purpose of the Study:

  • To introduce scalable data parallel algorithms for image processing tasks.
  • To develop real-time algorithms for texture synthesis and compression.
  • To enable machine-independent algorithms for image analysis.

Main Methods:

  • Implementation of data parallel algorithms on Thinking Machines CM-2 and CM-5.
  • Utilizing fine-grained, data parallel processing techniques.

Related Experiment Videos

  • Focusing on Gibbs and Markov random field models for texture representation.

    • Main Results:

    • Achieved real-time performance for texture synthesis and compression.
    • Demonstrated substantial speedups compared to sequential implementations.
    • Developed parallel algorithms for maximum likelihood parameter estimation.

    Conclusions:

    • Scalable data parallel algorithms offer significant performance improvements for image processing.
    • The presented methodology facilitates machine-independent algorithms.
    • Fine-grained parallel processing is effective for complex texture analysis tasks.