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Related Experiment Videos

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

Summary

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.

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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.
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.