David A. Bader
IEEE Fellow
AAAS Fellow
College of Computing
Georgia Tech
Atlanta, GA 30332



Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields

This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. Use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented here enables machine independent scalable algorithms for a number of problems in image processing and analysis.

Publication History

Versions of this paper appeared as:
  1. University of Maryland CS-TR-3123, UMIACS-TR-93-80
  2. D. A. Bader, J. JáJá , R. Chellappa. ``Scalable Data Parallel Algorithms for Texture Synthesis using Gibbs Random Fields,'' IEEE Transactions on Image Processing, 4(10):1456-1460, October 1995.

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Last updated: July 25, 2004


Computational Biology

Parallel Computing