Scalable Data Parallel Algorithms for Texture Synthesis Using Gibbs Random Fields

David A. Bader, Joseph JáJá, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


This correspondence 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.

Original languageEnglish (US)
Pages (from-to)1456-1460
Number of pages5
JournalIEEE Transactions on Image Processing
Issue number10
StatePublished - Oct 1995
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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