Scalable data parallel algorithms for texture synthesis using Gibbs random fields

TitleScalable data parallel algorithms for texture synthesis using Gibbs random fields
Publication TypeJournal Articles
Year of Publication1995
AuthorsBader DA, JaJa JF, Chellappa R
JournalImage Processing, IEEE Transactions on
Pagination1456 - 1460
Date Published1995/10//
ISBN Number1057-7149
Keywordsalgorithms;maximum, algorithms;parallel, algorithms;scalable, algorithms;texture, analysis;image, CM-2;Thinking, CM-5;fine-grained, compression;image, compression;texture, Connection, data, estimation;model, estimation;parallel, field;Thinking, fields;Markov, likelihood, machine, Machines;Gibbs, machines;random, Parallel, parameter, processes;, processes;data, processing;image, processing;machine-independent, random, representation;real-time, scalable, synthesis;Markov, texture;maximum

This article 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. The 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 enables machine-independent scalable algorithms for a number of problems in image processing and analysis