Next: Introduction
Scalable Data Parallel Algorithms for Texture Synthesis using Gibbs Random Fields
David A. Bader
Joseph JáJá
Rama Chellappa
{dbader,joseph,chella}@eng.umd.edu
Department of Electrical Engineering, and
Institute for Advanced Computer Studies,
University of Maryland, College Park, MD 20742
Submitted: October 4, 1993 Revised: July 28, 1994
Abstract:
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.
Permission to publish this abstract separately is granted.
Keywords: Gibbs Sampler, Gaussian Markov Random Fields, Image
Processing, Texture Synthesis, Data Parallel Algorithms.
Next: Introduction
David A. Bader
dbader@umiacs.umd.edu