@article {15021,
	title = {Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields},
	volume = {UMIACS-TR-93-80},
	year = {1998},
	month = {1998/10/15/},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	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.
(Also cross-referenced as UMIACS-TR-93-80.)
},
	keywords = {Technical Report},
	url = {http://drum.lib.umd.edu/handle/1903/596},
	author = {Bader,David A. and JaJa, Joseph F. and Chellapa, Rama}
}
