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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... (Update)
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...on Digital Signal Processing, vol. 1, Hellas, Santorini, Greece) pp. 67 70, IEEE Signal Processing Society, July 1997. Invited Paper. [6] R. Paget and D. Longsta#, Extracting the cliques from a neighbourhood system, IEE Proceedings Vision, Image and Signal Processing,...
...12, 13, 14, and 15, for a higher order model on the same set of images. Similarly, CM 2 timings for these estimates can be found in [2]. Tables 8 15 are given in Appendix C. 21 5 Texture Compression We implement an algorithm for compressing an image of a GMRF texture to...
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BibTeX entry: (Update)
D. A. Bader, J. J'aJ'a, and R. Chellappa. Scalable Data Parallel Algorithms for Texture Synthesis and Compression Using Gibbs Random Fields. Technical Report CS-TR-3123 and UMIACS-TR-93-80, UMIACS and Electrical Engineering, University of Maryland, College Park, MD, August 1993. http://citeseer.ist.psu.edu/article/bader93scalable.html More
@techreport{ bader93scalable,
author = "David A. Bader and Joseph J\'aJ\'a and Rama Chellappa",
title = "Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields",
number = "CS-TR-3123",
year = "1993",
url = "citeseer.ist.psu.edu/article/bader93scalable.html" }
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178 The Connection Machine CM-5 Technical Summary (context) - Corporation, MA - 1992
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156 Fat-Trees: Universal Networks for Hardware-Efficient Superco.. (context) - Leiserson - 1985
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