Modified Markov random field model and its applications to texture synthesis and data compression

Yuan Baozong, Xiaohan Yu, Baozong Yuan

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

Markov Random Field (MRF) model is a very useful model for image texture processing. But its stability condition is hardly to meet for natural textures. To find a stable MRF model is difficult and complex in computation. In this paper a new MRF model, called Modified Markov Random Field Model, is proposed; A stable Modified MRF model can be easily obtained for stochastic and natural textures. It is suitable for texture synthesis and data compression.
Original languageEnglish
Title of host publicationClose-Range Photogrammetry Meets Machine Vision
Pages1203-1209
DOIs
Publication statusPublished - 1990
MoE publication typeA4 Article in a conference publication

Publication series

SeriesProceedings of SPIE
NumberPart 2
Volume1395
ISSN0277-786X

Fingerprint

Data compression
Textures
Image texture
Processing

Cite this

Baozong, Y., Yu, X., & Yuan, B. (1990). Modified Markov random field model and its applications to texture synthesis and data compression. In Close-Range Photogrammetry Meets Machine Vision (pp. 1203-1209). Proceedings of SPIE, No. Part 2, Vol.. 1395 https://doi.org/10.1117/12.2294396
Baozong, Yuan ; Yu, Xiaohan ; Yuan, Baozong. / Modified Markov random field model and its applications to texture synthesis and data compression. Close-Range Photogrammetry Meets Machine Vision. 1990. pp. 1203-1209 (Proceedings of SPIE; No. Part 2, Vol. 1395).
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title = "Modified Markov random field model and its applications to texture synthesis and data compression",
abstract = "Markov Random Field (MRF) model is a very useful model for image texture processing. But its stability condition is hardly to meet for natural textures. To find a stable MRF model is difficult and complex in computation. In this paper a new MRF model, called Modified Markov Random Field Model, is proposed; A stable Modified MRF model can be easily obtained for stochastic and natural textures. It is suitable for texture synthesis and data compression.",
author = "Yuan Baozong and Xiaohan Yu and Baozong Yuan",
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Baozong, Y, Yu, X & Yuan, B 1990, Modified Markov random field model and its applications to texture synthesis and data compression. in Close-Range Photogrammetry Meets Machine Vision. Proceedings of SPIE, no. Part 2, vol. 1395, pp. 1203-1209. https://doi.org/10.1117/12.2294396

Modified Markov random field model and its applications to texture synthesis and data compression. / Baozong, Yuan; Yu, Xiaohan; Yuan, Baozong.

Close-Range Photogrammetry Meets Machine Vision. 1990. p. 1203-1209 (Proceedings of SPIE; No. Part 2, Vol. 1395).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AB - Markov Random Field (MRF) model is a very useful model for image texture processing. But its stability condition is hardly to meet for natural textures. To find a stable MRF model is difficult and complex in computation. In this paper a new MRF model, called Modified Markov Random Field Model, is proposed; A stable Modified MRF model can be easily obtained for stochastic and natural textures. It is suitable for texture synthesis and data compression.

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Baozong Y, Yu X, Yuan B. Modified Markov random field model and its applications to texture synthesis and data compression. In Close-Range Photogrammetry Meets Machine Vision. 1990. p. 1203-1209. (Proceedings of SPIE; No. Part 2, Vol. 1395). https://doi.org/10.1117/12.2294396