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

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  • 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