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

Yuan Baozong; Xiaohan Yu; Baozong Yuan

doi:10.1117/12.2294396

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

Yuan Baozong, Xiaohan Yu, Baozong Yuan

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-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 language	English
Title of host publication	Close-Range Photogrammetry Meets Machine Vision
Pages	1203-1209
DOIs	https://doi.org/10.1117/12.2294396
Publication status	Published - 1990
MoE publication type	A4 Article in a conference publication

Publication series

Series	Proceedings of SPIE
Number	Part 2
Volume	1395
ISSN	0277-786X

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@inproceedings{88224acf097a48d5936784b010d0b903,

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",

year = "1990",

doi = "10.1117/12.2294396",

language = "English",

series = "Proceedings of SPIE",

publisher = "International Society for Optics and Photonics SPIE",

number = "Part 2",

pages = "1203--1209",

booktitle = "Close-Range Photogrammetry Meets Machine Vision",

}

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 proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Yu, Xiaohan

AU - Yuan, Baozong

PY - 1990

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N2 - 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.

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.

U2 - 10.1117/12.2294396

DO - 10.1117/12.2294396

M3 - Conference article in proceedings

T3 - Proceedings of SPIE

SP - 1203

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BT - Close-Range Photogrammetry Meets Machine Vision

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