STATISTICAL APPROACHES FOR TEXTURE SYNTHESIS.

Rama Chellappa; Chatterjee Shankar Chatterjee

STATISTICAL APPROACHES FOR TEXTURE SYNTHESIS.

Rama Chellappa, Chatterjee Shankar Chatterjee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Some statistical methods of texture synthesis are discussed. The statistical models at the pixel level, like the SAR and GMRF models, seem to capture the main characteristics of microtextures like grass, send, cork and so on. These models do not synthesize textures like plastic bubbles and brick wall satisfactorily due to macrostructures present in these textures. A combination of statistical and structural techniques should prove more efficient to synthesize macrotextures.

Original language	English (US)
Title of host publication	Unknown Host Publication Title
Publisher	Natl Computer Graphics Assoc
Pages	671-678
Number of pages	8
ISBN (Print)	0941514056
State	Published - 1984
Externally published	Yes

ASJC Scopus subject areas

General Engineering

Cite this

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title = "STATISTICAL APPROACHES FOR TEXTURE SYNTHESIS.",

abstract = "Some statistical methods of texture synthesis are discussed. The statistical models at the pixel level, like the SAR and GMRF models, seem to capture the main characteristics of microtextures like grass, send, cork and so on. These models do not synthesize textures like plastic bubbles and brick wall satisfactorily due to macrostructures present in these textures. A combination of statistical and structural techniques should prove more efficient to synthesize macrotextures.",

author = "Rama Chellappa and {Shankar Chatterjee}, Chatterjee",

year = "1984",

language = "English (US)",

isbn = "0941514056",

publisher = "Natl Computer Graphics Assoc",

pages = "671--678",

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T1 - STATISTICAL APPROACHES FOR TEXTURE SYNTHESIS.

AU - Chellappa, Rama

AU - Shankar Chatterjee, Chatterjee

PY - 1984

Y1 - 1984

N2 - Some statistical methods of texture synthesis are discussed. The statistical models at the pixel level, like the SAR and GMRF models, seem to capture the main characteristics of microtextures like grass, send, cork and so on. These models do not synthesize textures like plastic bubbles and brick wall satisfactorily due to macrostructures present in these textures. A combination of statistical and structural techniques should prove more efficient to synthesize macrotextures.

AB - Some statistical methods of texture synthesis are discussed. The statistical models at the pixel level, like the SAR and GMRF models, seem to capture the main characteristics of microtextures like grass, send, cork and so on. These models do not synthesize textures like plastic bubbles and brick wall satisfactorily due to macrostructures present in these textures. A combination of statistical and structural techniques should prove more efficient to synthesize macrotextures.

UR - http://www.scopus.com/inward/record.url?scp=0021693687&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0021693687&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0021693687

SN - 0941514056

SP - 671

EP - 678

BT - Unknown Host Publication Title

PB - Natl Computer Graphics Assoc

ER -

STATISTICAL APPROACHES FOR TEXTURE SYNTHESIS.

Abstract

ASJC Scopus subject areas

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