A Maximum Likelihood Approach to Texture Classification

A. L. Vickers; J. W. Modestino

doi:10.1109/TPAMI.1982.4767197

A Maximum Likelihood Approach to Texture Classification

A. L. Vickers, J. W. Modestino

Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

Abstract

A new approach to texture classification is described which is based on measurements of the spatial gray-level co-occurrence probability matrix. This approach can make use of assumed stochastic models for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The efficacy of the approach is demonstrated through experimental results obtained with real-world texture data.

Original language	English (US)
Pages (from-to)	61-68
Number of pages	8
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	PAMI-4
Issue number	1
DOIs	https://doi.org/10.1109/TPAMI.1982.4767197
State	Published - Jan 1982

Keywords

Digital image processing
maximum-likelihood estimation
texture classification

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/TPAMI.1982.4767197

Cite this

@article{013a1ebe2de04d768a4fc97265969475,

title = "A Maximum Likelihood Approach to Texture Classification",

abstract = "A new approach to texture classification is described which is based on measurements of the spatial gray-level co-occurrence probability matrix. This approach can make use of assumed stochastic models for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The efficacy of the approach is demonstrated through experimental results obtained with real-world texture data.",

keywords = "Digital image processing, maximum-likelihood estimation, texture classification",

author = "Vickers, {A. L.} and Modestino, {J. W.}",

year = "1982",

month = jan,

doi = "10.1109/TPAMI.1982.4767197",

language = "English (US)",

volume = "PAMI-4",

pages = "61--68",

journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",

issn = "0162-8828",

publisher = "IEEE Computer Society",

number = "1",

}

TY - JOUR

T1 - A Maximum Likelihood Approach to Texture Classification

AU - Vickers, A. L.

AU - Modestino, J. W.

PY - 1982/1

Y1 - 1982/1

N2 - A new approach to texture classification is described which is based on measurements of the spatial gray-level co-occurrence probability matrix. This approach can make use of assumed stochastic models for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The efficacy of the approach is demonstrated through experimental results obtained with real-world texture data.

AB - A new approach to texture classification is described which is based on measurements of the spatial gray-level co-occurrence probability matrix. This approach can make use of assumed stochastic models for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The efficacy of the approach is demonstrated through experimental results obtained with real-world texture data.

KW - Digital image processing

KW - maximum-likelihood estimation

KW - texture classification

UR - http://www.scopus.com/inward/record.url?scp=0019912334&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0019912334&partnerID=8YFLogxK

U2 - 10.1109/TPAMI.1982.4767197

DO - 10.1109/TPAMI.1982.4767197

M3 - Article

AN - SCOPUS:0019912334

VL - PAMI-4

SP - 61

EP - 68

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 1

ER -

A Maximum Likelihood Approach to Texture Classification

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this