Robust rotation invariant texture classification

RMS Porter; CN Canagarajah

doi:10.1109/ICASSP.1997.595462

Robust rotation invariant texture classification

RMS Porter
, CN Canagarajah

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16×16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes

Translated title of the contribution	Robust rotation invariant texture classification
Original language	English
Title of host publication	Unknown
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3157 - 3160
Volume	4
ISBN (Print)	0818679190
DOIs	https://doi.org/10.1109/ICASSP.1997.595462
Publication status	Published - Apr 1997
Event	1997 IEEE International Conference on Acoustics, Speech, and Signal Processing - Munich, Germany Duration: 21 Apr 1997 → 24 Apr 1997

Conference

Conference	1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP '97
Country/Territory	Germany
City	Munich
Period	21/04/97 → 24/04/97

Bibliographical note

Conference Proceedings/Title of Journal: ICASSP
Rose publication type: Conference contribution

Terms of use: Copyright © 1997 IEEE. Reprinted from IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997 (ICASSP-97).

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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10.1109/ICASSP.1997.595462

Porter IEEE ICASSP 1997Accepted author manuscript, 392 KB

http://hdl.handle.net/1983/1509

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@inproceedings{60b1b3f5c052489cbeaa8040a3d0a586,

title = "Robust rotation invariant texture classification",

abstract = "The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16{\texttimes}16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes",

author = "RMS Porter and CN Canagarajah",

note = "Conference Proceedings/Title of Journal: ICASSP Rose publication type: Conference contribution Terms of use: Copyright {\textcopyright} 1997 IEEE. Reprinted from IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997 (ICASSP-97). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. ; 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '97 ; Conference date: 21-04-1997 Through 24-04-1997",

year = "1997",

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doi = "10.1109/ICASSP.1997.595462",

language = "English",

isbn = "0818679190",

volume = "4",

pages = "3157 -- 3160",

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publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

TY - GEN

T1 - Robust rotation invariant texture classification

AU - Porter, RMS

AU - Canagarajah, CN

N1 - Conference Proceedings/Title of Journal: ICASSP Rose publication type: Conference contribution Terms of use: Copyright © 1997 IEEE. Reprinted from IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997 (ICASSP-97). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

PY - 1997/4

Y1 - 1997/4

N2 - The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16×16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes

AB - The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16×16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes

U2 - 10.1109/ICASSP.1997.595462

DO - 10.1109/ICASSP.1997.595462

M3 - Conference Contribution (Conference Proceeding)

SN - 0818679190

VL - 4

SP - 3157

EP - 3160

BT - Unknown

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 21 April 1997 through 24 April 1997

ER -

Robust rotation invariant texture classification

Abstract

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