Robust rotation invariant texture classification

RMS Porter, CN Canagarajah

Research output: Chapter in Book/Report/Conference proceedingConference 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 contributionRobust rotation invariant texture classification
Original languageEnglish
Title of host publicationUnknown
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3157 - 3160
Volume4
ISBN (Print)0818679190
DOIs
Publication statusPublished - Apr 1997
Event1997 IEEE International Conference on Acoustics, Speech, and Signal Processing - Munich, Germany
Duration: 21 Apr 199724 Apr 1997

Conference

Conference1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP '97
CountryGermany
CityMunich
Period21/04/9724/04/97

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).

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