New Rotation-Invariant Texture Analysis Technique Using Radon Transform and Hidden Markov Models

Abdul JALIL; Anwar MANZAR; Tanweer A. CHEEMA; Ijaz M. QURESHI

doi:10.1093/ietisy/e91-d.12.2906

New Rotation-Invariant Texture Analysis Technique Using Radon Transform and Hidden Markov Models

Abdul JALIL, Anwar MANZAR, Tanweer A. CHEEMA, Ijaz M. QURESHI

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A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.12 pp.2906-2909

Publication Date: 2008/12/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.12.2906

Type of Manuscript: LETTER

Category: Computer Graphics

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Abdul JALIL, Anwar MANZAR, Tanweer A. CHEEMA, Ijaz M. QURESHI, "New Rotation-Invariant Texture Analysis Technique Using Radon Transform and Hidden Markov Models" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 12, pp. 2906-2909, December 2008, doi: 10.1093/ietisy/e91-d.12.2906.
Abstract: A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.12.2906/_p

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@ARTICLE{e91-d_12_2906,
author={Abdul JALIL, Anwar MANZAR, Tanweer A. CHEEMA, Ijaz M. QURESHI, },
journal={IEICE TRANSACTIONS on Information},
title={New Rotation-Invariant Texture Analysis Technique Using Radon Transform and Hidden Markov Models},
year={2008},
volume={E91-D},
number={12},
pages={2906-2909},
abstract={A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.},
keywords={},
doi={10.1093/ietisy/e91-d.12.2906},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - New Rotation-Invariant Texture Analysis Technique Using Radon Transform and Hidden Markov Models
T2 - IEICE TRANSACTIONS on Information
SP - 2906
EP - 2909
AU - Abdul JALIL
AU - Anwar MANZAR
AU - Tanweer A. CHEEMA
AU - Ijaz M. QURESHI
PY - 2008
DO - 10.1093/ietisy/e91-d.12.2906
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2008
AB - A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.
ER -