The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Radon Transform(RT)과 Hidden Markov Models(HMM)의 새로운 조합을 이용한 회전 불변 텍스처 분석 기법이 제안되었습니다. 모든 텍스처의 특징은 RT 중에 추출되며 고유한 속성으로 인해 특정 텍스처의 모든 방향 속성을 캡처합니다. HMM은 분류 목적으로 사용됩니다. 하나의 HMM은 특징 벡터의 회전 불변성을 보다 간결하고 유용한 형태로 유지하는 특징 벡터의 각 텍스처에 대해 훈련됩니다. 모든 HMM이 훈련되면 임의의 방향에서 이러한 텍스처를 선택하여 테스트가 수행됩니다. 올바른 분류(PCC)의 최고 비율은 Brodatz 앨범의 98 텍스처에서 XNUMX% 이상 수행되었습니다.
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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
부
@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},}
부
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 -