IEICE TRANSACTIONS on Information
Roughness Classification with Aggregated Discrete Fourier Transform
Summary :
In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.10 pp.2769-2779
- Publication Date
- 2014/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7082
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Chao LIANG
Tsinghua University,Shenzhen Key Laboratory of Information Science and Technology,Tsinghua-PolyU Biometrics Joint Lab
Wenming YANG
Tsinghua University,Shenzhen Key Laboratory of Information Science and Technology,Tsinghua-PolyU Biometrics Joint Lab
Fei ZHOU
Tsinghua University,Shenzhen Key Laboratory of Information Science and Technology,Tsinghua-PolyU Biometrics Joint Lab
Qingmin LIAO
Tsinghua University,Shenzhen Key Laboratory of Information Science and Technology,Tsinghua-PolyU Biometrics Joint Lab
Keyword
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Chao LIANG, Wenming YANG, Fei ZHOU, Qingmin LIAO, "Roughness Classification with Aggregated Discrete Fourier Transform" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 10, pp. 2769-2779, October 2014, doi: 10.1587/transinf.2014EDP7082.
Abstract: In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7082/_p
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@ARTICLE{e97-d_10_2769,
author={Chao LIANG, Wenming YANG, Fei ZHOU, Qingmin LIAO, },
journal={IEICE TRANSACTIONS on Information},
title={Roughness Classification with Aggregated Discrete Fourier Transform},
year={2014},
volume={E97-D},
number={10},
pages={2769-2779},
abstract={In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.},
keywords={},
doi={10.1587/transinf.2014EDP7082},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Roughness Classification with Aggregated Discrete Fourier Transform
T2 - IEICE TRANSACTIONS on Information
SP - 2769
EP - 2779
AU - Chao LIANG
AU - Wenming YANG
AU - Fei ZHOU
AU - Qingmin LIAO
PY - 2014
DO - 10.1587/transinf.2014EDP7082
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2014
AB - In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.
ER -
English
