Roughness Classification with Aggregated Discrete Fourier Transform

Chao LIANG; Wenming YANG; Fei ZHOU; Qingmin LIAO

doi:10.1587/transinf.2014EDP7082

Roughness Classification with Aggregated Discrete Fourier Transform

Chao LIANG, Wenming YANG, Fei ZHOU, Qingmin LIAO

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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

aggregated DFT (ADFT), completed local binary pattern (CLBP), roughness texture classification

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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://globals.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},}

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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 -