Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

Metric of global texture direction Based on wavelet modulus maxima threshold selection and Power Spectrum

Authors
Ye Chen, Peng Jin, Xiaoping Yuan
Corresponding Author
Ye Chen
Available Online March 2017.
DOI
https://doi.org/10.2991/ifmca-16.2017.29How to use a DOI?
Keywords
Texture orientation , Gabor Filter, Global texture direction, Feature extraction
Abstract
The present study proposes a metric algorithm for image texture orientation to establish the corresponding mathematical model. First, the image power spectrum is obtained through wavelet maximum edge detection and edge expansion of the texture image. Then threshold image power is set to acquire multiple sets of texture power vector. Then the sum of texture power vector sets makes global power vector, quantitative value of global texture direction. At last, precision of sector and global texture direction under different threshold values is tested on multiple linear stripe and wood texture images. Test results on these two texture databases have a high consistency with human visual perception. It can be concluded that the proposed metric algorithm of texture direction measurement can satisfy the need of high accuracy of man-machine texture recognition model.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.29How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Ye Chen
AU  - Peng Jin
AU  - Xiaoping Yuan
PY  - 2017/03
DA  - 2017/03
TI  - Metric of global texture direction Based on wavelet modulus maxima threshold selection and Power Spectrum
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 179
EP  - 184
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.29
DO  - https://doi.org/10.2991/ifmca-16.2017.29
ID  - Chen2017/03
ER  -