Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain
DOI:
10.3993/jfbi09201310
Journal of Fiber Bioengineering & Informatics, 6 (2013), pp. 325-333.
Published online: 2013-06
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@Article{JFBI-6-325,
author = {Junfeng Jing, Tao Peng and Pengfei Li},
title = {Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2013},
volume = {6},
number = {3},
pages = {325--333},
abstract = {This paper proposes a new texture image segmentation algorithm using a Multi-resolution Markov
Random Field (MRMRF) model with a variable weight in the wavelet domain. For segmentation on
textile printing design, firstly it combines wavelet decomposition to multi-resolution analysis. Secondly
the energy of the label field and the feature field are calculated on multi-scales based on variable
weight MRMRF algorithm. Finally new segmentation results are obtained and saved. Compared with
traditional algorithms, experimental results prove that the new method presents a better performance
in achieving the edge sharpness and similarity of results.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbi09201310},
url = {http://global-sci.org/intro/article_detail/jfbi/4846.html}
}
TY - JOUR
T1 - Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain
AU - Junfeng Jing, Tao Peng & Pengfei Li
JO - Journal of Fiber Bioengineering and Informatics
VL - 3
SP - 325
EP - 333
PY - 2013
DA - 2013/06
SN - 6
DO - http://doi.org/10.3993/jfbi09201310
UR - https://global-sci.org/intro/article_detail/jfbi/4846.html
KW - Texture
KW - Image Segmentation
KW - MRMRF Model
KW - Wavelet Domain
KW - Weight
AB - This paper proposes a new texture image segmentation algorithm using a Multi-resolution Markov
Random Field (MRMRF) model with a variable weight in the wavelet domain. For segmentation on
textile printing design, firstly it combines wavelet decomposition to multi-resolution analysis. Secondly
the energy of the label field and the feature field are calculated on multi-scales based on variable
weight MRMRF algorithm. Finally new segmentation results are obtained and saved. Compared with
traditional algorithms, experimental results prove that the new method presents a better performance
in achieving the edge sharpness and similarity of results.
Junfeng Jing, Tao Peng and Pengfei Li. (2013). Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain.
Journal of Fiber Bioengineering and Informatics. 6 (3).
325-333.
doi:10.3993/jfbi09201310
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