Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain
Year: 2013
Journal of Fiber Bioengineering and Informatics, Vol. 6 (2013), Iss. 3 : pp. 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.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbi09201310
Journal of Fiber Bioengineering and Informatics, Vol. 6 (2013), Iss. 3 : pp. 325–333
Published online: 2013-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 9
Keywords: Texture
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Automatic measurement of yarn hairiness based on the improved MRMRF segmentation algorithm
Jing, Junfeng
Huang, Mengying
Li, Pengfei
Ning, Xiaocui
The Journal of The Textile Institute, Vol. 109 (2018), Iss. 6 P.740
https://doi.org/10.1080/00405000.2017.1368106 [Citations: 11]