Volume 6, Issue 3
Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain

Junfeng Jing, Tao Peng & Pengfei Li

Journal of Fiber Bioengineering & Informatics, 6 (2013), pp. 325-333.

Published online: 2013-06

Preview Purchase PDF 1 1861
Export citation
  • 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.

  • Keywords

Texture Image Segmentation MRMRF Model Wavelet Domain Weight

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JFBI-6-325, author = {}, 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 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 & Pengfei Li. (2019). 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
Copy to clipboard
The citation has been copied to your clipboard