@Article{JFBI-6-3,
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 = {https://global-sci.com/article/86719/textile-image-segmentation-using-a-multi-resolution-markov-random-field-model-on-variable-weights-in-the-wavelet-domain}
}