Image Segmentation of Printed Fabrics with Hierarchical Improved Markov Random Field in the Wavelet Domain. Issue 3 (September 2016)

• Record Type: Journal Article
• Authors: Jing, Junfeng; Li, Qi; Li, Pengfei; Zhang, Hongwei; Zhang, Lei
• Is Part Of: Journal of engineered fibers and fabrics. Volume 11:Issue 3(2016)
• Publication Date: 2016-09
• Subjects: Image segmentation -- Feature field modeling -- Label field modeling -- Parameter estimation; Nonwoven fabrics -- Periodicals; Fibers -- Periodicals; Fibers; Nonwoven fabrics; Periodicals; 677.6
• DOI: 10.1177/155892501601100305
• Languages: English
• ISSNs: 1558-9250

Abstract

An improved MRF algorithm–hierarchical Gauss Markov Random Field model in the wavelet domain is presented for fabric image segmentation in this paper, which obtains the relation of inter-scale dependency from the feature field modeling and label field modeling. The Gauss-Markov random field modeling is usually adopted to feature field modeling. The label field modeling employs the inter-scale causal MRF model and the intra-scale non-causal MRF model. After that, parameter estimation is the essential section in the inter-scale, enhancing modeling capabilities of the pixels partial dependency. Sequential maximum a posterior criterion is applied to achieve the results of image segmentation. Comparisons with other hybrid schemes, results are indicated that performance of the presented algorithm is effective and accurate, in terms of classification accuracy and kappa coefficient, for patterned fabric images.