Dimensional change measurement and stain segmentation in printed fabrics

Abstract

Dimensional changes and soil release are important functional attributes of fabrics that impact their pricing in the marketplace. This paper presents the underlying principles of a machine vision system that creates a framework for making both measurements in a simultaneous and automatic fashion from digitized fabric images. In addition, the proposed approach is equipped to operate on solid color, as well as printed/patterned fabrics. This system, using image analysis, can (i) measure dimensional changes, which include shrinkage and skew, and (ii) localize stains on the fabric image, which can then be evaluated for soil release. Robust extraction of the above measurements on printed fabric images are made possible using a sequence of customized image registration and background-subtraction techniques. The system was validated using a set of 623 fabric images that involved the detection of 1868 shrinkage dots which were used to make 934 shrinkage measurements, 467 skew measurements, and detect 240 potential stains. The system produced excellent results with a successful shrinkage dot detection rate of 98.9% and an average stain segmentation accuracy of 0.87 using the dice metric.

Automatic measurement for dimensional changes of woven fabrics based on texture

“…Sridharan et al [32] presented a machine vision system for objective evaluation of shrinkage. Shrinkage measurements in the length-wise and width-wise directions were obtained by detecting and measuring the distance between two pairs of appropriately segmented benchmarks before and after wash. Hill et al [33] reported a machine vision system that can measure dimensional changes on patterned fabrics.…”