Machine vision system for simultaneous measurement of dimensional changes and soil release in printed fabric
This thesis presents a protocol for creating digital images of printed fabric swatches and an algorithm that will automatically measure dimensional changes and segment stains so that soil release could be evaluated. The dimensional changes measured here are shrinkage and skew. Current methods for evaluating dimensional changes on printed fabrics are manual. There are no current methods for evaluation of soil release on printed fabrics and the segmentation that the proposed algorithm provides is a vital first step to such a system. This thesis proposes a system that could become a standard for making both measurements simultaneously. To make these measurements, printed fabric swatches are scanned before and after wash using an off-the-shelf scanner. Reference points (called shrinkage dots) are placed on the fabric swatches and then located in the scanned images. This is done using image registration and subtraction to remove the influence of the pattern followed by cross-correlation to then locate the shrinkage dots. The locations of the shrinkage dots are used both to calculate the dimensional changes and in locating the stains. Before a snake-based method segments the stain, the influence of the background pattern is removed using the same registration and subtraction method used for shrinkage dot detection. In an experiment involving 240 images and 10 different printed patterns, the algorithm was able to correctly identify 98.8% of the shrinkage dots and identify stains in 93.3% of stains that were determined to exist according to technicians. The segmentation accuracy is quantified by an average dice metric of .87 in a set of 50 potential stains when comparing to manual segmentations.