Visualization of Textile Surface Roughness Based on Silhouette Image Analysis

This paper presents a digital imaging method based on silhouette image analysis to visualize the three-dimensional (3D) surface profile of textiles, or other flexible materials, and investigates its 3D reconstruction principle. A prototype of an imaging system consisting of five components has been developed: visible lighting source (white backlighting), sample stage, progressive sample feeding equipment, detector (CCD camera), and analysis software. A sequence of silhouette images of textile surface patches was digitized progressively where a sample was passed through the sharp edge of a crossbeam in bending status, so that the whole 3D surface profile could be generated by a combination of the silhouette height profile of each surface patch in the sequence. This non-destructive testing method is effective for the surface characterization or defects detection of flexible materials. It can reconstruct a pure 3D surface profile of the surface while discarding unwanted information such as surface color. This avoids the fusion of shadows and dark colors, which is a problem when using traditional two-dimensional (2D) image analysis methods. The Kawabata Evaluation System (KES) is used to validate the effectiveness of this silhouette imaging system (SIS), and our experiment shows that good correlation can be achieved in terms of thickness and surface roughness measurements. However, there are some differences due to the contact or non-contact methods used in terms of the measurement of fuzzy layers in textile materials.