A fabric's smoothness is a key factor to determine the quality of textile finished products and has great influence on the
functionality of industrial textiles and high-end textile products. With popularization of the 'zero defect' industrial
concept, identifying and measuring defective material in the early stage of production is of great interest for the industry.
In the current market, many systems are able to achieve automatic monitoring and control of fabric, paper, and
nonwoven material during the entire production process, however online measurement of hairiness is still an open topic
and highly desirable for industrial applications.
In this paper we propose a computer vision approach, based on variable homography, which can be used to measure the
emergent fiber's length on textile fabrics. The main challenges addressed in this paper are the application of variable
homography to textile monitoring and measurement, as well as the accuracy of the estimated calculation. We propose
that a fibrous structure can be considered as a two-layer structure and then show how variable homography can estimate
the length of the fiber defects. Simulations are carried out to show the effectiveness of this method to measure the
emergent fiber's length. The true lengths of selected fibers are measured precisely using a digital optical microscope, and
then the same fibers are tested by our method. Our experimental results suggest that smoothness monitored by variable
homography is an accurate and robust method for quality control of important industrially fabrics.