Fabric surface appearance is one of the most important visible properties of fabrics determining their price and
consumer satisfaction. This characteristic is routinely evaluated with reference to a set of visual standards in the textile industry.
The visual evaluation is unreliable, time-consuming, inefficient, and, depends on many human factors. Machine
vision techniques can overcome these difficulties and evaluate the fabric surface with high precision and high speed. Application
of machine vision to evaluate woven fabric appearance such as seersucker effect, repetition, uniformity, disorder,
wrinkling, and quality of printed patterns needs a lighting system, an image capturing device, a computer, and appropriate
software for data processing. This paper introduces some new quantitative analysis methods for fabric appearance inspection
and addresses the development of optical devices in evaluating textural features. The gray-level co-occurrence matrix,
spectral density function, angular power spectrum function, distance matching function, and, normalized cross-correlation
are the most important algorithms used in this new method. The method developed in this work makes the rapid and exact
inspection of fabric surface possible. Objective results from these new methods were compared with subjective ones from
human experts. The correlation between texture analysis and subjective evaluation was acceptable at 95% confidence interval
for all cases.