Many fabric defects are very small and undistinguishable, which makes them
very difficult to detect by only monitoring the intensity change, Faultless
fabric is a repetitive and regular global texture and Fourier transform ca
n be applied to monitor the spatial frequency spectrum of a fabric. When a
defect occurs in fabric, its regular structure is changed so that the corre
sponding intensity at some specific positions of the frequency spectrum wou
ld change, However, the three-dimensional frequency spectrum is very diffic
ult to analyze, In this paper, a simulated fabric model Is used to understa
nd the relationship between the fabric structure in the image space and in
the frequency space. Based on the three-dimensional frequency spectrum, two
significant spectral diagrams are defined and used for analyzing the fabri
c defect, These two diagrams are called the central spatial frequency spect
rums. The defects are broadly classified into four classes: 1) double yarn;
2) missing yarn; 3) webs or broken fabric; and 4) yarn densities variation
. After evaluating these four classes of defects using some simulated model
s and real samples, seven characteristic parameters for a central spatial f
requency spectrum are extracted for defect classification.