Classification of Woven Fabric Faulty Images Using Convolution Neural Network

Algorithm 1

Process for woven fabric fault classification.

	Input: Fabric images (FI) M [r, c]: set of features.
	Output: Classification of woven fabric images N [r, c] as defective and nondefective.
(1)	Deep learning-based features are extracted.
(2)	The classifier is trained with deep learning-based features.
	Begin
(3)	Computer histogram equalization.
(4)	Computer Gaussian filtering.
(5)	Computer Fourier transform.
(6)	Computer edges of the fabric faults after applying the inverse Fourier transform.
(7)	Computer deep learning features
(8)	for training samples (TestSi, TrainSi) do
	Train the classifier
(9)	end for
(10)	The classification results
	End