%0 Journal Article
	%A Atiqul Islam and  Shamim Akhter and  Tumnun E. Mursalin
	%D 2008
	%J International Journal of Materials and Textile Engineering 
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 13, 2008
	%T Automated Textile Defect Recognition System Using Computer Vision and Artificial Neural Networks
	%U https://publications.waset.org/pdf/12913
	%V 13
	%X Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
	%P 110 - 115