Paper Info
Title
Defects Identification in Textile by Means of Artificial Neural Networks
Abstract
In this paper we use a neural network approach for defects identification in textile. The images analyzed came from an artificial vision system that we used to acquire and memorize them in bitmap file format. The vision system is made of two grey scale line scan camera arrays and each array is composed of four CCD cameras with a sensor of 2048 pixels. Every single camera has a field of view of 600mm. The big amount of pixels to be studied to determine whether the texture is defective or not, requires the implementation of some encoding technique to reduce the number of the significant elements. The artificial neural networks (ANN) are manipulated to compress a bitmap that may contain several defects in order to represent it with a number of coefficients that is smaller than the total number of pixel but still enough to identify all kinds of defects classified. An error back propagation algorithm is also used to train the neural network. The proposed technique includes, also, steps to break down large images into smaller windows or array and eliminate redundant information.
Year
DOI
Venue
2008
10.1007/978-3-540-85984-0_140
ICIC (2)
Keywords
Field
DocType
neural network approach,defects identification,encoding technique,total number,neural network,artificial neural networks,camera array,artificial vision system,ccd camera,bitmap file format,artificial neural network,error back propagation,field of view,vision system
Machine vision,Computer science,Time delay neural network,Artificial intelligence,Artificial neural network,File format,Computer vision,Pattern recognition,Pixel,Bitmap,Backpropagation,Machine learning,Encoding (memory)
Conference
Volume
ISSN
Citations 
5227
0302-9743
2
PageRank 
References 
Authors
0.38
4
5
Name
Order
Citations
PageRank
Vitoantonio Bevilacqua146866.40
Lucia Cariello2322.88
G. Mastronardi316726.29
Vito Palmieri420.38
Marco Giannini5101.32