Title :
Preliminary study of vision system for the colorfastness rate assessment on woven fabrics
Author :
Suprijanto ; Samsi, Agus ; Mariyana, Elva ; Putra, Narendra Kurnia
Author_Institution :
Instrum. & Control Res.Group, ITB, Bandung, Indonesia
Abstract :
Commonly, subjective method base on human visual perception is the main method in textile colorfastness assessment. However, this method is time consuming and relatively inaccurate. This research aims to develop an objective method using vision system as an alternative method to perform woven fabrics colorfastness assessment. This vision system method relied on texture analysis, which is based on woven fabrics samples color homogeneity quantification. Firstly, image pre-processing method is conducted to get minimum fraction channel of the image, followed by feature extraction process using histogram method image and Grey Level Co-occurrence Matrix (GLCM). The pixel space used in GLCM method was 4 pixels with 0°, 45°, 90° and 135° observed orientation angle. Variance and bucket histogram are used as the parameters on the feature´s extraction through histogram analysis while GLCM use contrast, correlation, energy and homogeneity aspect as its parameters. Output of the feature extraction became input for classification system using artificial neural network. The artificial neural network analysis results shown that a system using a parallel input combination of contrast, homogeneity and variance can be used as a proper input for woven fabrics colorfastness test. This vision system results showed the validity of the system which can determine the colorfastness rate of the woven fabrics with accuracy of 100% for red, purple and yellow, 83.33% for green and 91.67% for blue.
Keywords :
computer vision; fabrics; feature extraction; image classification; image colour analysis; image texture; neural nets; production engineering computing; GLCM; artificial neural network; bucket histogram; classification system; color homogeneity quantification; colorfastness rate assessment; contrast parallel input combination; feature extraction process; grey level cooccurrence matrix; histogram analysis; histogram method image; image preprocessing method; pixel space; texture analysis; vision system; woven fabrics colorfastness assessment; Artificial neural networks; Fabrics; Feature extraction; Histograms; Image color analysis; Machine vision; GLCM; artificial neural network; colorfastness; image histogram; vision system;
Conference_Titel :
Instrumentation Control and Automation (ICA), 2013 3rd International Conference on
Conference_Location :
Ungasan
Print_ISBN :
978-1-4673-5795-1
DOI :
10.1109/ICA.2013.6734082