ABSTRACT This paper presents a new approach for processing images of woven fabrics to determine the warp-weft densities. This approach includes three main steps, namely; image transformation, image enhancement, and analyzing signals of the image. In the experimental process, 19 different woven fabric images were scanned at a high resolution (2400 dpi); then these images were transferred to the MATLAB program. By using the vertical and horizontal frequencies of the textile image, the FFT analyses were carried out. Consequently with 97 % accuracy, the densities were predicted only by using the images instead of counting them by hand

Kazım Yildiz

Mustafa Sabri Özen

Volkan Yusuf Şenyürek

Zehra Yildiz

CiteSeerX

Journal of Engineered Fibers and Fabrics 1 http://www.jeffjournal.org 
Volume 9, Issue 1 – 2014 
A New Approach to the Determination of  
Warp-Weft Densities in Textile Fabrics by  
Using an Image Processing Technique 
 
Kazım Yildiz1, Volkan Yusuf Şenyürek1, Zehra Yildiz2, Mustafa Sabri Özen2 
 
1Department of Electronic-Computer Education, Marmara University, Kadıköy, İstanbul, Turkey 
 
2Department of Textile Education, Marmara University, Kadıköy, İstanbul, Turkey 
 
Correspondence to: 
Kazım Yildiz email:  kazim.yildiz@marmara.edu.tr 
 
ABSTRACT 
This paper presents a new approach for processing 
images of woven fabrics to determine the warp-weft 
densities. This approach includes three main steps, 
namely; image transformation, image enhancement, 
and analyzing signals of the image. In the 
experimental process, 19 different woven fabric 
images were scanned at a high resolution (2400 dpi); 
then these images were transferred to the MATLAB 
program. By using the vertical and horizontal 
frequencies of the textile image, the FFT analyses 
were carried out. Consequently with 97 % accuracy, 
the densities were predicted only by using the images 
instead of counting them by hand.  
 
Keywords: textile fabrics, warp weft densities, image 
processing 
 
INTRODUCTION 
Image processing technique usage for industrial 
applications is widespread. For instance, image 
processing techniques can be used in the textile 
industry for applications such as flaw detection in 
textile fabrics [1, 2], lint in textile fibers [3, 4],  yarn 
diameter measurement from a given image of an 
optical sensor [5], and scanning electron microscopy 
[6], and yarn quality testing equipment for measuring 
yarn production characteristics [7]. In a previous 
study, the digital image correlation technique was 
used to assess macro-scale textile deformations 
during biaxial tensile and shear tests in order to 
verify the loading homogeneity and the 
correspondence between the textile and rig 
deformation [8]. Also, tow geometry parameters are 
measured with the help of microscopy and image 
processing techniques in order to determine tow 
deformations in a textile preform subjected to 
 
 
forming forces and the influence of these two 
deformations on composite laminate properties [9]. 
In another research an automated searching system 
based on color image processing system was 
improved for forensic fiber investigations, in order to 
determine the connection between the crime scene 
and the textile material [10].  
 
Warp and weft densities in woven fabrics are very 
important parameters in making textile production 
measurements. Determination of weft and warp 
densities in textile woven fabric depends on the 
principle of analyzing the vertical and horizontal 
frequencies from a textile image. Counting these 
parameters is generally made by hand with the help 
of a tiny device namely “loupe”. So the exact density 
can change from person to person. This paper 
presents a warp-weft density prediction system based 
on a given textile image. In order to prevent 
individual errors the image processing technique was 
used to determine the exact weft-warp densities. For 
the system design, 19 different textile fabrics were 
scanned in 2400 dpi resolution and then transformed 
to the MATLAB program. The FFT analysis was 
performed from the vertical and horizontal 
frequencies of the textile image. The results show 
that weft-warp densities can be determined by using 
just its’ image with 97 % accuracy.  
 
EXPERIMENTAL 
Materials and Methods 
Textile fabric samples were collected from various 
textile companies. The scanning process was 
performed with a 2400 dpi, HP Scanjet 4070 
Photosmart Scanner; then the MATLAB program 
was used for all analyses.  
 
Journal of Engineered Fibers and Fabrics 2 http://www.jeffjournal.org 
Volume 9, Issue 1 – 2014 
A warp-weft density prediction system was designed 
by using an image processing technique from a given 
textile fabric image. Firstly, 19 different woven 
fabrics were scanned in 2400 dpi resolution and then 
transformed into gray-scale images in the MATLAB 
program. In the second step, horizontal and vertical 
segmentation was achieved with the morphological 
image processing. For this segmentation, two 
different line structure elements were applied to the 
input image. The length of the line elements were 50 
pixel and the angles of the elements were 0 degrees 
and 90 degrees respectively.  Figure 1 shows the 
original batiste texture input image and vertical and 
horizontal segmented images.  Thirdly, the FFT 
analysis was carried out by getting horizontal signals 
from 400 different rows of the images. The average 
value of these signals gave us the horizontal 
frequency components of the image. The same 
process was applied on the vertical direction of the 
image to determine the vertical frequency 
components.  
 
 
FIGURE 1. (a) The raw fabric (b) vertical segmented image (c) 
horizontal segmented image. 
 
Image Processing Technique 
Images can be treated as two-dimensional data, and 
many signal processing approaches can be directly 
applied to image data. An image is always 
represented in one, or more, two dimensional arrays 
I(m,n). Each element of the variable I represents a 
single picture element, or pixel. The most convenient 
indexing protocol follows the traditional matrix 
notation, with the horizontal pixel locations indexed 
left to right by the second integer n and the vertical 
locations indexed top to bottom by the first integer m 
(Figure 2a). Another indexing protocol accepts non-
integer indexes. In this protocol, termed spatial 
coordinates, the pixel is considered to be a square 
patch, the center of which has an integer value. In the 
default coordinate system, the center of the upper 
left-hand pixel still has a reference of (1,1), but the 
upper left-hand corner of this pixel has coordinates of 
(0.5,0.5) (Figure 2b) [11]. 
 
FIGURE 2. (a)Pixel coordinate system (b) Spatial coordinate 
system. 
 
Frequency content of the waveform provides more 
useful information than the time domain 
representation. Determining of the frequency content 
of a waveform is termed spectral analysis. The 
Fourier transform (FT) method is the most 
straightforward spectral analysis technique. The 
Fourier transform approach takes advantage of the 
fact that sinusoids contain energy at only one 
frequency. The two-dimensional version of the 
Fourier transform can be applied to images providing 
a spectral analysis of the image content. It can be a 
very useful analysis tool for describing the content of 
an image. When applied to images, the spatial 
directions are equivalent to the time variable in the 
one dimensional Fourier transform, and this 
analogous spatial frequency is given in term of 
cycles/unit length (i.e., cycles/cm or cycles/inc) [12]. 
 
The two-dimensional Fourier transform in continuous 
form is given by: 
 
     (1) 
 
The variables  and  are frequency variables, 
they define spatial frequency and their units are in 
radians per sample. F(w1,w2) is a complex-valued 
function that is periodic in both  and . 
The discrete form of Eq. (1) is similar to their time 
domain analogs. For an image size of M by N, the 
discrete Fourier transform (DFT) becomes: 
 
     (2) 
 
Journal of Engineered Fibers and Fabrics 3 http://www.jeffjournal.org 
Volume 9, Issue 1 – 2014 
A simple square object (30 pixels by 30 pixels) is 
constructed. The resultant spatial frequency function 
is plotted both as a three-dimensional function and as 
intensity images (Figure 3, 4). 
 
 
FIGURE 3. The square images object. 
 
 
FIGURE 4. Fourier transform of the square object in Figure 3 
plotted a)as a function, b) as an image. 
 
RESULTS AND DISCUSSION 
In conclusion, as can be seen from Figure 5, the 
highest frequency values for both vertical and 
horizontal directions give us the exact value of warp 
and weft densities.  
 
 
FIGURE 5. The average horizontal and vertical frequency 
spectrum of the image batiste. 
The real and predicted values of weft-warp densities 
belong to 19 different fabric types can be seen in 
Table I. It can be concluded that the designed image 
processing system can predict the densities with 97 
% accuracy.  
TABLE I. Actual and Predicted Values of Warp-Weft Densities 
per cm at Different Fabric Types. 
 Actual value Predicted value 
Fabric 
types 
Warp/cm Weft/cm Warp/c
m 
Weft/cm 
Batiste 44 29 45,7 26,74 
Jeans 38 30 37 28 
Canvas 32 21 31,4 21,27 
Madras 50 36 47,61 32,7 
Damask 34 26 31,8 24,57 
Coating 26 22 25,4 20 
Velvet 26 23 28,9 21,7 
Printed 
Cotton 
38 30 40 31 
Opal 40 26 42 27 
Etamine 14 14 20 16 
Flamel 24 12 23,2 15,4 
Sailcloth 20 14 18,9 14,3 
Oxford 48 42 49 41,6 
Ottoman 64 16 66 13,8 
Panama 12 10 11,7 9,6 
Broadcloth 34 16 29,1 16,1 
Serge 32 26 30 23,7 
Fresco 24 20 24,9 18,7 
Lama 44 22 44 22 
 
The properties of the fabric types that were used in 
the experimental design can be seen below: 
 
Batiste: A thin plain-weave cotton or linen fabric; 
used for shirts or dresses. 
Journal of Engineered Fibers and Fabrics 4 http://www.jeffjournal.org 
Volume 9, Issue 1 – 2014 
Jeans: A coarse durable twill-weave cotton fabric, 
which is normally dyed dark blue. 
Canvas: A strong, durable, closely woven cotton 
fabric, which can be used for clothing or chairs or 
sails or tents. 
Madras: Hand loomed Indian cotton fabric in plaids, 
checks, or stripes all colorfully intermingled Because 
the yarn is dyed with natural vegetable dyes, colors 
run together (bleeding), producing a muted effect 
The weave itself has many slubs and imperfections. 
Damask: A fabric of linen or cotton or silk or wool 
with a reversible pattern woven into it a table linen 
made from linen damask having a woven pattern; 
"damask table linens. 
Coating: A heavy fabric suitable for coats. 
Velvet: A closely woven fabric, originally of silk, 
now also of cotton or man-made fibers, with a thick 
short pile on one side. 
Printed Cotton: A fabric with a dyed pattern pressed 
onto it (usually by engraved rollers). 
Opal: A finely woven cotton fabric that has a slight 
sheen. 
Etamine: A soft cotton or worsted fabric with an 
open mesh; used for curtains or clothing etc. 
Flannel: Light to heavyweight plain or twill weave 
fabric with a napped surface can be made of cotton or 
wool the brushing process creates insulating air cells 
that provide more warmth than plain cotton. 
Sailcloth: A type of heavy canvas fabric used to 
make sails. 
Oxford: Soft, somewhat porous and rather stout 
cotton shirting weave gives a silk like finish, also 
made from spun rayon, acetate, and other man-made 
fibers Oxford also means a woolen or worsted fabric 
with a grayish cast. 
Ottoman: A tightly woven plain weave ribbed fabric 
with a hard slightly lustered surface The ribbed effect 
is created by weaving a finer silk or manufactured 
warp yarn with a heavier filler yarn, usually made of 
cotton, wool, or waste yarn In the construction, the 
heavier filler yarn is completely covered by the warp 
yarn, thus creating the ribbed effect End uses for this 
fabric include coats, suits, dresses, upholstery, and 
draperies. 
Panama: A kind of fabric made from acrylic/cotton 
blend fibers. It’s warm and durable.  Panama Fabric 
has the barest trace of shrinkage and is very soft.  
Broadcloth: A densely textured woolen fabric with a 
lustrous finish. 
Serge: A fabric with a smooth hand that is created by 
a two-up, two-down twill weave.  
Fresco: A hot-weather fabric, but only available in 
dark colors/patterns. 
Lama: A light weight fabric for home decoration 
usage [13-16].  
 
CONCLUSIONS 
In the present study, an image processing technique 
was designed to determine warp-weft densities in 
woven textile fabrics. For this reason, 19 different 
woven fabric samples were used in the experimental 
setup. From the vertical and horizontal frequencies of 
textile images the FFT analysis was performed. 
Results show that warp and weft densities were 
measured with the 97 % accuracy. This approach 
demonstrates that with the help of this method, the 
personal effort which is made while counting the 
densities can be been eliminated. Thus the loupe 
usage was removed. Additionally, this method 
quickens the textile production measurements and 
makes the process easier. This technique also can be 
embedded to a pattern design program.   
 
ACKNOWLEDGEMENT 
The authors wish to acknowledge Ms. Zuhal IRS 
who is an undergraduate student in Marmara 
University for supplying the fabric samples to this 
research. 
 
REFERENCES 
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Journal of Engineered Fibers and Fabrics 5 http://www.jeffjournal.org 
Volume 9, Issue 1 – 2014 
[3] W. Yang, D. Li, L. Zhu et al., “A new ap-
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AUTHORS’ ADDRESSES 
Kazım Yildiz 
Volkan Yusuf Senyurek 
Zehra Yildiz 
Mustafa Sabri Ozen 
Marmara University  
Goztepe Campus 
Technical Education Faculty 
Department of Electronic 
Computer Education D412 
Kadikoy, Istanbul, 34722 
TURKEY 


A New Approach to the Determination of Warp-Weft Densities in Textile Fabrics by Using an Image Processing Technique

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A New Approach to the Determination of Warp-Weft Densities in Textile Fabrics by Using an Image Processing Technique

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