A METHOD OF OBJECTIVE EVALUATION AND GRADING OF TEXTILE OR FABRIC OR GARMENT APPEARANCE
FIELD OF INVENTION
The present invention relates to a method of objective evaluation and grading of textile or fabric or garment appearance. More particularly, the present invention relates to an objective method of quantifying seam pucker in stitched fabrics using grid projection technique. More particularly, in 3D analysis, the three dimensional shape of the sewn fabric samples (in terms of X-, Y- & Z coordinates) are obtained by 'shape from moire contouring' technique. Attributes related to variation of height profile (Z-values) are advocated as the measurers of quantifying appearance/pucker/wrinkles of textile materials. These attributes are well correlated with AATCC's subjective grades. A multiple linear regression analysis is performed to correlate subjective pucker grade and four attributes and an equation is obtained. These attributes and the multiple regression equation can be used to quantify pucker/appearance of textile materials.
BACKGROUND AND PRIOR ART OF THE INVENTION
The appearance of textile material/fabrics/garments includes pilling, wrinkles/creases and pucker in and around stitched lines. Of these the wrinkles and puckers are distortions of the plane of the otherwise smooth fabrics or stitched/sewn garments. The visual appeal of the garment is a principal factor deciding its value. Seam pucker, which is a wrinkled appearance along the seam, influences the appearance of garments/stitched fabrics to a considerable degree. Seam pucker appears when the material properties and sewing parameters are not suitably selected. The problem of seam pucker is receiving much attention in recent years with the development of micro fibre fabrics which are inherently prone to deformations during garment manufacture. Every garment manufacturer wants to eliminate this problem and it can only be accomplished if the evaluation is made and its causes being found. Considering the importance of the problem, various associations/organizations concerned with the textile industry and trade proposed different standards, the most commonly used is the one proposed by AATCC (American Association of Textile Chemists and Colorists). According to this standard, seam puckers are divided into five discrete levels and the fabric is
given a grade of the reference specimen which matches most nearly to the fabric specimen. The subjective evaluation suffers from limitations such as variability between experts' judgment, high evaluation time, biased-ness toward a particular color etc. There exists a need for objective evaluation of seam pucker. Also the five grades do not have equal intervals since it is not based on any quantitative measurement.
Many methods have been tried and reported that are based on non-imaging and imaging techniques to characterize wrinkles or pucker or undulations or distortion, in other words the appearance of textile materials. Yet no commercial system is available today quantify the appearance of textile materials.
The US patent 6842532 B2 describe a photometric stereo method to extract 3-dimensional surface of fabrics. The method comprising using a fixed digital camera positioned above a piece of fabric, shining at least two different parallel light beams from inclined directions on to the surface and capturing different reflected images of the surface with the camera, analyzing the captured images to derive values parameters of the surface (P and Q) based on intensities of light reflected from a number of evenly distributed points of the surface. The values P and Q are summations of surface gradients for a plurality of evenly distributed points in x- and Y- direction respectively. The values P+Q are calibrated against subjective grade analysis of the fabric (AATCC), and thereafter using calibrated P and Q to determine the grades of fabrics.
OBJECTIVE OFN THE INVENTION
The primary objective of the present invention is to provide a method of evaluation and grading of textile or fabric or garment appearance.
Another objective of the present invention is to provide an objective method of quantifying seam pucker in stitched fabrics using grid projection technique.
SUMMARY OF THE INVENTIONS
The present invention relates to an objective method of quantifying seam pucker in stitched fabrics using grid projection technique. Fabrics were scanned by a commercial white light scanner that uses fringe interference technique. The dimensions of the fabric sample are 32 cm X 15 cm. About 28 cm X 4 cm area of the fabric specimen were used for analysis. A white
light source situated vertically above the fabric project a pattern on the fabric. A CCD camera mounted at an angle captures the image of the fabric. 3-D cloud points were obtained from the scanner. Coordinate data lying in the 28 X 4 cm area of scanned fabric (sample size as per AATCC) were extracted from the available data using a CAD modeling software.
Next process in the sequence is data girding using interpolation. The cloud data of fabrics are randomly digitized points in space i.e. they have not been scanned at regular intervals in X and Y directions. But the computation of attributes describing seam pucker necessitates the data in regular interval in both X and Y directions. The cloud data which is random in 3D space were interpolated using bilinear interpolation using software. The spacing between successive data points is chosen to be about 1 mm. This results in a total of 281 X 41 points representing the fabric surface area of about 28 X 4 cm.
The cloud data represents various points of the surface of the fabric which is positioned in 3D space. Consequently the data contains negative coordinate values. To be physically meaningful, and for analysis purpose data points should contain only positive coordinate values. For this, the data was shifted to first quadrant of 3D space. To do this shifting, minimum value of X was multiplied by -1 and added with all values of X. In the same way, Y and Z values were shifted. This results in 3D data which is present only in first quadrant. The data contains coordinate values which extends from 0 to 280mm in X- direction and 0-40mm in Y- direction. These preprocessed data were used for determining attributes describing seam pucker.
To objectively evaluate seam pucker, various attributes characterizing the seam pucker were determined from 3-D data of fabric samples. The attributes essentially describes the variation in the shape of the fabric. Each attribute describes the variations in the height data (Z data) in a different way. The attributes 'Mean deviation', 'Variance' and 'Power spectral density' are correlated well with Mean pucker grade (R2~=0.89) and can be used to objectively evaluate the seam pucker. The attribute 'surface area ratio' is correlated better than other attributes with R2=0.92. This indicates the superiority of Surface area ratio for objective evaluation.
A multiple linear regression model has been developed by combining all these four attributes is correlated well with Subjective pucker grades as per AATCC standards (R2=0.95), which is higher than those obtained for correlations between individual attributes and the said pucker
grades. This equation can be used for objective evaluation of seam pucker in garments. This technique of using white light does not suffer from the influence of texture and colour of fabrics.
BRIEF DESCRIPTION OF TABLES
Table 1 Typical 3D data
Table 2 Final preprocessed data
Table 3 Seam pucker grading by subjective evaluation
Table 4 Subjective and objective grades of fabrics
BRIEF DESCRIPTION OF FIGURES
Figure 1 shows the fabric sample
Figure 2 shows the top view of scanned fabric samples PD1 and PC5
Figure 3 shows the wave profile of sample PC5 at +20 mm from seam line
Figure 4 shows the wave profile of sample PC5 at -20 mm from seam line
Figure 5 shows the wave profile of sample PC5 at seam line
Figure 6 shows the wave profile of sample PD1 at +20 mm from seam line
Figure 7 shows the wave profile of sample PD1 at - 20 mm from seam line
Figure 8 shows the wave profile of sample PD1 at seam line
Figure 9 shows the scatter plots of mean pucker grade and mean deviation
Figure 10 shows the scatter plots of mean pucker grade and variance
Figure 11 shows the scatter plots of mean pucker grade and surface area ratio
Figure 12 shows the scatter plots of mean pucker grades and power spectral density
Figure 13 shows the normal probability plots of residuals
Figure 14 shows the scatter plots of subjective and objective pucker grades.
Figure 15 shows fringe generating system.
DEATIL DESCRIPTION OF THE INVENTION
Accordingly, the present invention relates to a method of objective evaluation and grading of textile or fabric or garment appearance comprising the steps of a) scanning fabric by a white light scanner that uses fringe interference technique, b) mounting CCD camera at a predetermined angle to capture the various images of said fabric, c) obtaining 3-D dimensional data (cloud points) from the scanner, d) extracting the coordinate data from the 3
dimensional data of step (c) lying in predetermined area of scanned fabric, e) arranging the 3-
D dimensional coordinate in data girding by using interpolation, f) scanning the fabric data in
regular interval in both X, Y and Z directions for determining attribute describing seam
pucker, g) determining various attributes of seam pucker from 3-D data of fabric samples,
wherein the said attributes are mean deviation, variance, power spectral density and surface
area ratio, h) correlating the attributes of step (g) with subjective pucker grades as per
AATCC (American association of textile chemical & colorist) for objective evaluation and
grading of textile or fabric or garment appearance.
One aspect of the present invention wherein, the seam puckers are divided into five discrete
levels and the fabrics is given a grade of the reference specimen.
Yet another aspect of the present invention, wherein the fabric in step (a) is a stitched fabric.
Yet another aspect of the present invention, wherein in step (a) the white light source is
preferred to have vertically above the fabric.
Yet another aspect of the present invention, wherein in step (e) the cloud data which is
random in 3-D space being interpolated by using bilinear interpolation to get z values at a
fixed interval along the stitched line and normal to stitched line.
Yet another aspect of the present invention, wherein in step (g) the attributes such as mean
deviation, variance, power spectral density surface area ratio are combined by multiple linear
regression equation such as herein described for comparing with subjective pucker grades.
Accordingly, the present invention relates to a method of evaluation and grading of textile or fabric or garment appearance. More particularly, the present invention relates to an objective method of quantifying seam pucker in stitched fabrics using grid projection technique. More particularly, 3 D technique based upon height data of the fabrics. The method relays on the 3D mapping of the fabrics. Variation of height profile of the fabrics assessed through various attributes has very high correlation with respect to AATCC s pucker grades.
Accordingly, the present invention relates to data girding of 3D coordinates (cloud points) of scanned data of fabric sample by bilinear interpolation to get z- values at fixed intervals of 1 mm in x- and y- directions for a sample size of 28 X 4 cm (along the stitched line and normal to stitched line). Transposing the thus 3D data into first quadrant (data shifting).
Demonstrations of various attributes of Z-values of stitched fabric with respect to the reference plane such as mean deviation, variance, surface area ratio, power spectral density and multiple regression equation and relating these attributes to the grades of pucker/appearance/shape/irregularity/undulation of fabrics/textile materials.
The dimensions of the fabric sample scanned are 32 cm X 15 cm. The cloud point's data for a typical fabric is shown in Table 1.
The cloud data which is random in 3D space were interpolated using bilinear interpolation using software. The spacing between successive data points is about 1 mm resulting in a total of 281 X 41 points representing the fabric surface area of about 28 X 4 cm shown in Figure 1. The X-, Y- and Z- coordinates were shifted to first quadrant. An extract of the preprocessed data is shown in Table 2.
The 3D visualization of the scanned fabrics is useful in analysis and visual perception of the shape of the fabric. The cloud data of scanned image can be visualized as surface or a wireframe in 3D space. Samples PC5 and PD1 are only used here for demonstration, each having a subjective mean grade of 4.9 and 1 respectively. Figure 2 illustrates the top view of surface of fabric samples PD1 and PC5. These were obtained by first creating a mesh from the XYZ data of the fabric and then plotting a surface using this mesh. These reconstructed surfaces resemble the original profile of fabric. Different grades of fabric samples assume different shape based on pucker level. The difference in the shape of the fabric can be easily perceptible from these reconstructed surfaces.
Seam pucker can be seen as a series of waves running parallel to seam line. The nature of these waves differs from sample to sample. Also the nature of wave varies within a sample depending on the position of wave from seam line. This can be seen from a two dimensional plot of these waves at three positions across the seam line. The positions considered for plotting are seam line and 20 mm from seam line on both sides of seam line. Both amplitude and wavelength of these waves is low at the seam line. As the distance from seam line increases, both amplitude and wavelength increases. Figure 3 shows the wave profile of sample PC5 at +20 mm from seam line. The amplitude varies from 0 to 2 mm and the wavy nature is also absent. Figures 4 & 5 show the wave profile at -20 mm and + 20 mm
respectively from seam line. Both waves have amplitude which varies from 0 to 2 mm. The wave profiles at these three different positions remain almost same indicating less pucker.
Figure 6 illustrates the wave profile of sample PD1 at +20 mm from seam line. This clearly indicates the presence of wavy nature. The magnitude varies from 3 to 12 mm in this case. The magnitude varies from 0 to 12 mm in wave profile at -20 mm from seam line given in Fig 7. The magnitude variation of wave profile at seam line is about 6-11 mm. This can be seen in Fig 8. The magnitude of variation of height is low at seam line compared with those at -20 mm and +20 mm. This variation is attributed to the high severity of pucker present in the sample. Notations:
MG is Mean pucker grade
MD is Mean deviation of sample
V is Variance
AR is Surface area ratio
PSD is Power spectral density
Y is Response variable,
X 1, X2, X3, X4 are Regressor variables,
ß0, ß1,ß2, ß3 ß4 are Regression coefficients.
To objectively evaluate seam pucker, various attributes characterizing the seam pucker have to be determined from three dimensional data of fabric samples. The attributes essentially describes the variation in the shape of the fabric. Each attribute describes the variations in the height data (Z data) in a different way. In this study, four attributes namely Mean deviation, Variance, Surface area ratio and Power spectral density were utilized for describing the variations in height data and characterizing the seam pucker. The mean deviation (MD) and variance (Var) of n data points are:
(Equation Removed)
The Fourier transform of a discrete function of two variables f(x, y),
x=0,l,2 M-1
y=0,l,2(Equation Removed)
N-l is defined as
for u=0, 1, 2 M-l
v=0, 1, 2 N-l
In general, the components of Fourier transform are complex numbers and they can be represented in polar quantities.
(Equation Removed)

Where,
(Equation Removed)

is called magnitude or spectrum of Fourier transform.
(Equation Removed)

is called phase angle ox phase spectrum of Fourier transform. Power spectral density is defined as square of Fourier spectrum.
P(u,v) = R2(u,v) + I2(u,v) R (u, v) and I (u, v) in above equations are called real and imaginary components of transform respectively.
Surface area ratio measures the extent to which the surface area of the puckered fabric deviates from that of the fabric without pucker. The surface area ratio is formally defined as the ratio of surface area of fabric to the projected area of fabric in XY plane.
Area ratio= Fabric surface area /Projected area
The attributes determined from the preprocessed data have to be correlated with present method of subjective evaluation. Various methods of subjective evaluation is available, the most widely used being AATCC standard 88B. According to this standard seam pucker was classified into five grades 1 to 5, grade of 1 refers to heavily puckered fabric and 5 refers to fabric with very little or no pucker at all. It should be noted that AATCC provides only reference photograph, not reference specimens. The sewn fabrics are compared with this reference photograph. The grade of fabric is the grade of the reference specimen which
matches most nearly to sample fabric specimen. About 10 judges were asked to grade the seam pucker of 25 samples. The final pucker grade of each sample is the average of grades given by 10 judges. The results of subjective evaluation conducted are shown in Table 3. The mean pucker grade varies from grade 1 to grade 4.9. A pucker grade of 1 was assigned to sample PD1 and a mean pucker grade of 4.9 was assigned to sample PC5. Each of the attributes describing the seam pucker was correlated with mean pucker grade using regression analysis and a regression equation was obtained for each of them. The scatter plot of Mean deviation of samples and their assigned Mean pucker grades given by the judges is shown in Fig 9. The scatter plot of Variance against Mean pucker grade is shown in Fig. 10.
The linear relation between mean deviation and mean pucker grade is clearly seen. A linear regression equation correlating Mean pucker grade and Mean deviation was obtained using simple linear regression analysis. The obtained equation is MG = -2.2781 *MD + 4.8998 and the coefficient of determination R2=0.8933. The adequacy of the fitted model is clearly evidenced by a high coefficient of determination.
A regression equation correlating Mean pucker grade and Variance was obtained using simple linear regression analysis. The equation obtained is MG- -0.8483*ln (V) + 2.67428998 and the Coefficient of determination R2=0.8907. The high value of R2 indicates the adequacy of the fitted regression equation. The scatter plot of Mean pucker grade and surface area ratio is shown in Fig 11. A regression equation relating Mean pucker grade and surface are ratio was obtained using simple linear regression analysis. The obtained equation is MG = 5E+08* e 18.473*AR and the coefficient determination R2=0.9247. The coefficient of determination obtained in this case is higher than those obtained using attributes Mean deviation and Variance. Due to this, Surface area ratio is a better attribute for objective evaluation than Mean deviation and Variance.
The scatter plot of Mean pucker grade and Power spectral density is shown in Fig 12. A regression equation relating Mean pucker grade and Power spectral density was obtained using simple linear regression analysis. The obtained equation is MG = -0.9348*ln (PSD) + 5.2978 and the coefficient of determination R2 = 0.895. The adequacy of the model can be inferred from a high coefficient of determination.
Each of the attributes describing seam pucker and mean pucker grade are correlated to higher level. It is intuitive to correlate all the attributes simultaneously to mean pucker grade. A multiple linear regression analysis was performed to fit the regression model of the form, Y=ß0 + ß1X1+ ß2 X2 + ß3 X3+ ß4X4. The response variable of multiple linear regressions is Mean pucker grade and Regressor variables are seam pucker attributes namely mean deviation, variance, surface area ratio, and power spectral density. The multiple linear regression equation obtained is MG = 35.3 - 3.24 * MD+ 0.852 * V - 29.9 * AR - 0.00293 * PSD and the coefficient of determination R2=0.953. The adequacy of the fitted model can be verified using the residual analysis of fitted regression model. This is done by analyzing the normal probability plot of residuals. As can be seen from Fig 13, the calculated residuals lie on a straight line, indicating that the fitted multiple linear regression equation is adequate. The multiple regression equation can be used to objectively evaluate the seam pucker in fabrics. Table 4 shows the mean pucker grades of fabric samples obtained using subjective evaluation and objective pucker grade obtained using multiple regression equation.
Figure 14 illustrates the scatter plot of subjective and objective pucker grades. The subjective and objective grades are correlated in a linear fashion with a correlation coefficient of 0.9764. The high value of correlation coefficient indicates that the multiple regression equation can be used for subjective evaluation. This is expected because the developed model has a very high coefficient of determination of 0.953. Also the ambiguity in subjective evaluation can be eliminated by objective evaluation using this multiple regression equation.
Conclusions
The objective evaluations of seam pucker using 3D image analysis are developed. The 3D method relies on moire contouring principle for reconstruction of 3D shape of fabric. The reconstructed shapes of fabrics are used for determining seam pucker attributes viz., Mean deviation, Variance, Power spectral density and Surface area ratio. The attributes 'Mean deviation', 'Variance' and 'Power spectral density' are correlated well with Mean pucker grade (R2~=0.89) and can be used to objectively evaluate the seam pucker. The attribute 'surface area ratio' is correlated better than other attributes with R2=0.92. This indicates the superiority of Surface area ratio for objective evaluation. A multiple linear regression model has been developed by combining all these four attributes is correlated well with subjective
pucker grades as per AATCC standards. The coefficient of determination using the above equation is R2=0.95, which is higher than those obtained for correlations between individual attributes and the said pucker grades. This equation can be used for objective evaluation of seam pucker in garments.
In 2D image analysis, the attributes 'Mean deviation' and 'Variance' of profile lines correlate moderately with subjective grades. The coefficient of determination R2 varies from 0.635 to 0.67. The lower values of R2 are attributed to smaller scanned area of fabric used in the study. There is further scope left to improve evaluation of seam pucker based on 2D image analysis by using considerably large scanned area of fabric than that used in this reported study and having uniform illumination (as the illumination of the present system was not uniform) of the fabric by having better light source.
The novelty of the present invention is based upon 3 D technique based upon height data of the fabrics. The method relays on the 3D mapping of the fabrics. Variation of height profile of the fabrics assessed through various attributes has very high correlation with respect to AATCC's pucker grades.
Accordingly, the present invention relates to data girding of 3D coordinates (cloud points) of scanned data of fabric sample by bilinear interpolation to get z- values at fixed intervals of 1 mm in x- and y- directions for a sample size of 28 X 4 cm (along the stitched line and normal to stitched line). Transposing the thus 3D data into first quadrant (data shifting).
Demonstrations of various attributes of Z-values of stitched fabric with respect to the reference plane such as mean deviation, variance, surface area ratio, power spectral density and multiple regression equation and relating these attributes to the grades of pucker/appearance/shape/irregularity/undulation of fabrics/textile materials.
Table2 Typical 3D data
(Table Removed)
Table 2 Final preprocessed data
(Table Removed)
Table 3 Seam pucker grading by subjective evaluation
(Table Removed)
Table 4 Subjective and objective grades of fabrics
(Table Removed)

WE CLAIM
1. A method of objective evaluation and grading of textile or fabric or garment
appearance comprising the steps of
a) scanning fabric by a white light scanner that uses fringe interference technique,
b) mounting CCD camera at a predetermined angle to capture the various images of said fabric,
c) obtaining 3-D dimensional data (cloud points) from the scanner,
d) extracting the coordinate data from the 3 dimensional data of step (c) lying in predetermined area of scanned fabric,
e) arranging the 3-D dimensional coordinate in data girding by using interpolation,
f) scanning the fabric data in regular interval in both X, Y and Z directions for determining attribute describing seam pucker,
g) determining various attributes of seam pucker from 3-D data of fabric samples, wherein the said attributes are mean deviation, variance, power spectral density and surface area ratio,
h) correlating the attributes of step (g) with subjective pucker grades as per AATCC (American association of textile chemical & colorist) for objective evaluation and grading of textile or fabric or garment appearance.
2. The method as claimed in claim 1, wherein seam pucker are divided into five discrete levels and the fabrics is given a grade of the reference specimen.
3. The method as claimed in claim 1, wherein the fabric in step (a) is a stitched fabric.
4. The method as claimed in claim 1, wherein in step (a) the white light source is preferred to have vertically above the fabric.
5. The method as claimed in claim 1, wherein in step (e) the cloud data which is random in 3-D space being interpolated by using bilinear interpolation to get z values at a fixed interval along the stitched line and normal to stitched line.
6. The method as claimed in claim 1, wherein in step (g) the attributes such as mean deviation, variance, power spectral density surface area ratio are combined by multiple linear regression equation such as herein described for comparing with subjective pucker grades.