The document evaluates the robustness of wavelet texture analysis (WTA) for characterizing carbon fibre composite surfaces. WTA uses the discrete wavelet transform to decompose images into orientation and scale-based detail coefficients, from which texture features are extracted. Principal component analysis is applied to classify samples by surface finish grade. The study examines WTA's sensitivity to common imaging errors like translation, rotation, and dilation. Results show the method maintains good discrimination between grades despite such errors, demonstrating it is a robust automated approach to surface assessment.
1. Evaluation of the Robustness of Surface
Characterisation of Carbon Fibre Composites
Using Wavelet Texture Analysis
Associate Professor Stuart Palmer
Faculty of Science and Technology
Deakin University, Australia
Dr Wayne Hall
Griffith School of Engineering
Griffith University, Australia
1
2. Introduction
The mechanical properties of composites are important
for their structural performance
But, quality of finish on visible surfaces is also important
for customer satisfaction
Currently, surface finish assessment is often based on
human observation, which is time consuming, subjective
and not appropriate for automation
The wavelet transform has the ability to effectively
characterise many engineering surfaces
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3. The 2D discrete wavelet transform (2DDWT)
Produces a nearly orthogonal decomposition of an
image into coefficients that separately represent the
information in the image in:
• 3 orientations (horizontal, vertical and diagonal);
• and, different scales (scale=characteristic dimension)
The 2DDWT is an iterative decomposition where the
scale doubles each step, until the limit of the image
resolution is reached
3
5. The 2D discrete wavelet transform (2DDWT)
Original image
Decomposition cD1v cD1d
cA1 cD1h
level 1
5
6. The 2D discrete wavelet transform (2DDWT)
Original image
Decomposition cD1v cD1d
cA1 cD1h
level 1
Decomposition
cA2 h
cD2
v
cD2 cD2d
level 2
6
7. The 2D discrete wavelet transform (2DDWT)
Original image
Decomposition cD1v cD1d
cA1 cD1h
level 1
Decomposition
cA2 h
cD2
v
cD2 cD2d
level 2
Decomposition cAJ cD Jh
v
cD J cDJd
level J
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8. The 2D discrete wavelet transform (2DDWT)
Original image
Decomposition cD1v cD1d
cA1 cD1h
level 1
Decomposition
cA2 h
cD2
v
cD2 cD2d
level 2
Decomposition cAJ cD Jh
v
cD J cDJd
level J
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9. The 2D discrete wavelet transform (2DDWT)
It is possible to selectively re-assemble images:
Detail coefficients from Detail coefficients from Original image
levels 2-4 levels 5-6
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10. Wavelet texture analysis (WTA)
Energy measure computed for detail coefficient sets:
cD1h cD1v cD1d
h
cD2
v
cD2 cD2d
cAJ cD Jh
v
cD J cDJd
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11. Wavelet texture analysis (WTA)
Energy measure computed for detail coefficient sets:
1 k 2
E jk cD j F J j 1; k h, v , d E1h E1v E1d
M N
where:
j is the wavelet analysis scale/level
k is the wavelet detail coefficient set
E 2h
v
E2 E2d
orientation (horiz., vert. or diagon.)
J is the maximum analysis scale/level
M×N is the size of the coefficient set
and:
2
2
A a ij v
E Jd
F i, j
cAJ E Jh EJ
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12. Wavelet texture analysis (WTA)
A texture feature vector is created from the energy set
for each sample image:
[E1h, E1v, E1d, E2h, E2v, E2d, … EJh, EJv, EJd]
The texture feature vectors for all samples are used as
the inputs for principal components analysis (PCA)
PCA uses linear algebra to transform a set of correlated
variables into a smaller set of uncorrelated variables
called ‘principal components’
PC1=l1E1h+l2E1v+l3E1d+l4E2h+l5E2v+l6E2d…
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13. Method
Typical clear resin sample images for the three grades of
surface finish
Grade 1 Grade 2 Grade 3
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16. Robustness of the WTA method
Given these promising results, the following work
presents an evaluation of the robustness of the WTA
method to common process errors that can occur in the
imaging of material samples; those being:
• horizontal and/or vertical translation;
• rotation; and
• dilation
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64. Conclusions
The results obtained indicate that the WTA method is
robust to:
• significant horizontal and/or vertical translations of the
sample being imaged;
• significant rotation of the sample being imaged; and
• significant dilation of the sample being imaged
Gross rotation and/or dilation of the sample being
imaged can impact of the repeatability of the WTA
method
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65. Thank you for your time
Presentation: http://ow.ly/diQxn (~40 MB)
65
Notas del editor
For each of the 3 grades,[CLICK] a 1st sample was windowed from the centre of the sample panel. Here we show grade 3.[CLICK] the window was then translated to the left/west[CLICK] the window was then translated up/north-west[CLICK] the window was then translated right/north[CLICK…] and so on for 9 samplesThe PC1 scores are computed using the previously established calibrationVariation in the PC1 scores between the translated images is observed within each grade; however there is no over-lap between the grades, indicating that the WTA method is robust to significant horizontal and/or vertical translation of the sample being imaged