image analysis of hma to characterize the aggregate
TRANSCRIPT
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Asphalt Research Consortium Partner
Image Analysis of HMA to Characterize the Aggregate Orientation
Kyoungchul Kim and Carl JohnsonDept. of Civil & Environmental
Engineering
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ARCProgram Comparisons
Target Area Detection
Aggregates are uniformly distributed
Smaller than 1mm size aggregate can not be detected
Assumption:Gagg=2.5-2.7Gab=1.05-1.08Air Voids=8%Height=108 mm
Target AreaMaximum:67.4%Minimum:64.2%
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Procedures of KCKIM (1) Image Loading From Data Folder
Input Image File Name and Click Open Image
An mage for processing must be in data folder
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Procedure of KCKIM (2) Selection of Region of Image for the Processing
Input X and Y, Width and Height for the Interest Region of Image
Click Preview
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Procedure of KCKIM (3)Zoom In Selection Region
Click Zoom In
Image applied with Equalization, Stretching, Smoothing, and Filtering
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Procedure of KCKIM (4)Analysis and Labeling
Input Size of aggregate and Click Result
In the Results Folder,
Analysis Data (Text File) and Processing Images of Each Step (JPEG File)
are Stored
Processing Techniques
1.Edge Detection, 2.Thresholding
3.Region Growing
4.Erosion and Dilation 5.Opening and Closing
6.Spliting
7.Labeling
8.Data Stored
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Image Processing from Image Pro
Raw Image Processed with density indexMastic: ≤164
Aggregate: >164
Fine Image Processing
Using Image-Pro software
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(1)Thresholded Image
Using Image-Pro software From KCKIM
1st Threshold=0.65 and Region Growing
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Using Image-Pro software From KCKIM
Aggregate Area Detection=50.38% Aggregate Area Detection=55.28%
1st Threshold=0.65 and Region Growing
(2)Final Segmented Image
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From KCKIM
Aggregate Area Detection=56.01% Aggregate Area Detection=57.14%
1st Threshold=0.75 and Region Growing
(2)Final Segmented Image
1st Threshold=0.70 and Region Growing
From KCKIM
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N=200 Mixture
Original Image after Equalization
From KCKIM
Threshold Image
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N=200 Mixture
Final Image
From KCKIM
Labeling and Analysis
Area Detection=58.26%
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N=200 MixtureAggregate Orientation
From KCKIM
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Internal Structure Analysis Using Image Analysis Technique� The direction distribution of aggregate orientation
approximated by harmonic series expansion (Masad 1998)
� Absolute average angle of orientation θ, orientations of aggregates
� Vector magnitude Δ, Complete random distribution of the orientation=0%, Exactly the same direction=100%
)sincossin2cos1()(2
22
2
2 iiiiaiABAnn θθθθθ −++=
θ =θ
k∑N
∑ ∑+=∆22
)2cos()2sin(100
kk
Nθθ
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Proposed Internal Structure InterpretationIn the Image Processing
� N is the number of aggregates with a diameter larger than 1.18mm in the image. XK is either the largest length (LK) or the area (AK) of each aggregate object in the image.
� Vector magnitudes, the value of ΔN, ΔL, and ΔA, varies from zero to one. Complete random distribution of the orientation will give a vector magnitude of zero
N
k∑=
θθ ∑ ∑+=∆
22)2cos()2sin(
100kkN
Nθθ
∑
∑ ∑+=∆
K
KKKK
AorL
X
XX22
,
)2cos()2sin( θθ
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Comparison of N=30 and N=200
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 2 4 6 8 10> Aggregate Size> Aggregate Size> Aggregate Size> Aggregate Size
(standard sieve size)( standard sieve size)( standard sieve size)( standard sieve size)
Del
ta N
Del
ta N
Del
ta N
Del
ta N
N=30
N=200
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 2 4 6 8 10> Aggregate Size> Aggregate Size> Aggregate Size> Aggregate Size
(standard sieve size)(standard sieve size)(standard sieve size)(standard sieve size)
Del
ta L
Del
ta L
Del
ta L
Del
ta L
N=30
N=200
Aggregate Orientation
From KCKIM
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Comparison of N=30 and N=200
0.00
0.05
0.10
0.15
0.20
0.25
0 2 4 6 8 10> Aggregate Size> Aggregate Size> Aggregate Size> Aggregate Size
(standard sieve size)(standard sieve size)(standard sieve size)(standard sieve size)
Del
ta A
Del
ta A
Del
ta A
Del
ta A
N=30
N=200
Aggregate Orientation
From KCKIM