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The study of Digital Image Process Technique On the Analysis of Asphalt Concrete

Jyh-Dong Lin1, Shih-Huang Chen2, Chiuan-Chi Zung3, Pei Liu4, Ching-Tsung Hung 5 1Professor, Dept. of Civil Engineering, National Central University, Taiwan 2 Assistant Professor, Dept. of Transportation & Logistic, Toko University, Taiwan 3 Master, Dept. of Civil Engineering, National Central University, Taiwan 4Assistant Professor, Dept. of Traf.. Eng. & Manag. , Feng Chia University, Taiwan 5Ph.D. candidate, Dept. of Civil Engineering, National Central University, Taiwan

Abstract describe mechanical behavior of asphalt With the swift advancement of computer concrete. software and hardware techniques, applications of digital image process are no longer limited to remote sensing or military purposes. Nowadays, Introduction researches in the field of civil engineering utilize The emersion of digital image processing this technique. The technique of digital image technique can be retrospect to late 1940’s. This process and the incorporated relating software technology caught scientists’ attention with its can help us. Overcome the time-consuming and powerful ability to quantitatively analyze various labor-intensive problem in analyzing the physical phenomena. However, due to the fact aggregate structure of asphalt concrete. In the that both hardware and software used for digital mean time, it offers a rapid and objective image processing were expansive, it was limited quantitative analysis. Consequently, significant to remote sensing and military applications for parameters that can hardly be obtained from quite a period of time. Lately, with more traditional experiments can then be easily and powerful and much cheaper computer related accurately collected. products, researchers in the field of civil The objective of this study is mainly focused engineering gradually began to adopt digital on examining the particle shape characteristics of image processing to study composition of asphalt the coarse aggregates by digital image analysis concrete. More objectively quantitative analyses, instead of the traditional way – which is more in compared with traditional methods, can be subjective. It’s expected to reach the goal of time obtained with this technique. In the meantime, and labor saving without losing accuracy. Thus, characteristic parameters, which can’t be we can make a great deal of quantitative analysis obtained before, can be defined by using digital through digital image analysis. On the other image processing technique. hand, we can discuss what’s the effect of changes of aggregate arrangement on mechanical behavior of asphalt concrete. Classification of Image The result reveals that application of the rapid The ISODATA (Iterative Self-Organizing and objective quantitative image process on Data Analysis Technique) are widely used in analysis of the shape of aggregate is feasible. unsupervised and supervised classification at Meanwhile, important parameters, such as taiwan. The ISODATA algorithm is a widely aggregate orientation, obtained with the digital used clustering method to partition the image image process are also found adequate to data in the multispectral space into a number of

1 spectral classes. While unsupervised, the part in terms of their convenience and ISODATA automatically select spectral classes portability. Adequacy and efficiency of these inherent to the data and can normally complete two devices for image generation was clustering swiftly. Consequently, it is suitable to evaluated. be applied to handle vast amount, yet close and  Process part: simple image data. The process part digitizes images conveyed With user specified maximum number of from the input part. This part is generally a clusters, maximum % of pixels whose class software scheme. With different algorithms, values are allowed to be unchanged between quantity of generated data and speed of iterations, maximum number of iterations, processing calculation can be very different. minimum numbers of members in a cluster, In this study, a 2-D scheme was developed maximum standard deviation, and minimum and applied. distance between cluster means, the ISODATA algorithm will allocate central mean (μ) of each  Storage part: cluster. Each pixel is compared to each cluster Equipped for data storage.Compact disks, mean and assigned to the cluster whose mean is hard disks, MO, etc., are of this kind. The closest in Euclidean distance, and a new cluster storage part needs to have enough available center is computed by averaging the locations of space for great amount of generated image all the pixels assigned to that cluster. The Sum of data. Squared Errors (SSE) computes the cumulative squared difference (in the various bands) of each  Output part: pixel from its cluster center for each cluster Equipped for providing processed images for individually, and then sums these measures over visual inspection. Computer monitors, all the clusters. The algorithm will stop either printers, and plotters are generally adopted for when the number of iteration threshold is reached this purpose. or the max % of unchanged pixel threshold is reached [4]. 2. Processing and analysis on image The operation processes of Processing and analysis can be divided into six steps as follows [3] Digital image processing technique  Step1:Capture: 1. Fundamental structure Image of interested object needs to be captured by using a video camera, scanner, or Fundamental structure of a dip system camera, and stored in a computer system for consists of four main parts, i.e., input part, further operation. process part, storage part, and output part. Brief description of these parts is given in  Step2:Coding: the following sections. Image coding reduces the storage requirement  Input part: for an image by removing redundant information. The image may be reconstructed The input part is responsible for creating exactly (i.e., Exact Coding). Standard coding images of certain planes of test samples. techniques may be used. On the other hand, Resolution provided by this part is critical. the image may be reconstructed so that a Various instruments, such as digital camera, human observer notices little or no video camera, and scanner, etc., can be degradation (i.e., Fidelity Coding). applied for this purpose. In this study, a digital camera and scanner were used as input  Step3:Enhancement:

2 After an image stack has been acquired it may of roundness in 1932. He defined roundness be preprocessed to improve image quality as prior to image restoration. The preprocessing A usually involves application of image filters to Roundness = …(1) the entire data set to remove noise and B artifacts, smooth or sharpen the images, or to A=Average radius length calculated from diagonals of correct for problems with contrast and/or aggregate projection B= Radius of the largest inscribed circle of aggregate brightness. While these filters are generally projection performed as preprocessing steps, they can also be carried out after an image has been Russell and Taylor [7] compared Wadell’s reconstructed from the image stack. definition of roundness with photos of aggregate and assigned five classes of  Step4:Restoration: roundness as angular (0.00~0.15), sub-angular The objective of image restoration is to (0.15~0.30), sub-rounded (0.30~0.50), reconstruct the original image from its rounded (0.50~0.70), and well-rounded degraded version. Two groups of image (0.70~1.00). Pettijohn [8] adopted restoration techniques, Deterministic methods geometrical ratio to redefine the five classes and Stochastic techniques, are generally of roundness. On the other hand, Powers [9] applied. suggested a six classes roundness chart by adding the class of very-angular  Step5:Detection: Feature detection creates an image that  Sphericity (φ) contains information about the presence of certain types of features. Edges are common Wadell [6] and Krumbein [10] further features. The need for performing feature adopted the 3-D concept to define shape detection is usually motivated by the desire of characters. Sphericity is thus suggested as condensing the rich intensity pattern to a more [volume of aggregate/volume φ= compact representation for further processing. of the circumscribed circle]1/3  Step6:Description: φ= ( (6abc) /(bc) )1/3 …..(2) With the output binary image, evaluation on image characteristics, such as circumference, φ= [(bc / a2)1/3, area, shape index, etc., can be performed.

where a, b, and c are the long, medium, and short axle lengths of aggregate, respectively. Evaluation of composition  Shape factor characteristics of aggregate There are many other indexes proposed, Composition characters of aggregates play besides roundness and sphericity, to identify an important role in the mechanical behavior of shape characters of aggregate. Yudhbir and asphalt concrete. Generally, composition Rahim [11] defined planar index - shape characters refer to the shape and composition of factor (SF) from image processing output as aggregate. Three types of indexes are customarily used to quantify aggregates’ shape SF = A /4P2 characters [5]. where A is the area of aggregate, and P is the  Roundness and angularity circumference of aggregate, respectively. Aggregate is nearly spherical when SF is Wadell [6] first brought up the concept close to 1. Researches have shown that shape

3 factor can be closely correlated to roundness program specifically suit for this study was (as shown in figure 1). Consequently, developed. The program was written in C++ roundness of aggregate can be effectively language, and is composed of two subprograms estimated by using shape factor. for distinguishing image and calculation, respectively. 1. Distinguishing image This subprogram is mainly used to distinguish, and calculation of image pixels. With the input size of acquired image, the program will automatically separate asphalt and aggregates into two clusters and produce binary images (as shown in figure 2 and 3).

Figure 1. Correlation between shape factor and roundness As for the composition of aggregates, consistency ratio (Δ) was proposed by Reiche [13] using vector method in 1938. He suggested using north-south and east-west measures to Figure2.Original image Figure3. binary image evaluate the consistency of a group of particles. 2. Calculation In 1939, Krumbein [14] modified the correction While the binary image is produced, with angle to 2θk to prevent data from been original sample dimension and image unnecessarily neglected. The formula Krumbein acquiring resolution, various parameters, such proposed to calculate  is shown in equation (4), as shape factor, aggregate scattering pattern, 100 2 2 and flatness -elongation rate, can be calculated △﹦ (sin 2k )  (cos2k ) n ……….(4) using this subprogram. Three schemes based on different parameters (length of long axle, where sin2θk and cos2θk are north-south and length of short axle, and radius of inscribed east-west composition factors, respectively, and circle) for analysis of aggregate characters are n is the number of particles. Values of used in this subprogram. Minimum size of consistency ratio vary from 0% to 100%, which aggregate for calculation to facilitate analysis represent particles scatter from random to can be specified. Moreover, by specifying the consistent (Curray [15]). ratio between long axle and short axle, the flatness-elongation rate (F/E rate) of an Image processing technique applied aggregate can be calculated. Examples of Due to the fact that image processing output from this subprogram are given in software is generally expansive, an computer figure 4 and table 1, 2.

4

Figure 4. Aggregates been labeled by program

Table1、Output summary calculated from binary image

Sample ID △ RLS < 3 RLS > 3 F/E rate T1 16.53 14 41 27.53

Table2、Sample aggregate characters output Radius Long axle Short axle Area Area of equiv. Circumference Inclination ID Angularity SF length length (mm2) % circle (mm) (o) (mm) (mm) (mm) 1 273.70 3.40 18.67 82.78 1.41 0.502 25.43 15.00 15.05 2 18.67 0.23 4.88 22.67 1.48 0.457 9.83 3.36 42.21 3 5.96 0.07 2.76 11.64 1.34 0.554 5.05 1.32 39.56 4 21.02 0.26 5.17 20.47 1.26 0.631 8.17 3.62 50.04 5 5.71 0.07 2.70 12.26 1.45 0.477 5.55 1.56 -77.66 6 17.81 0.22 4.76 20.32 1.36 0.542 9.01 2.39 -5.39 7 6.34 0.08 2.84 11.39 1.28 0.613 5.07 1.29 -64.29 8 8.06 0.10 3.20 16.79 1.67 0.359 7.79 1.44 42.36 9 40.40 0.50 7.17 30.41 1.35 0.549 12.05 4.18 -24.04

Principles for calculation of parameters correct values of aggregate area and listed in table 1 and 2 are briefly described as circumference. followed.  Percentage of one aggregate area to the  Area and circumference overall aggregate area. With dimension of the original image This parameter is calculated by and resolution used for acquiring image, dividing number of grids in one aggregate pixel length and grid area can be calculated. by number of grids in all aggregates. Thus by counting the number of pixels in  Radius of equivalent circle circumference and number of grids in The equivalent circle is defined as the aggregate area, the program can calculate circle with the same area of that aggregate.

5 Hence, radius of the equivalent circle (req) the long axle and horizontal axis (as in can be calculated using figure 5). By systematically calculating distance between any two pixels on the area …………. req r=2 (5) circumference, the long axle, as well as the  length of it, can be defined. With  Long axle and inclination coordinates of the two pixels forming the The long axle is defined as the longest long axle, inclination of the long axle can be diagonal of the aggregate, whereas the calculated. inclination is defined as the angle between

long long axle short axle axle

90。 θ

Figure 5. Illustration of axles and inclination

 Short axle recorded fro that pixel. By iterating this Short axle is defined as the diagonal process through all pixels on circumference perpendicular to the long axle. Slope of the can then find out the position and length of short axle (S2) can be determined as the short axle. inversion of slope of the long axle. Then  Angularity for each pixel on the circumference, slope Angularity is defined as 2 times the of diagonals connecting it to all other pixels ratio between circumference of the on circumference can be calculated. The aggregate and its equivalent circle. diagonal with same value as S2 will be

 Shape factor and Consistency ratio # of all aggregates Shape factor is calculated according to equation (3). Meanwhile the consistency ratio is calculated according to equation (4) 3. Program verification  Flatness-elongation rate Selected images (as in figure 6) were used For every single aggregate, the length to verify precision of the program output. ratio of long and short axles (RLS) can be Since shapes and dimensions of these images calculated. By specifying a threshold value are known, parameters obtained from the Rth (say 3 or 5), the flatness-elongation rate program can be hand-calculated to check can then defined as the percentage of differences between these values. Data listed aggregates with RLS < Rth. That is, in table 3 ~ 4 are the results from program and hand calculations. Although the hand calculated values are generally smaller

F/E rate = # of aggregates with RLS < Rth (6) (implies that overestimation of the program), it can be seen that errors are limited below 8%.

6 It thus may be considered that this program is capable of performing acceptable analysis with great savings on both time and energy.

Table 3-1. program output for images shown in figure 6

ID RLS > 3 RLS > 3 Long axle > 4.75 Long axle < 4.75 Verify 1 2 3 0

Figure 6. Selected images Figure 7. output image

Table 3-2. Program output for images shown in figure 7 ID Area(mm Area % Radius of Circumferen Angular SF Long Short Inclination (o) 2) equiv. circle ce ity axle axle (mm) (mm) length length (mm) (mm) 1 67.70 0.88 9.28 45.94 1.58 0.40 19.71 4.48 -81.10 2 147.90 1.93 13.72 53.63 1.24 0.65 23.37 12.03 89.17 3 30.08 0.39 6.19 22.86 1.18 0.72 9.27 4.48 80.54  245.68 3.21 1.33 0.59

Table 4. Hand calculation results and comparison for images shown in figure7

7 ID Area(mm2) Area % Radius of Circumferen Angular SF Long axle equiv. circle ce ity length (mm) (mm) (mm) 1 70.00 70/7700*100 = 9.44 47.0 1.59 20 4.8 0.91 Error 3.29% 3.30% 1.69% 2.26% 0.6% 1.45 6.67% % 2 156.25 156.25/7700*100 14.11 56.0 1.26 25 12.5 =2.03 Error 5.34% 4.93% 2.76% 4.23% 1.59% 6.52 3.76% % 3 30.00 30/7700*100=0.3 6.18 23.0 1.19 10 4.8 9 Error 0.34% 0% 0.16% 0.61% 0.84% 7.3 6.67% %

4. Future of the study Although the developed program is able Reference to produce an acceptable 2-dimentional images and related parameters, the planar 1. Lee, Iee long. Discussion sand skeleton with image still can’t tell us the true aggregate Image analysis machine. Dissertation of composition inside asphalt concrete sample Department of Civil Engineering National Central since there are lots of aggregate overlaps. By University, 1995, pp.27-29. cutting the sample into several slices and 2. Shay Zane and. Image Process，1997, Chan acquiring images of each of these slices, we Wan press company. may be able to construct the 3-dimentional 3. Zung ,ChiuanChi，The study of skeleton of image of the sample. However, it is obviously Asphalt concrete with Image process, a very difficult task. Similar image processing Dissertation of Department of Civil Engineering techniques have been applied to medical National Central University, 1999,pp.21-244 studies to simulate human anatomy. Efforts in 4. Zhone win. Cloud identified form Satellite Image extending the current study to construct 3-D with Linear multiply Method ， Dissertation of images will certainly be enforced. Department of Civil Engineering National Central University ,1998。Pp25-26 5. Kuo ,Lion-Song，the skeleton of Sand Particle, Conclusion Dissertation of Department of Civil Engineering Result of the study has shown that National Central University ,1998,pp15-20. adoption of image processing technique can save 6. Wadell H., "Volume Shape and Roundness of greatly over these measures. In the meantime, Rock Particles," Journal of Geology, Vol.40, pp. asphalt content and aggregate composition of 443-688, (1932) core samples taken from field may also be 7. .Russel, R.D. and Taylor, R.E., "Roundness and examined using image processing technique, Shape of Mississippi River Sands," Journal of instead of using extraction test (which uses Geology , Vol.45, pp.225-267, (1937) poisonous chemicals as solvent). Quantitative 8. Pettijohn, F.J, "Sedimentary Rocks". Harpers and results obtained are another merit of image Brothers. New York, pp.55-59, (1957) processing technique. Consequently, applying 9. Powers, M.C., "A New Roundness Scale for image processing technique onto asphalt concrete Sedimentary Particles." Journal of Sedimentary seems to be a worthwhile research topic to Petrology. Vol.23. No.2. pp .117-119, (1953.) endeavour. 10. Krumbein, W. C., ”Measurement of Geological Significance of Shape and Roundness of

8 Sedimentary Particles, ”Journal of Sedimentary 13. Reiche , P., "An analysis of cross-lamination: The Petrology, Vol.11.pp.64-72. Coconino sandstone” Journal of Geology, .vol.46, 11. Yudhbir , and .Rahim, A., "Quantification of pp.905-932, (1938.) Particle Shape and Angularity Using the Image 14. Krumbein, W .C., "Preferred orientation of Analyzer," Geotechnical Testing Journal pebbles in sedimentary deposits" Journal of ,GTJODJ, Vol. 14. No.3, pp. 296- 308. (1991) Geology,. Vol.47 pp.673-706 (1939) 12. Huang, Wei Ching and Kao Ching Hang. The 15. Curray, J. R., ”Analysis of Two Dimensional Roundness Measurement of Sand with Digital Orientation Data,“ Journal of Geology, Vol. 64, Image.5th Geological Engineering Conference pp. 117-131, (1956) .Taiwan，pp.793-800(1993)

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