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From page 30... ... C-2 IMAGE ANALYSIS METHODS FOR CHARACTERIZING AGGREGATE SHAPE PROPERTIES Several investigations have been conducted on the use of imaging technology to quantify aggregate shape properties and relate them to the performance of pavement layers. Some of these studies focused on developing procedures to describe shape (1, 2, 3, 4, 5, 6, 7, 8) Read the entire page →
From page 31... ... C-3 Form Factor Form factor is widely used measure of shape in two-dimensions (2-D) and is expressed by the following equation: Form Factor = 2 4 P Aπ (C-3) Read the entire page →
From page 32... ... C-4 zero. Although the form index is based on 2-D measurements, it can easily be extended to analyze the 3-D images of aggregates. Read the entire page →
From page 34... ... C-6 supposed to have the same area as the particle image and first and second degree moment. Aspect ratio is always equal to or greater than 1.0, since it is defined as (major axis/minor axis) Read the entire page →
From page 35... ... C-7 angularity analysis can easily be achieved using automated systems for capturing images. Masad et al., (24) Read the entire page →
From page 36... ... C-8 Dilation is the opposite of the erosion. A layer of pixels is added around the periphery of the eroded image to form a simplified version of the original object. Read the entire page →
From page 37... ... C-9 and, consequently, angularity along the object boundary. The basic idea for measuring a fractal dimension by image analysis came from the Minkowski definition of a fractal boundary dimension (32) Read the entire page →
From page 38... ... C-10 Erosion Dilation Area = A1 Area = A2 Ex-Or Effective width Erosion - Dilation Technique Fractal Behavior Technique Dilation (d) Read the entire page →
From page 39... ... C-11 Hough Transform Hough Transform is another technique used to recognize co-linearity in pixels that form the particle outline (33) Read the entire page →
From page 40... ... C-12 detection step. Next, the gradient vectors at each surface point are calculated, using a Sobel mask that operates at each point on the surface and its eight nearest neighbors (21) Read the entire page →
From page 41... ... C-13 Figure C-2. Illustration of the Difference in Gradient between Particles (after (21) Read the entire page →
From page 42... ... C-14 Direct Measurements of Particle Dimensions Kuo and Freeman (13) Read the entire page →
From page 43... ... C-15 outline obtained from each of the top, side, and front images. Accordingly, the AIUI procedure first determines an angularity index value for each 2-D image. Read the entire page →
From page 44... ... C-16 n = 1 2 3 n=24 4 α1 α2 α3 αn n-1 Figure C-3. Illustration of an n-Sided Polygon Approximating the Outline of a Particle (after (15) Read the entire page →
From page 45... ... C-17 Convexity Convexity is another parameter that can used to describe angularity of aggregate particles. Convexity can be calculated using the following formula: Convexity = Convex Particle A A Conv = (C-24) Read the entire page →
From page 46... ... C-18 Figure C-4. Average Minimum Curve Radius Calculations. Read the entire page →
From page 47... ... C-19 Typical Analysis of Texture The analysis of texture has been performed using both black and white images and gray images. The main disadvantage of using black and white images is the high resolution required for capturing images, which makes it difficult to use automated systems. Read the entire page →
From page 48... ... C-20 where a0, an, and bn are found using Equations C-9 to C-11. Texture is captured using harmonics with frequencies that are higher than angularity and shape. Read the entire page →
From page 49... ... C-21 (a) Smooth texture (b) Read the entire page →
From page 50... ... C-22 Wavelet Analysis Texture in an image is represented by the local variation in the pixel gray intensity values. Although there is no single scale that represents texture, the histogram and FFT analyses of texture capture only a single scale. Read the entire page →
From page 53... ... C-25 REFERENCES 1. Barksdale, R Read the entire page →
From page 54... ... C-26 6. Weingart, R Read the entire page →
From page 55... ... C-27 13. Kuo, C., and Freeman, R Read the entire page →
From page 56... ... C-28 19. Fletcher, T., Chandan, C., Masad, E., and Sivakumar, K Read the entire page →
From page 58... ... C-30 34. Wilson, J Read the entire page →

From page 30...

... C-2 IMAGE ANALYSIS METHODS FOR CHARACTERIZING AGGREGATE SHAPE PROPERTIES Several investigations have been conducted on the use of imaging technology to quantify aggregate shape properties and relate them to the performance of pavement layers. Some of these studies focused on developing procedures to describe shape (1, 2, 3, 4, 5, 6, 7, 8)