## 3 Image Processing Technique Used
### Threshold with the Edge Detection  
Many segmentation methods for image analysis often use brightness intensity thresholding techniques. The simplest implementation of thresholding is the selection of pixels between two user-defined values. However, without further user interaction, this technique is limited in its application, because a region of interest is often connected to other objects via the linking bridges of pixels. Thus, the authors applied an edge detector to find edges in the image. The Sobel method finds edges using the Sobel approximation to the derivative. It returns edges at those points where the gradient of the image is the maximum. In contrast to the intensity-oriented conventional object detection methods, the proposed method in this study refers to the local edge properties, and integrally evaluates them by a fuzzy function to achieve better detection quality. The result of an evaluation applied to an asphalt mixture image showed very a high detection rate in detecting the aggregate regions without any difficulty. After the edge detection by the Sobel filter, the authors reconstructed the edge to connect it to the edge boundary. Figure 6a is an edge-detection image using the Sobel edge detector. The use of the edge detector enhances the program's ability to process the threshold. There are many threshold approaches for image segmentation [47; 48]. A combination of the global threshold method and the boundary-based method was proposed in the image processing development. In addition, the complement of the boundary-based method (limited region-based method) was introduced with the regions by comparing one pixel to its neighbors. If a criterion of homogeneity is satisfied, the pixel belongs to the same class as one or more of its neighbors. In the coding, the first step of the segmentation process involves the boundary-based method applying the Sobel filter and the partial region-based methods. Then, the threshold is applied for further segmentation because of its availability in the MATLAB software (MATLAB Student, Seoul, Korea) with the 'gravthresh' function and its wide usage in many applications. The graythresh function uses [PERSON]'s method, which chooses the threshold to minimize the variance of the black and white pixels. The threshold is a very time-consuming process of trial and error. Before automatically defining the threshold values, it changed the threshold value until the results were satisfactory. Instead, a multi-level threshold approach was introduced in the program. If the entire image has relatively consistent characteristics, a global threshold gives satisfying results. If there are unevenly illuminated regions in the image, the local threshold might perform better than the global threshold. One of the limitations of the local threshold method is that it only determines the localized threshold for current regions, which is only a small portion of the entire image. However, the best fit value at once is not sufficient at all when the image has more complicated components. Thus, for images in which the brightness ranges of the foreground objects and background entangle, different thresholds must be coded for different regions. In such a scenario, the domain-dependent information was also coded in the algorithm to get the work done. Figure 6 includes the images of the threshold process in the program. In Figure 6b, the white region is initially thresholded by the graythresh function in the MATLAB function [49]. However, the global threshold value cannot fully detect all of the aggregate regions. Thus, the authors applied the second and the third threshold values until those thresholded regions, which are presented with gray in Figure 6b, reached the edges of the aggregate. After the  
Figure 5: A series of image enhancements: (**a**) an asphalt mixture image; (**b**) gray-scale image of rectangular region in the asphalt mixture image; (**c**) equalizing image; (**d**) low pass filtering image; (**e**) median filtering image.