# Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
###### Abstract
This is essential because similarity tracking, segmenting, and stitching high spatial resolution images require efficient FDs to significantly reduce the training time. Figure 1: Comparison of edge detection algorithm using gradient and Laplacian filter: (**a**) gray-level and (**b**) binary images. From the above discussion, two issues need to be addressed: (1) improving the threshold separator for the image binarization process and (2) developing an integrated and flexible image-processing method for tracking the metal grain deformation. The development of a threshold adjustment tool set that allows observers to adapt threshold separators using the unique properties of each polycrystalline image is currently unavailable. Although it can be intuitively understood that setting the threshold using these unique properties would be more accurate than equalizing all the parameters, the key question is what properties represent the uniqueness of each polycrystalline image? In addition, which physical property would give not only a more robust model to track the deformation of metal grains but also a less computational load? Therefore, algorithms to solve these questions could provide an integrated image-processing approach to characterize the deformation trends and physical changes in metal grains with a high-fidelity representation of the direction of the metal grain deformation, thus making it possible to secure the safety and reliability of structures by tracking microstructural changes [9; 10; 11; 12; 13]. In this paper, three integrated image-processing algorithms, namely image stitching, grain tracking, and boundary extraction, are proposed. The idea is to integrate a sectional view of a specific grain image, which is used to extract the GBs, with an image of the entire material surface. This approach utilizes two different scope lenses to achieve an observation efficiency. The 50-scope lens exploited all the surfaces of the specimen, capturing the grain location coordinates. Section-view images were taken using a 200-scope lens to produce a clear grain image, thus increasing the accuracy and reliability of the GB extraction. The results showed that the capture time and volume of the data were reduced by up to 1/16 compared with the use of the 200-scope lens alone. Furthermore, image-stitching and grain-matching algorithms are proposed to track the grain location to complement the image features of the 50-scope and 200-scope datasets. The novelty of this study is the unique implementation of image binarization based on the histogram thresholding techniques that extract the grain properties based on the adaptive Gaussian distribution approach. This algorithm executes the binarization process by tracking the bimodal histogram distribution, which represents the uniqueness of each polycrystalline image. Interestingly, extraction flexibility can be achieved by arranging the standard deviation of the unique histogram properties of each polycrystalline image using a statistical level of confidence, which is very convenient to implement. In addition, a unique data acquisition process that combines two different scope lenses can increase the flexibility of GB deformation analysis by providing tools that can extract the grain at a specific location.