A Study of Recognizing Flake Surfaces of Stone Tool Based on Feature Lines Extracted by Mahalanobis Distance Metric

One of the areas that benefit from point clouds is cultural heritage research. The study of point clouds is contributing to cultural heritage saving for the next generation. One example is the study of stone tool. The study of stone tools plays an increasingly important role in the behavior of our ancestors. The surface collection obtained from excavated stone tools required to be open for archaeological research. In this purpose, the publication of the stone tool study needs to be presented. Archaeological research is commonly used stone tool illustration which is called "Scale drawing". Drawing a scale drawing is too time-consuming work. Reassembling Fractured Objects is the impor­ tant concept of technological analysis of stone tool artifacts. In general. the problem of matching real-world archaeological fragments is a complex and time-consuming task. Because the evaluation of similarity between artifacts and their parts is difficult to evaluate. To facilitate the time-consuming work of scale drawing and matching of fragmented objects, this thesis studied com­ puter graphics techniques to assist the study of stone tools. The thesis mostly considers the two aspects about extracting feature line and recognizing flake surfaces. In archaeological research, the scale drawing, which is hand-drawn from measured stone tools, is traditionally used. In the scale drawing creation, a base drawing which consists outline and ridge lines is initially drawn from ge­ ometric features of shape. After that other lines are extracted from knowledge of making stone tools and are added to the base drawing. It requires special knowledge to extract feature lines from stone tools so that scale drawing is time-consuming. Therefore, if the base drawing is automatically extracted, the working hours are reduced. To overcome this issue, this paper proposes a feature line extraction method using the Mahalanobis distance metric. First, the points on the outline are extracted from a point cloud. Then, the sur­ face variation is calculated with a various number of neighbors and thus the potential feature points are detected by the analysis of its surface variation. After that, the potential feature points are thinned towards the highest vari­ ation points by using Laplacian smoothing. Then, the thinned feature points are shrunk to the potential feature points. F inally, a feature line is extracted by connecting the nearest thinned feature points locating in the Mahalanobis distance field. To verify our method, the extracted outline and ridge lines are compared to scale drawing of the stone tool drawn by archaeological illustra­ tors. Our method is applied to stone tools, and we confirm the effectiveness of our method. Studying stone tools provides us with information about the human history dating as far back as 2.5 million years ago. Nowadays, the important historic artifacts of stone tools are studied by functionality and technological analysis of lit.hie analysis. The reassembly process of stone tools is commonly done in lit.hie materials. The reassembly process can be easily done by the refitted flake matching process. The refitted flake matching process is required for highly accurate segmented flakes. The thesis presents a.n algorithm for automatic recognition of the flake surface based on feature lines of stone tools from point clouds. The feature-line-based recognition method for the flake surface is very effective for stone tools because it is based on the geometric characteristic of stone tools. The proposed segmentation method is based on the seeded-region method. Before the segmentation, surface normal at a point is estimated from neighbor points and the nearest distance from a point to feature line segment is calculated. Initially, the furthest point from the feature line is selected for a seed point. Then, the seed point clusters its neighbor points by the following two criteria. First, neighbor points of seed point are calculated. To save the program processing speed, a distance threshold is used. If the distance of a selected neighbor point is greater than a given threshold value, the neighbor point is clustered to the seed point region. Second: if the neighbor point is located near the feature line, the local coordinate system is constructed along the normal vector of the seed point. Then, a line is constructed by seed point and its neighbor point. If the line does not intersect with the nearest feature line segment on the local coordinate system, the neighbor point is segmented into the seed point region. If liue segments intersect with each other on the local coordinate system, the neighbor point is selected to a new seed point