A Content-Based Feature for Shape Classification of Tortoise Jiagu-Rubbings Fragments

• Main Authors: Yi-Shin Tang, 湯億鑫
• Other Authors: 丁肇隆
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/75638962173951266825

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 103 === Many oracle bones, which were buried underground for over 3,000 years, were broken into fragments by natural causes such as subsurface stress and water infiltration. The oracle bones were also sometimes broken into smaller fragments during excavation. To facilitate research into the Jiagu characters, archaeologists classify and rejoin the fragments as much as possible. However, classifying and then rejoining fragments are considerably time-consuming steps, slowing the progress of research. This thesis proposes a content-based feature for shape classification of tortoise Jiagu rubbings fragments to classify the rubbings fragments according to the shape rules of the bone plates defined by archaeologists. The main goal is to provide archaeologists with a mode of efficient classification. This thesis consists of three parts: the preprocessing of the rubbings fragments , feature extraction, the construction of a database, and the development of a mode of classification. The work The Great Collection of the Oracle Inscriptions offers 23 complete tortoise shells. According to the nine shapes of bone plates, these 23 complete tortoise shells can each be cut into 9 pieces. Then the 23 fragments in each of the nine classes can be used in the construction of a database, analysis of the features of each class, and the design of the mode of classification. Experimental results indicate that the proposed method has 95.6% accuracy in classifying 1,926 rubbings of fragments, whether the shapes are similar to those of the nine bone plates or not. With a total execution time of 561 seconds (including all input image preprocessing, feature extraction, and classification), this method can effectively reduce the time that is required for archaeologists to classify fragments by shape.