電腦視覺在甲骨文上的應用：甲骨文碎片分類

Computer Vision for Jiagu-Rubbings Application: Classification of Tortoise Jiagu-Rubbings Fragments

本論文提出一個基於圖像內容特徵之龜甲類甲骨拓片碎片形狀分類方法，透過甲骨學家定義好的各類骨板形狀規則，進行拓片碎片的分類。目的在幫助甲骨學家，提供快速分類的結果與予參考。本研究主要由三個流程組成：拓片碎片前處理、碎片特徵擷取、建立資料庫和分類模型。針對《甲骨文合集》書中，23片完整龜甲片，依據九種骨板的形狀，進行切割成碎片，並利用23片龜甲所得之各類別碎片建立資料庫，分析各類別的碎片特徵，並建立分類模型。實驗顯示，本研究提出的方法，針對九種骨板碎片，共1,712片進行分類，其分類準確度結果為94%，而平均時間約為383秒（總執行時間為所有輸入影像前處理、特徵擷取及碎片分類），有效地減少甲骨學家在碎片形狀分類上花費的時間。

This paper 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 paper 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 94% accuracy in classifying 1,712 rubbings of fragments, whether the shapes are similar to those of the nine bone plates or not. With a total execution time of 383 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.