本文關鍵詞： 古陶瓷 科技鑒定 機器視覺 結構信息 釉色信息 紋飾特征　出處：《硅酸鹽學報》2017年12期 　論文類型：期刊論文

【摘要】：為客觀、有效地對古陶瓷進行無損分類,提出了一種基于機器視覺古陶瓷無損分類識別方法。通過遍歷古陶瓷器型邊緣輪廓,獲取古陶瓷器型結構細節(jié)特征,并在HSI空間下提取古陶瓷釉色多通道顏色直方圖特征。同時,提取反映古陶瓷紋理多樣性的LBP紋飾特征�；谏鲜鎏卣�,采用機器學習方法實現(xiàn)古陶瓷器型結構、釉色及其紋飾圖案的無損分類識別。結果表明:通過機器視覺可以有效地對古陶瓷進行分類識別;在以16為曲率步長、9為LBP算子分塊數(shù)時,分別提取古陶瓷結構,紋飾特征有較好的識別精度,其中,基于結構與釉色融合特征相比單一特征具有更好的識別效果;當古陶瓷發(fā)生結構或紋飾上的小部分缺損時,該方法可以保持一定的魯棒性,當信息丟失或缺損為5%時,平均識別率依舊可達85%以上,可期望實現(xiàn)古陶瓷科技鑒定中的良好應用。
[Abstract]:In order to classify ancient ceramics objectively and effectively, a new method based on machine vision was proposed. By traversing the edge contours of ancient ceramics, the structural details of ancient ceramics were obtained. The multi-channel color histogram features of ancient ceramic glaze were extracted in HSI space. At the same time, LBP decorative features reflecting the texture diversity of ancient ceramics were extracted, based on the above features. The machine learning method is used to realize the nondestructive classification and recognition of the structure, glaze and patterns of ancient ceramics. The results show that the classification and recognition of ancient ceramics can be effectively carried out by machine vision. With 16 as the curvature step size and 9 as the LBP operator block number, the ancient ceramic structures are extracted, and the decorative features have good recognition accuracy. Compared with single feature, the recognition effect based on structure and glaze fusion is better than that of single feature. The method can maintain a certain robustness when a small part of the defect on the structure or decoration occurs. When the information is lost or the defect is 5, the average recognition rate can still reach more than 85%. It can be expected to realize the good application in the scientific and technological appraisal of ancient ceramics.
【作者單位】： 上海大學通信與信息工程學院;新型顯示技術及應用集成教育部重點實驗室;上海大學材料科學與工程學院;
【基金】：國家自然科學基金(11176016;60872117) 高等學校博士學科點專項科研基金(20123108110014)資助
【分類號】：TP391.41;TQ174.66
【正文快照】： 2.新型顯示技術及應用集成教育部重點實驗室,上海200072;3.上海大學材料科學與工程學院,上海200444)WENG Zhengkui1,GUAN Yepeng1,2,LUO Hongjie3(1.School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China;2.Key Laboratory of Adv

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