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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁肇隆(Chao-Lung Ting) | |
| dc.contributor.author | Han-Jen Ku | en |
| dc.contributor.author | 古涵仁 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:48:20Z | - |
| dc.date.available | 2018-08-16 | |
| dc.date.copyright | 2013-08-16 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-12 | |
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Keller, 'FFT based image registration,' in Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, 2002, pp. IV-3608-IV-3611. [13] G. Xu and Y. Xian, 'An Approach for Polygon Fragment Reassembly Based on Multiple Features,' in Intelligent Computation Technology and Automation, 2009. ICICTA'09. Second International Conference on, 2009, pp. 485-488. [14] E. Tsamoura and I. Pitas, 'Automatic Color Based Reassembly of Fragmented Images and Paintings,' Image Processing, IEEE Transactions on, vol. 19, pp. 680-690, 2010. [15] J. MacQueen, 'Some methods for classification and analysis of multivariate observations,' in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, p. 14. [16] H. J. Wolfson, 'On curve matching,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 12, pp. 483-489, 1990. [17] J. T. Schwartz and M. Sharir, 'Identification of partially obscured objects in two and three dimensions by matching noisy characteristic curves,' The International Journal of Robotics Research, vol. 6, pp. 29-44, 1987. [18] R. A. Hummel and S. W. Zucker, 'On the foundations of relaxation labeling processes,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 267-287, 1983. [19] P. Faber, 'A General Framework for Relaxation Processes,' Technical Report EDI-INF-RR-0057, Univ. of Edinburgh2001. [20] J. Kennedy and R. Eberhart, 'Particle swarm optimization,' in Neural Networks, 1995. Proceedings., IEEE International Conference on, 1995, pp. 1942-1948. [21] J. Kennedy and R. C. Eberhart, 'A discrete binary version of the particle swarm algorithm,' in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, 1997, pp. 4104-4108. [22] H. NEZAMABADIPOUR, S. B. M. ROSTAMI, and F. M. MAGHFOURI, 'BINARY PARTICLE SWARM OPTIMIZATION: CHALLENGES AND NEW SOLUTIONS,' THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, 2008. [23] M. A. Khanesar, M. Teshnehlab, and M. A. Shoorehdeli, 'A novel binary particle swarm optimization,' in Control & Automation, 2007. MED'07. Mediterranean Conference on, 2007, pp. 1-6. [24] J. Ciger, M. Gutierrez, F. Vexo, and D. Thalmann, 'The magic wand,' in Proceedings of the 19th spring conference on Computer graphics, 2003, pp. 119-124. [25] N. Otsu, 'A threshold selection method from gray-level histograms,' Automatica, vol. 11, pp. 23-27, 1975. [26] E. M. Arkin, L. P. Chew, D. P. Huttenlocher, K. Kedem, and J. S. Mitchell, 'An efficiently computable metric for comparing polygonal shapes,' in Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms, 1990, pp. 129-137. [27] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, 'An efficient k-means clustering algorithm: analysis and implementation,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, pp. 881-892, 2002. [28] C.-S. Yang, L.-Y. Chuang, J.-C. Li, and C.-H. Yang, 'A novel BPSO approach for gene selection and classification of microarray data,' in Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, 2008, pp. 2147-2152. [29] P. De Smet and E. Corluy, 'High-precision recomposition of fragmented 2-d objects,' in 14th ProRISC workshop on Circuits, Systems and Signal Processing, 2003. [30] A. Criminisi, P. Perez, and K. Toyama, 'Region filling and object removal by exemplar-based image inpainting,' Image Processing, IEEE Transactions on, vol. 13, pp. 1200-1212, 2004. [31] 太平洋史前Lapita陶器線上數位資料庫, http://lapita.rchss.sinica.edu.tw/web | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61131 | - |
| dc.description.abstract | 歷史陶瓷文物經過日積月累的自然變動，在考古挖掘時，通常會有大量的碎片散落在不同處，考古學家在找尋碎片相關特性或藉由經驗拼接的過程，經常會消耗數十年的時間。因此本論文提出一個基於圖像內容特徵之陶瓷碎片重建系統，自動拼接碎片，最終產出拼接建議結果，給予使用者參考，使相關工作者能利用此系統找出匹配的陶瓷碎片標本。本系統主要由三個模組所構成：拼接碎片前處理、產生候選匹配組合、挑選最佳候選匹配組合。經由萃取碎片上的圖像內容特徵，以曲線匹配找出候選匹配組合後，使用二進位粒子群最佳化演算法挑選最佳候選匹配組合。實驗顯示，本研究提出之方法相較於過去相關研究，不僅提供更高的準確度，同時具備更低的最佳化時間耗損，並能適用於多種資料碎片，解決單一特徵不能解決之問題。 | zh_TW |
| dc.description.abstract | Archaeological ceramic relics are changed by times, huge amount of fragments will be found at different places during archaeological excavation. Archaeological fragments relation founding and reconstruction requires huge time costing and expert knowledge. Our research proposes a content based feature matching for ceramic fragment reconstruction system (CFRS) which automatically reconstruct fragments and generate a suggestion for user. Related workers can find out matching ceramic fragments through this system. CFRS includes three main module: fragments preprocessing, produces matching candidates and select matching candidates. The features will be first extracted from the images and matching candidates will be picked up through curve matching. After that, Binary Particle Swarm Optimization (BPSO) is applied to select optimized matching candidates. Experiment results indicates the proposed method not only attends higher precision but also reduce the optimization time in our knowledge. Support different data types and solve problems that single feature cannot solve. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:48:20Z (GMT). No. of bitstreams: 1 ntu-102-R00525043-1.pdf: 3386016 bytes, checksum: 929380e6e701a926401beb60485f91f7 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
致謝 ii 摘要 iii ABSTRACT iv 論文目錄 v 圖目錄 vii 表目錄 ix 第一章、緒論 1 1.1 研究動機與目的 1 1.2 相關研究 2 1.2.1 特徵萃取與比對方法 3 1.2.2 點集合於二維平面上轉換之方法 6 1.2.3 最佳化演算法 7 1.3 論文架構 10 第二章、拼接碎片前處理 12 2.1 圖像內容特徵字串萃取 12 2.2 碎片輪廓等距取樣 14 第三章、產生候選匹配組合 16 3.1 圖像內容特徵字串萃取 17 3.1.1 轉向函數特徵字串萃取 17 3.1.2 顏色特徵字串萃取 19 3.2 曲線匹配 21 3.2.1 位移特徵字串 21 3.2.2 找尋候選匹配組合 24 3.3 圖像內容相似度計算 25 3.3.1 曲線相似度 26 3.3.2 顏色相似度 26 3.4 候選特徵資訊過濾 27 第四章、挑選最佳候選匹配組合 33 4.1 候選匹配組合相容度計算 33 4.2 全域一致性定義 36 4.3 鬆弛法 37 4.4 二進位粒子群最佳化演算法 38 第五章、實驗結果與討論 41 5.1 二進位粒子群最佳化演算法測試 41 5.1 多種類型碎片資料測試 46 5.2 參數調整與建議 53 第六章、論文與未來展望 57 參考文獻 59 附錄一 63 附錄二 64 | |
| dc.language.iso | zh-TW | |
| dc.subject | 特徵萃取 | zh_TW |
| dc.subject | 碎片重建 | zh_TW |
| dc.subject | 曲線匹配 | zh_TW |
| dc.subject | 二進位粒子群最佳化演算法 | zh_TW |
| dc.subject | Feature extraction | en |
| dc.subject | Curve matching | en |
| dc.subject | Binary Particle Swarm Optimization (BPSO) | en |
| dc.subject | Fragment reconstruction | en |
| dc.title | 基於圖像內容特徵匹配之陶瓷碎片重建研究 | zh_TW |
| dc.title | Content based Feature Matching for Ceramic Fragment Reconstruction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.oralexamcommittee | 林正偉,王家輝,郭真祥 | |
| dc.subject.keyword | 碎片重建,特徵萃取,曲線匹配,二進位粒子群最佳化演算法, | zh_TW |
| dc.subject.keyword | Fragment reconstruction,Feature extraction,Curve matching,Binary Particle Swarm Optimization (BPSO), | en |
| dc.relation.page | 66 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-12 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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| ntu-102-1.pdf 未授權公開取用 | 3.31 MB | Adobe PDF |
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