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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 生醫電子與資訊學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44004
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳志宏
dc.contributor.authorWei-Chun Lien
dc.contributor.author李韋均zh_TW
dc.date.accessioned2021-06-15T02:35:55Z-
dc.date.available2019-12-31
dc.date.copyright2009-08-18
dc.date.issued2009
dc.date.submitted2009-08-13
dc.identifier.citation[1] M. Brown, “Autostitch,” http://www.cs.ubc.ca/~mbrown/autostitch/autostitch.html, 2009.
[2] M. Brown and D. G. Lowe, “Recognising Panoramas,” Proceedings of International Conference on Computer Vision, Nice, France, Vol. 2, pp. 1218-1225, 2003.
[3] M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, Vol. 24, pp. 381–395, 1981.
[4] D. G. Lowe, “Object Recognition from Local Scale-Invariant Features,” Proceedings of the International Conference on Computer Vision, Kerkyra, Corfu, Greece, Vol. 2, pp.1150–1157,1999.
[5] Wikipedia, “Kd Tree,” http://en.wikipedia.org/wiki/Kd-tree, 2009.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44004-
dc.description.abstract全景合成的技術是為了讓人們在看圖片時可以有一個更大的視野,這樣的技巧可以被運用在相當多的領域中,而如何讓他更快,更簡單,夠精確,就變成了一個很重要的議題。
在這篇論文中,我們介紹了影像縫合的技術,又稱為全景合成。並且實作出來且將他們應用在各種不同類型的影像中,更進一步探討他們在生醫影像這個領域的應用,驗證我們的方法是有效且快速的。
zh_TW
dc.description.abstractThe technique of image stitching is made up for a better range of a picture’s view for humans. It can be applied to many specific fields. How to make it fast, simple and accurate thus becoming a very important issue.
In this thesis, we introduce the image stitching technique also called panoramas. Then implement it and apply it to different types of images. Especially on the field of biomedical images to verify that our method is effective and efficient.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T02:35:55Z (GMT). No. of bitstreams: 1
ntu-98-R96945008-1.pdf: 4839502 bytes, checksum: 5eef732bd9851d4df182c18ec141473a (MD5)
Previous issue date: 2009
en
dc.description.tableofcontentsChapter 1 Introduction 1
1.1 Motivation 1
1.2 History of panoramas 2
Chapter 2 Related Work 4
2.1 Pre-step 4
2.2 Warping: Cylindrical projection 8
Chapter 3 Feature Point-Based Method 11
3.1 SIFT: Scale Invariant Feature Transform 11
3.1.1 Features 11
3.1.2 Scale-Space Extrema Detection 12
3.1.3 Keypoint Localization 13
3.1.4 Interpolation of Nearby Data for Accurate Position 14
3.1.5 Discarding Low-Contrast Keypoints 15
3.1.6 Eliminating edge responses 15
3.1.7 Orientation assignment 15
3.2 Kd Tree 16
3.3 RANSAC: RANdom SAmple Consensus 18
Chapter 4 Our Proposed Method 21
4.1 RGB Color Channel Balance 21
4.2 Error Matrix 24
4.3 Stitch Line Finding 26
4.2 Seamless Blending 30
Chapter 5 Experiment Results 33
5.1 Experiment Environment 33
5.2 Results 34
5.2.1 Medical Images 34
5.2.2 Normal Daily Photos 54
5.3 Comparision 68
Chapter 6 Conclusion and Future Work 72
6.1 Conclusion 72
6.2 Future Work 72
References…………………………………… ……………………73
dc.language.isoen
dc.subject生醫影像zh_TW
dc.subject全景合成zh_TW
dc.subjectbiomedical imageen
dc.subjectpanoramaen
dc.title影像縫合與全景合成在生醫影像的應用zh_TW
dc.titleImage Stitching and Panoramas Applied to Biomedical Imagesen
dc.typeThesis
dc.date.schoolyear97-2
dc.description.degree碩士
dc.contributor.coadvisor傅楸善
dc.contributor.oralexamcommittee詹志康
dc.subject.keyword全景合成,生醫影像,zh_TW
dc.subject.keywordpanorama,biomedical image,en
dc.relation.page73
dc.rights.note有償授權
dc.date.accepted2009-08-13
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept生醫電子與資訊學研究所zh_TW
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