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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66056
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dc.contributor.advisor鄭士康(Shyh-Kang Jeng)
dc.contributor.authorChang-Feng Tsaien
dc.contributor.author蔡長峰zh_TW
dc.date.accessioned2021-06-17T00:20:21Z-
dc.date.available2017-06-29
dc.date.copyright2012-06-29
dc.date.issued2012
dc.date.submitted2012-06-22
dc.identifier.citation[1] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester. “Image inpainting,” The Proceedings of the 27th annual conference on Computer graphics and interactive techniques, 2000, pp. 417-424.
[2] A. A. Efros and T. K. Leung. “Texture synthesis by non-parametric sampling,” The Proceedings of the 7th IEEE International Conference on Computer Vision, 1999, vol.2, no., pp. 1033-1038.
[3] M. Bertalmio, L. Vese, G. Sapiro, and S. Osher. “Simultaneous structure and texture image inpainting,” IEEE Transactions on Image Processing, Aug. 2003, vol.12, no.8, pp. 882- 889.
[4] A. Criminisi, P. Perez, K. Toyama. “Object removal by exemplar-based inpainting,” The Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol.2, no., II-721 - II-728.
[5] J. Sun, L. Yuan, J. Jia, and H.-Y. Shum. “Image completion with structure propagation,” SIGGRAPH, 2005, pp. 861-868.
[6] J. Hays and A. A. Efros. “Scene completion using millions of photographs,” SIGGRAPH, 2007, Article No. 4.
[7] P. Pe ́rez, M. Gangnet, and A. Blake. “Poisson image editing,” SIGGRAPH, 2003, pp. 313-318.
[8] Z. Farbman, G. Hoffer, Y. Lipman, D. Cohen-Or, and D. Lischinski. “Coordinates for instant image cloning,” SIGGRAPH, 2009, Article No. 67.
[9] E. Rosten and T. Drummond. “Fusing Points and Lines for High Performance Tracking,” In Proceedings of the 10th IEEE International Conference on Computer Vision, 2005, vol.2, no., pp. 1508-1515.
[10] E. Rosten and T. Drummond. “Machine learning for high-speed corner detection,” In Proceedings of the 9th European conference on Computer Vision, 2006, vol.1, no., pp. 430-443.
[11] M. Calonder, V. Lepetit, C. Strecha and P. Fua. “BRIEF: Binary Robust Independent Elementary Features,” In Proceedings of the Eleventh European conference on Computer Vision, 2010, vol.6314, no., pp. 778-792.
[12] T. Tuytelaars and K. Mikolajczyk. “Local Invariant Feature Detectors: A Survey,” Foundations and Trends in Computer Graphics and Vision, 2007, vol.3, no. 3, pp. 177-280.
[13] M. A. Fischler and R. C. Bolles. “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications. ACM, 1981, vol. 24, issue 6, pp. 381-395.
[14] CGAL, Computational Geometry Algorithms Library. http://www.cgal.org. 2007.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66056-
dc.description.abstract攝影時,常常會遇到物體闖入照片的情形,這導致攝影者原先的構圖被破壞,且照片的美感降低。然而,因某些特定因素,攝影者依然希望保留此張被破壞構圖的照片,且希望將闖入的物體去除。因此,我們提出了一個方法,以解決此種問題。為了去除使用者所選定的不要物體,前人之方法藉由抽取被破壞照片本身的資訊或搜索網路上相似之照片,以填補不要的物體。反之,我們專注在使用智慧型手機之使用情境上且對兩張由相同場景收集來的照片做處理。我們提出的方法可分為兩個步驟:影像對齊與影像融合。藉由估計兩張照片之間的座標轉換,影像對齊的技術可校正兩張照片中不同的視野,以便在來源照片中取得對應的區域,用以填補欲移除之區域。得到對應區域的影像後,影像融合技術消弭兩張照片因不同光照環境影響之色差。經由此兩步驟的處理,我們所提出的方法,可以消除照片中意外闖入的物體,並填入適當的影像。zh_TW
dc.description.abstractOften when photographers take pictures, some undesired objects rush into pictures. It will break the framing of these pictures. However, photographers still want to keep these pictures for some reasons. Hence, we propose a method to remove the undesired object in pictures. To remove the undesired object specified by users, some previous works draw the information from the image itself and other previous works search the similar scene from internet to complete the undesired object. Instead, we focus on the scenario of using smartphone and process with two pictures collected from the same scene. Our method consists of two stages: image alignment and image blending. By estimating the homography between images, image alignment calibrates the difference of geometry between images. Therefore, we can locate the source patch, used to complete the undesired region. After image alignment, image blending smoothly spread the discrepancies of photometry between images under different lighting conditions. After these two steps, our work removes the rush-in objects and fills in the appropriate images.en
dc.description.provenanceMade available in DSpace on 2021-06-17T00:20:21Z (GMT). No. of bitstreams: 1
ntu-101-R99942119-1.pdf: 11322777 bytes, checksum: cff117e7aeaaca060df6a8a2d7ddae49 (MD5)
Previous issue date: 2012
en
dc.description.tableofcontents口試委員會審定書 #
中文摘要 iii
ABSTRACT v
CONTENTS vii
LIST OF FIGURES ix
LIST OF TABLES xi
Chapter 1 Introduction 1
1.1 Motivation and Objectives 1
1.2 Literature Survey 1
1.3 Key Contributions 3
1.4 Overview and Thesis Organization 4
Chapter 2 Image Alignment 7
2.1 Background Knowledge of Image Alignment 7
2.1.1 Homography 7
2.1.2 Fast feature detection and Brief feature description 10
2.1.3 Feature Matching 10
2.1.4 RANSAC 12
2.2 General Image Alignment 14
2.3 Refined image alignment 20
Chapter 3 Image Blending 25
3.1 Image Blending 25
3.1.1 Problem Formulation: 26
3.2 Mean Value Coordinates 27
3.3 Acceleration 28
3.4 The Result of Image Blending 33
Chapter 4 Improvement Evaluation by the Refined Image Alignment 35
4.1 Data Collection 35
4.2 Evaluation Results 36
Chapter 5 Results and Discussions 41
5.1 Results of Object Removal 41
5.2 Discussions on Execution Time 45
5.3 Limitations 46
Chapter 6 Conclusions 51
Bibliography 53
dc.language.isoen
dc.title利用兩張照片影像對齊與融合之物體移除技術zh_TW
dc.titleObject Removal Using Image Alignment and Blending of Two Pictuesen
dc.typeThesis
dc.date.schoolyear100-2
dc.description.degree碩士
dc.contributor.oralexamcommittee傅楸善(Chiou-Shann Fuh),丁建均(Jian-Jiun Ding)
dc.subject.keyword物體移除,影像修復,zh_TW
dc.subject.keywordobject removal,image inpainting,en
dc.relation.page54
dc.rights.note有償授權
dc.date.accepted2012-06-25
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電信工程學研究所zh_TW
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