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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42889完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳家麟 | |
| dc.contributor.author | Hong-Ming Chen | en |
| dc.contributor.author | 陳鴻銘 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:27:41Z | - |
| dc.date.available | 2019-12-31 | |
| dc.date.copyright | 2009-08-04 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-22 | |
| dc.identifier.citation | 1. H.G. Barrow, J.M.T., Recovering intrinsic scene characteristics from images Computer Vision System, 1978.
2. Weiss, Y., Deriving images from image sequences, in ICCV. 2001. 3. Marshall F. Tappen, W.T.F., and Edward H. Adelson, Recovering Intrinsic Images from a Single Image. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2005. 27(9). 4. Graham D. Finlayson, S.D.H., Color Constancy at a pixel. J. Opt. Soc. Am. A, 2001. 18(2). 5. Mark S. Drew, G.D.F., Steven D. Hordley, Recovery of Chromaticity Image Free from Shadows via Illumination Invariance ICCV workshop 2003 2003. 6. Graham D. Finlayson, M.S.D., Cheng Lu, Intrinsic Images by Entropy Minimization. ECCV, 2004. 7. Graham D. Finlayson, S.D.H., and Mark S. Drew, Removing Shadows from Images. ECCV, 2002. 8. G. D. Finlayson, S.D.H., C. Lu, M. S. Drew, On the Removal of Shadows From Images. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006. 28(1). 9. Cl′ement Fredembach, G.D.F., Hamiltionian path based shadow removal. British Machine Vision Conference, 2005. 10. Lee, H.-C., Introduction to Color Imaging Science 2005. 11. F.E. Nicodemus, J.C.R., J.J. Hsia, I.W. Ginsberg, T. Limperis Geometrical Considerations and Nomenclature for Reflectance. Gairthersburg, MD: National Bureau of Standards (US), 1977. 12. Wyszecki, G.S., W. S., Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition. 2000. 13. P.B. Delahunt, D.H.B., Does human color constancy incorporate the statistical regularity of natural datlight? Journal of Vision, 2004: p. 57-81. 14. Buchsbaum, G., A spatial processor model for object colour perception Journal of the Franklin Institute, 1980. 15. H.-C Lee, R.M.G., Color as seen by humans and machines. Final program and advance printing papers of the IS&T's 47th annual conference 1994: p. 401-405. 16. Alkofer, J.S., Tone value sample selection in digital image processing mothod employing histogram normalization US Patent 4,654,722 1987. 17. D.B. Judd, D.L.M., G.W. Wyszecki, Spectral distribution of typical daylight as a function of correlated color temperature. Journal of the Optical Society of America, 1964. 18. Cohen, J., Dependency of the spectral reflectance curves of the Munsell color chips. Phychonomic Science 1964: p. 369-370. 19. Maloney, L.T., Evaluation of linear models of surface spectral reflectance with small number of parameters. Journal of the Optical Society of America, 1986. 20. Sapiro, G., color and illuminant voting IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1999. 21. B.Tao, I.T., N. Katoh, Illumination detection in linear space Proceedings of the IS&T/SID Eighth color imaging conference, 2000. 22. Pietro Perona, J.M., Scale-Space and Edge Detection Using Anisotropic Diffusion IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1990. 12(7). 23. Michael J. Black, G.S., David H. Marimont, David Heeger Robust Anisotropic Diffusion IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998. 7(3). 24. Michael J. Black, A.R., On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. International Journal of Computer Vision, 1996. 19(1). 25. Volker Aurich, J.W., Non-Linear Gaussian Filters Performing Edge Preserving Diffusion. Mustererkennung 1995, 17. DAGM-Symposium, 1995. 26. Tomasi, C., Manduchi, R. , Bilateral filtering for gray and color images. Computer Vision, 1998. Sixth International Conference on, 1998. 27. F. R. Hampel, E.M.R., P. J. Rousseeuw, and W. A. Stahel, Robust Statistics: The Approach Based on Influence Functions. New York: Wiley, 1986. 28. Georg Petschnigg, R.S., Maneesh Agrawala, Michael Cohen, Hugues Hoppe, Kentaro Toyama Digital Photography with Flash and No-Flash Image Pairs. ACM SIGGRAPH, 2004: p. 664 - 672. 29. Elmar Eisemann, F.D., Flash Photography Enhancement via Intrinsic Relighting. ACM Transactions on Graphics (TOG), 2004. 23(3). 30. Sylvain Paris, P.K., Jack Tumblin, and Frédo Durand A Gentle Introduction to Bilateral Filtering and its Applications. A course at ACM SIGGRAPH 2007, 2007. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42889 | - |
| dc.description.abstract | 此論文探討有關本質影像恢復之問題。在物理學為基礎的假設下，我們提出了一個由單一影像來得到本質影像的方法。我們也提出了一個新的最小平方法: 聯合強健統計法(the Joint Robust Statistics)。我們以此方法來解決上述問題。它可將特定形式之非線性最小平方問題轉換成線性最小平方問題。在此論文中，我們也將分析此方法與其他相關方法之關連性，例如非等項擴散、強健統計法、雙向濾波器等方法。此外，以此方法所產生之本質影像在影像編輯處理上具有很大便利性。最後我們將以自然影像來呈現此方法之效果。 | zh_TW |
| dc.description.abstract | This paper concerns about the intrinsic image recovery problem. Based on physics assumptions, we present a method to discover intrinsic images from a single image. We also propose a new least-square minimization approach: the Joint Robust Statistics, to solve the prescribed problem. It can transfer a specific kind of nonlinear least-square minimization problem to a linear least-square one. The analysis of the relationships between the proposed minimization approach and the other popular methods, such as anisotropic diffusion, robust statistics, and bilateral filter, is also provided in this paper. Moreover, the resultant intrinsic images are especially friendly for image editing. Natural images are used to demonstrate the effectiveness of our work. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:27:41Z (GMT). No. of bitstreams: 1 ntu-98-R96944004-1.pdf: 4332858 bytes, checksum: a651066c549caeeadd7fdc6c45adac2c (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | CHAPTER 1 INTRODUCTION 1
CHAPTER 2 RELATED WORKS 5 CHAPTER 3 BACKGROUND AND THE FORMULATION OF THE PROBLEM 7 3.1 THE NATURE OF COLOR: CHARACTERISTICS OF LIGHT SOURCE, ILLUMINANTS, AND REFLECTANCE 7 3.1.1 The formation of a natural scene 7 3.1.2 Characteristics of light source and illuminants 8 3.1.3 Reflectance 10 3.1.4 Eyes and camera sensors 11 3.2 COLOR SPECIFICATION SYSTEM: CHROMATICITY COORDINATE 12 3.3 PROBLEM FORMULATION 15 3.4 3.1.1 THE FORMULATIONS OF IMAGING EQUATION 15 3.4.2 Simplification of the formulations 16 CHAPTER 4 RECOVERY OF INTRINSIC IMAGES: THE PROPOSED FRAMEWORK 19 4.1 LEAST-SQUARE FORM 19 4.2 TO DISCOVER COLOR CUES 21 4.3 NONLINEAR LEAST-SQUARE SOLUTION: THE APPROACH BASED ON ROBUST STATISTICS 24 4.3.1 The original linear least-square solution 24 4.3.2 Perona and Malik’s anisotropic Diffusion 25 4.3.3 Robust statistics 26 4.4 THE MODIFIED LINEAR LEAST-SQUARE SOLUTION: THE APPROACH BASED ON LINEAR-FORM JOINT ROBUST STATISTICS 29 CHAPTER 5 EXPERIMENT RESULTS 33 5.1 PARAMETER SETTING AND IMPLEMENTATION 33 5.2 RESULTS 34 CHAPTER 6 CONCLUSIONS AND FUTURE WORK 39 REFERENCE 41 | |
| dc.language.iso | en | |
| dc.subject | 最小平方法 | zh_TW |
| dc.subject | 本質影像 | zh_TW |
| dc.subject | 擴散 | zh_TW |
| dc.subject | 影像平滑處理 | zh_TW |
| dc.subject | 雙向濾波器 | zh_TW |
| dc.subject | 強健統計法 | zh_TW |
| dc.subject | Intrinsic image | en |
| dc.subject | least-square minimization | en |
| dc.subject | robust statistics | en |
| dc.subject | bilateral filter | en |
| dc.subject | image smoothing | en |
| dc.subject | diffusion | en |
| dc.title | 由單一影像以物理學為基礎之本質影像推測 | zh_TW |
| dc.title | Physics-Based Intrinsic Image Estimation from a Single Image | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許秋婷,許永真,莊永裕 | |
| dc.subject.keyword | 本質影像,擴散,影像平滑處理,雙向濾波器,強健統計法,最小平方法, | zh_TW |
| dc.subject.keyword | Intrinsic image,diffusion,image smoothing,bilateral filter,robust statistics,least-square minimization, | en |
| dc.relation.page | 42 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-23 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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