請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63564完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一平(Yi-Ping Hung) | |
| dc.contributor.author | Chia-Ping Chen | en |
| dc.contributor.author | 陳嘉平 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:14:04Z | - |
| dc.date.available | 2014-08-20 | |
| dc.date.copyright | 2012-08-20 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-20 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63564 | - |
| dc.description.abstract | 光線變化對於物體的外貌扮演了重要的角色，它造成了外觀的濃淡、反光、及陰影的效果。雖然這個世界因能有如此多樣的光影效果而迷人，這些光影效果卻對許多電腦視覺的問題造成了重大的困難。若試著開始描述光線變化所造成的影響，通常會因它的無窮自由度而感到棘手，儘管此問題的本質看似令人怯步，仍有如球面調和函數這樣顯著的進展被提出。球面調和函數提供了一種一致的數學架構來描述複雜的光線變化。反射出的光線可被看成是入射光線和物體表現反射屬性的一種迴旋積的結果，事實上，一個漫射的物體表面可以被當作是作用在入射光訊號上的一個低頻濾波器，畢竟一個漫射的物體即使在複雜的光線上仍能保有平滑的外觀。若將這些概念正式地用訊號處理的觀點來描述的話，就可導致許多新潁有趣的洞悉及在電腦視覺問題上的實際應用，例如辨視、光線估測、以及形狀重建。在本篇論文中，基於以上的技術我們檢視了幾個典型的電腦視覺問題並提出了一些新奇並有效的方法，先前的方法通常都有些踞限的假設，它們通常簡化甚至忽略光線變化所造成的影響，而我們提出的方法即使在複雜、未受控制的光線下仍能成功運作。 | zh_TW |
| dc.description.abstract | Lighting variation plays an important role on the appearance of objects, as the patterns of shading, specularities, and shadows. While the diverse ways in which our world can be illuminated make this world fascinating, the resulting variation in appearance is a major source of difficulty for many computer vision applications. Modeling the lighting variation may seem intractable at first glance due to its infinite number of degrees of freedom. Despite the daunting nature of this problem, some notable developments including the spherical harmonics are introduced. The spherical harmonics representation provides a coherent mathematical framework for modeling general lighting conditions. The reflected light field is considered as a convolution of the incident lighting and reflectance properties of object surfaces. In effect, a diffuse surface can be viewed as a low-pass filter acting on the incident illumination signal since a diffuse surface has a smooth appearance even if the lighting is complicated. Making these ideas formal in a signal processing framework, the theoretical results lead to many interesting insights and have practical implications of many computer vision problems, such as recognition, lighting estimation, and reconstruction. In this dissertation, we examine some traditional vision problems and propose novel methods based on the above techniques. Restricted assumptions that simplify or even ignore the lighting variation are relaxed such that the proposed methods work under general lighting conditions in uncontrolled environments. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:14:04Z (GMT). No. of bitstreams: 1 ntu-101-D94922025-1.pdf: 7034896 bytes, checksum: cd33f8f2915d05b3d51a2708c5bb3d31 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 摘要iii
Abstract v List of Figures xi List of Tables xiii 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Lighting Normalization for Face Recognition . . . . . . . . . . 4 1.2.2 Four-Source Photometric Stereo . . . . . . . . . . . . . . . . . 4 1.2.3 Pixel-Based Correspondence for Moving Objects . . . . . . . . 5 2 Modeling Lighting Variation with Spherical Harmonics 7 2.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Reflection as Convolution . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Spherical Harmonic Representation . . . . . . . . . . . . . . . . . . . 8 3 Lighting Normalization for Face Recognition 13 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3 Intrinsic Illumination Subspace . . . . . . . . . . . . . . . . . . . . . 20 3.3.1 Intrinsic Illumination Subspace . . . . . . . . . . . . . . . . . 20 3.3.2 Generic Intrinsic Illumination Subspace . . . . . . . . . . . . 23 3.4 Enforcing Non-negative Light with NMF . . . . . . . . . . . . . . . . 26 3.5 Lighting Normalization with Intrinsic Illumination Subspace . . . . . 28 3.6 Experiments and Discussion . . . . . . . . . . . . . . . . . . . . . . . 30 3.6.1 Intrinsic Illumination Subspace . . . . . . . . . . . . . . . . . 32 3.6.2 Lighting Normalization and Face Recognition . . . . . . . . . 34 3.6.3 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4 The 4-Source Photometric Stereo 43 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2.1 Modeling Reflection . . . . . . . . . . . . . . . . . . . . . . . 46 4.3 Lighting Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.4 Shape and Reflectance Reconstruction . . . . . . . . . . . . . . . . . 51 4.4.1 Refine Lighting Estimation . . . . . . . . . . . . . . . . . . . 52 4.4.2 Refine Reflectance . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4.3 Refine Surface Normals . . . . . . . . . . . . . . . . . . . . . 53 4.4.4 Iterative Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 54 4.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.6 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . 58 5 Pixel-Based Correspondence for Moving Objects 61 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3 Pixel-Based Correspondence . . . . . . . . . . . . . . . . . . . . . . . 65 5.3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.3.2 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.3.3 Two-Stage Over-Parametrization Method . . . . . . . . . . . 68 5.4 Shape Reconstruction Algorithm . . . . . . . . . . . . . . . . . . . . 70 5.4.1 Lighting Estimation . . . . . . . . . . . . . . . . . . . . . . . 70 5.4.2 Shape Reconstruction . . . . . . . . . . . . . . . . . . . . . . 72 5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.5.1 Synthetic Experiment . . . . . . . . . . . . . . . . . . . . . . 76 5.5.2 Real Experiment . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.6 Extension for General Lighting Conditions . . . . . . . . . . . . . . . 79 5.6.1 Spherical Harmonic Representation . . . . . . . . . . . . . . . 81 5.6.2 Pixel-Based Correspondence . . . . . . . . . . . . . . . . . . . 82 5.6.3 Shape Reconstruction Algorithm . . . . . . . . . . . . . . . . 84 5.7 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . . 85 6 Conclusions and Future Work 87 Bibliography 89 | |
| dc.language.iso | en | |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 形狀重建 | zh_TW |
| dc.subject | 人臉辨識 | zh_TW |
| dc.subject | 球形調和函數 | zh_TW |
| dc.subject | 光線變化 | zh_TW |
| dc.subject | lighting variation | en |
| dc.subject | face recognition | en |
| dc.subject | shape reconstruction | en |
| dc.subject | computer vision | en |
| dc.subject | spherical harmonics | en |
| dc.title | 光線變化及其對視覺問題之應用 | zh_TW |
| dc.title | Lighting Variation and Its Applications for Vision Problems | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 陳祝嵩(Chu-Song Chen) | |
| dc.contributor.oralexamcommittee | 傅楸善(Chiou-Shann Fuh),莊永裕(Yung-Yu Chuang),賴尚宏(Shang-Hong Lai),王才沛(Tsaipei Wang),徐繼聖(Gee-Sern Hsu) | |
| dc.subject.keyword | 電腦視覺,光線變化,球形調和函數,人臉辨識,形狀重建, | zh_TW |
| dc.subject.keyword | computer vision,lighting variation,spherical harmonics,face recognition,shape reconstruction, | en |
| dc.relation.page | 95 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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