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題名 多視角影像中的退化問題與參數估測
Degeneracies and Parameter Estimation in Multiple View Images
作者 詹凱軒
Chan, Kai Hsuan
貢獻者 何瑁鎧<br>郭正佩<br>唐政元
Hor, Maw Kae<br>Kuo, Pei Jeng<br>Tang, Cheng Yuan
詹凱軒
Chan, Kai Hsuan
關鍵詞 退化
參數估測
多視角幾何
緊要配置
粒子群最佳化
貼片匹配法
degeneracy
parameter estimation
multiple view geometry
critical configuration
particle swarm optimization
patch-based matching
日期 2013
上傳時間 1-Nov-2013 11:43:28 (UTC+8)
摘要 多視角影像比起單一影像，可提供更多資訊，有助於影像之分析、比對與建模。相關研究諸如利用多視角影像進行三維模型重建、影像拼接、影像修補、利用多視角攝影機進行物體與人物追蹤、與四維動態模型捕捉等。然而，多視角影像的研究中，自動且精確的偵測影像對應點一直是個困難的問題。其中多視角影像的退化(degeneracy)問題，經常被研究者所忽略，而此一問題往往造成幾何關係上錯誤的估算，影響後續處理的精確度。
本研究中我們所定義之退化問題涵蓋範圍較廣，包含傳統數學上的退化，以及同時考慮多視角幾何與影像紋路匹配所造成之問題，我們將這些問題歸納成三種類型並進行探討：第一種類型，當相機參數未知、且影像幾何關係也未估算時，需透過對應點進行影像幾何的計算，若這些對應點相對之三維座標與相機中心之配置，剛好落在特定曲面或平面之情況，則會造成錯誤的估算；第二種類型，當相機參數已知、或已估算求得影像幾何關係時，透過這些估算的幾何關係，進行對應點的轉換，在特定情況下也會造成錯誤的結果；第三種類型，當我們透過多視角幾何關係，結合影像紋路，利用貼片(patch-based)方法進行對應點相似度評估時，容易因為視角的差異、比例的差異、及貼片平面與物體表面的差異，造成無法正確評估之問題。
我們針對這三種類型的退化問題，分別進行分析與探討，並提供適當的建議與處理方法，以避開可能遇到的退化狀況。同時，無論是在對應點的估算、多視角三維重建、與多視角幾何的評估等，通常需要透過強健的參數估測方法來求得，我們也分析、討論近年許多代表性的參數估測方法，並提出更穩健的估測方法，應用於處理特定退化問題。
透過我們提出的退化規避方法，能有效的提高多視角影像處理與重建三維模型的精確度。
Multiple view images carry much more information than that in single view images. This information is helpful in analyzing, matching, and reconstruction the points and models in real scenes. There are many related researches such as three-dimensional model reconstruction from multi-view images, multi-view image stitching, multi-view image inpainting, multi-view object and human tracking as well as four-dimensional dynamic model capture, etc. However, automatic and accurate corresponding point matching is one of the difficult problems in multiple view researches. Moreover, the multi-view degeneracy problems are normally ignored by researchers. These problems will include geometric estimation error and cause inaccuracy in subsequently processing.
In this dissertation, the definition of the degeneracy covers a wider scope. It includes the traditional degeneracy as discussed in mathematics as well as the corresponding point matching error caused by the model inaccuracy in geometries and in textures. We classify these problems into three categories and provide methods and guidelines for avoiding the degeneracy problems in multi-view image processing. The first category assumes the camera parameters or the geometries (for example, the fundamental matrix) are unknown and which must be estimated using the corresponding points. If the 3D locations of the corresponding points and the camera centers fall into particular configurations, such as the ruled quadric, the camera parameters estimation may yield multiple solutions or erroneous results. The second category assumes the camera geometry is known or known geometries (for example, the fundamental matrix or the trifocal tensor) are used to transfer the corresponding points. It may cause erroneous estimation under certain conditions. The third category occurs if we use the patch-based method to estimate the similarity of the corresponding points in multiple views, it will cause unreasonable estimation due to different viewing angle, image scaling, or inconsistencies between the patch plane and the object surface.
We propose various guidelines as well as processing methods in order to avoid the degeneracies. In addition, it generally requires robust parameter estimation methods to accomplish for the corresponding point estimations, the 3D reconstructions from multiple views, as well as for the multi-view geometries estimations. We analyzed the most representative parameter estimation methods developed in recent years and proposed the more robust methods that can be used to handle the specific degeneracy problems.
Using the proposed methods and guidelines for degeneracy avoidance, we can effectively improve the accuracy of the multi-view image processing as well as 3D model reconstructions.
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描述 博士
國立政治大學
資訊科學學系
96753501
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096753501
資料類型 thesis
dc.contributor.advisor 何瑁鎧<br>郭正佩<br>唐政元zh_TW
dc.contributor.advisor Hor, Maw Kae<br>Kuo, Pei Jeng<br>Tang, Cheng Yuanen_US
dc.contributor.author (Authors) 詹凱軒zh_TW
dc.contributor.author (Authors) Chan, Kai Hsuanen_US
dc.creator (作者) 詹凱軒zh_TW
dc.creator (作者) Chan, Kai Hsuanen_US
dc.date (日期) 2013en_US
dc.date.accessioned 1-Nov-2013 11:43:28 (UTC+8)-
dc.date.available 1-Nov-2013 11:43:28 (UTC+8)-
dc.date.issued (上傳時間) 1-Nov-2013 11:43:28 (UTC+8)-
dc.identifier (Other Identifiers) G0096753501en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/61488-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 96753501zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 多視角影像比起單一影像，可提供更多資訊，有助於影像之分析、比對與建模。相關研究諸如利用多視角影像進行三維模型重建、影像拼接、影像修補、利用多視角攝影機進行物體與人物追蹤、與四維動態模型捕捉等。然而，多視角影像的研究中，自動且精確的偵測影像對應點一直是個困難的問題。其中多視角影像的退化(degeneracy)問題，經常被研究者所忽略，而此一問題往往造成幾何關係上錯誤的估算，影響後續處理的精確度。
本研究中我們所定義之退化問題涵蓋範圍較廣，包含傳統數學上的退化，以及同時考慮多視角幾何與影像紋路匹配所造成之問題，我們將這些問題歸納成三種類型並進行探討：第一種類型，當相機參數未知、且影像幾何關係也未估算時，需透過對應點進行影像幾何的計算，若這些對應點相對之三維座標與相機中心之配置，剛好落在特定曲面或平面之情況，則會造成錯誤的估算；第二種類型，當相機參數已知、或已估算求得影像幾何關係時，透過這些估算的幾何關係，進行對應點的轉換，在特定情況下也會造成錯誤的結果；第三種類型，當我們透過多視角幾何關係，結合影像紋路，利用貼片(patch-based)方法進行對應點相似度評估時，容易因為視角的差異、比例的差異、及貼片平面與物體表面的差異，造成無法正確評估之問題。
我們針對這三種類型的退化問題，分別進行分析與探討，並提供適當的建議與處理方法，以避開可能遇到的退化狀況。同時，無論是在對應點的估算、多視角三維重建、與多視角幾何的評估等，通常需要透過強健的參數估測方法來求得，我們也分析、討論近年許多代表性的參數估測方法，並提出更穩健的估測方法，應用於處理特定退化問題。
透過我們提出的退化規避方法，能有效的提高多視角影像處理與重建三維模型的精確度。		zh_TW
dc.description.abstract (摘要) Multiple view images carry much more information than that in single view images. This information is helpful in analyzing, matching, and reconstruction the points and models in real scenes. There are many related researches such as three-dimensional model reconstruction from multi-view images, multi-view image stitching, multi-view image inpainting, multi-view object and human tracking as well as four-dimensional dynamic model capture, etc. However, automatic and accurate corresponding point matching is one of the difficult problems in multiple view researches. Moreover, the multi-view degeneracy problems are normally ignored by researchers. These problems will include geometric estimation error and cause inaccuracy in subsequently processing.
In this dissertation, the definition of the degeneracy covers a wider scope. It includes the traditional degeneracy as discussed in mathematics as well as the corresponding point matching error caused by the model inaccuracy in geometries and in textures. We classify these problems into three categories and provide methods and guidelines for avoiding the degeneracy problems in multi-view image processing. The first category assumes the camera parameters or the geometries (for example, the fundamental matrix) are unknown and which must be estimated using the corresponding points. If the 3D locations of the corresponding points and the camera centers fall into particular configurations, such as the ruled quadric, the camera parameters estimation may yield multiple solutions or erroneous results. The second category assumes the camera geometry is known or known geometries (for example, the fundamental matrix or the trifocal tensor) are used to transfer the corresponding points. It may cause erroneous estimation under certain conditions. The third category occurs if we use the patch-based method to estimate the similarity of the corresponding points in multiple views, it will cause unreasonable estimation due to different viewing angle, image scaling, or inconsistencies between the patch plane and the object surface.
We propose various guidelines as well as processing methods in order to avoid the degeneracies. In addition, it generally requires robust parameter estimation methods to accomplish for the corresponding point estimations, the 3D reconstructions from multiple views, as well as for the multi-view geometries estimations. We analyzed the most representative parameter estimation methods developed in recent years and proposed the more robust methods that can be used to handle the specific degeneracy problems.
Using the proposed methods and guidelines for degeneracy avoidance, we can effectively improve the accuracy of the multi-view image processing as well as 3D model reconstructions.		en_US
dc.description.tableofcontents Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Related Work 4
1.3 Contributions of the Dissertation 6
1.4 Dissertation Organization 7
Chapter 2 Multiple View Geometry and Degeneracies 9
2.1 Introduction 9
2.2 Multiple View Geometry 10
2.2.1 Two view geometry 11
2.2.2 Three view geometry 15
2.2.3 Multiple view reconstruction 19
2.3 The Applications in Multiple Views 19
2.4 Degeneracies in Multiple View Images 21
2.4.1 Multi-view critical configurations 22
2.4.2 Degeneracy in geometry transfer 29
2.4.3 Degeneracy in the geometry and texture matching 37
2.5 Summary 40
Chapter 3 Parameter Estimation in Multiple View Geometry 43
3.1 Introduction 43
3.2 Parameter Estimations 43
3.2.1 Linear methods 44
3.2.2 Robust methods 45
3.2.3 Evolution methods 48
3.3 Experiments 51
3.3.1 Experimental data 51
3.3.2 The analysis in the parameter estimation methods 53
3.4 Summary 54
Chapter 4 Robust Methods for Critical Configuration 57
4.1 Introduction 57
4.2 Parameter Estimation by ROPSO 59
4.2.1 ROPSO algorithm 59
4.2.2 Homography estimation 64
4.2.3 Fundamental matrix estimation 66
4.2.4 Trifocal tensor estimation 68
4.3 The Combination of the ROPSO and Genetic Algorithm 69
4.4 Experiments 71
4.4.1 Homography estimation 72
4.4.2 Fundamental matrix estimation 76
4.4.3 Trifocal tensor estimation 88
4.5 Summary 98
Chapter 5 The Avoidance of Degeneracy in Geometry Transfer 99
5.1 Introduction 99
5.2 The Influence of the Degeneracy in Epipolar Transfer 102
5.3 Robust Trifocal Tensor for Structure from Motion Estimation 106
5.3.1 Structure from motion 106
5.3.2 The combination of the SfM and trifocal Tensor 107
5.4 Experiments 110
5.5 Summary 125
Chapter 6 The Avoidance of Degeneracy in Texture Matching 127
6.1 Introduction 127
6.2 Mutually Supported Patch Matching 129
6.2.1 Dynamic Gaussian filtering 131
6.2.2 Integrated similarity function 132
6.3 Image Selection and Filtering 133
6.4 Experiments 133
6.4.1 Experiment environment 133
6.4.2 Experiment on mutually supported patch 135
6.4.3 Accurate matching 138
6.5 Summary 140
Chapter 7 Conclusions 143
7.1 Multi-view critical configurations 143
7.2 Degeneracy in geometry transfer 144
7.3 Degeneracy in the geometry and texture matching 145
Chapter 8 Future Work 147
8.1 Degeneracy problems 147
8.2 Parameter estimation 148
8.3 Intelligence tools for more applications 148
Reference 150		zh_TW
dc.format.extent 13121678 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096753501en_US
dc.subject (關鍵詞) 退化zh_TW
dc.subject (關鍵詞) 參數估測zh_TW
dc.subject (關鍵詞) 多視角幾何zh_TW
dc.subject (關鍵詞) 緊要配置zh_TW
dc.subject (關鍵詞) 粒子群最佳化zh_TW
dc.subject (關鍵詞) 貼片匹配法zh_TW
dc.subject (關鍵詞) degeneracyen_US
dc.subject (關鍵詞) parameter estimationen_US
dc.subject (關鍵詞) multiple view geometryen_US
dc.subject (關鍵詞) critical configurationen_US
dc.subject (關鍵詞) particle swarm optimizationen_US
dc.subject (關鍵詞) patch-based matchingen_US
dc.title (題名) 多視角影像中的退化問題與參數估測zh_TW
dc.title (題名) Degeneracies and Parameter Estimation in Multiple View Imagesen_US
dc.type (資料類型) thesisen
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