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研究生:鄒逸凡
研究生(外文):Yi-Fan Chou
論文名稱:利用數位相機進行光譜重建
論文名稱(外文):Obtaining Reflectance Functions Using Digital Cameras
指導教授:羅明羅明引用關係李三良李三良引用關係
指導教授(外文):Ronnier M LuoSan-Liang Lee
口試委員:羅明李三良
口試委員(外文):Ronnier M LuoSan-Liang Lee
口試日期:2012-10-04
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:101
語文別:英文
論文頁數:221
中文關鍵詞:色彩量測分光光度計相機校正光譜估測
外文關鍵詞:colour measurementspectrophotometercamera characterisationreflectance estimation
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  物體顏色可用其光譜反射率準確表示,此研究首先探討並比較多種量測反射率之方法。傳統上,利用分光光度計測色,但此種儀器設備為接觸式量測,一次只能量測單一色塊,且測試樣品表面須為均勻平面。而利用數位相機測色則可以克服這個限制,但系統準確性及精確性則會低於分光光度計。本論文將討論測色方法及顏色預估方法,前者包含分光光度計的測色效能,後者包含數位相機色彩特性校正模型,透過此特性校正模型可將數位相機數位訊號轉換成物體光譜反射率。
  此研究主要致力於數位相機的色彩演算技術開發,一般常用在相機的特性校正模型是將相機數位訊號的設備從屬色彩訊號RGB轉成非設備從屬色彩訊號,如XYZ或CIELAB色彩空間。此種特性校正模型需要一組目標特性校正色卡用來當作訓練資料,而要成功建立此模型取決於如何有效選擇這些顏色。因此在本論文中,提出一個新的方法去選取目標特性校正色塊,並指出那些顏色區域是重要的,特性校正色卡必須包含這些區域。
  以上這種基於非設備從屬色彩空間XYZ的特性校正模型,隨著光源改變會有不同模型,XYZ值也會跟著不同光源而有所改變。較好的方法去記錄色彩資訊,是直接將相機數位每個像素的訊號轉成光譜反射率值。五種常用的反射率還原演算法包含平滑限制、基函數、基函數平滑限制、溫納估計及假反置演算法。在此論文中,會用這五種方法將XYZ值還原至光譜反射率,另外有兩種自適應演算法將用來改善這五種基本演算法,此自適應演算法包含區域化訓練資料及權重矩陣。這些演算法的效能將用十萬個光譜數據來做測試,另外從這十萬個光譜數據中發展出一組基函數,可以成功的用線性方程式表示所有光譜數據。
  此論文發現,溫納估計或假反置演算法同時加上區域化訓練資料及權重矩陣可以有最好的效果。同樣的方法套用在數位相機RGB訊號值上,光譜反射率亦可以成功被還原,假反制演算法加上兩種自適應演算法可以最準確的還原光譜資料,用CIE2000色差公式來評估此演算法,其精度可小於一個色差值。
Spectral reflectance is the ‘finger print’ of the colour of an object. This study investigated a number of the methods to obtain spectral reflectance functions of colours, and to compare their performance. Spectrophotometers are traditionally colour measurement instruments. However, they can only measure one spot of flat uniform colour patches with direct contact. Using digital cameras as a means of colour measurement device to predict spectral reflectance can overcome this limitation, but system performance in terms of accuracy and precision is expected to be lower than using spectrophotometers. In the present work, the metrology and prediction of measuring colours were studied. The former included the evaluation of spectrophotometer performance. The latter considered characterisation models using digital cameras to predict spectral reflectance from camera responses.

Large efforts were spent to develop camera based technology. The characterisation model which was typically applied to transform the camera primary responses to device independent primaries (XYZ or CIELAB) requires a characterisation target. To make a successful model depends upon the selection of effective colours in the characterisation target. In the present research, a method for developing the characterisation target and the importance of colour regions for colour selection were proposed.

Using the colorimetry (XYZ) based characterisation models is illuminant dependency. A better way for colour specification using a digital camera is to estimate the reflectance of surface colours in a pixel-by-pixel basis. Five widely used methods including smoothness constraint, basis functions, basis functions with smoothness constraint, Wiener estimation and Pseudo-inverse were investigated for the reconstruction of reflectance function from the tristimulus values. Two adaptive methods including the localised set and the weighted matrix were applied to the basic methods. A set of 100,000 spectra was collected to evaluate the performance. A set of basis functions was also derived from the set which can represent all world colours. It was found that the Wiener estimation and pseudo-inverse methods modified by the adaptive methods performed the best among. The same techniques were applied to estimate reflectance functions from camera RGB response. The pseudo-inverse method with a localised set and weighted matrix gave the highest accuracy of 1.0 ΔE00.
論文摘要 i
Abstract ii
Acknowledgements iii
Table of Contents iv
List of Tables ix
List of Figures xii
List of Abbreviations xvi
Chapter 1 Introduction 1
1.1 Background 2
1.2 Aims and Objectives 4
1.3 Thesis Structure 4
1.4 Summary of Contribution of Knowledge 5
1.5 Related Publications 6
1.6 Other Publications 7
Chapter 2 Literature Survey 8
2.1 Colorimetry 9
2.1.1 Visible Spectrum 9
2.1.2 CIE Standard Illuminants 11
2.1.3 Object 14
2.1.4 CIE Colour Matching Function 15
2.1.5 Tristimulus Value Calculation 17
2.1.6 ASTM Weighting Tables 20
2.1.7 Uniform Colour Space 20
2.1.8 Colour Difference Formulae 22
2.1.8.1 CIELUV and CIELAB Formulae 23
2.1.8.2 CIEDE2000 Formula 24
2.1.9 Metamerism 26
2.2 Colour Measuring Instruments 28
2.2.1 Tristimulus Colorimeter 29
2.2.2 Spectrophotometer 30
2.2.3 Reflectance Measurement 31
2.2.4 Spectrophotometer Light Sources 32
2.2.5 Instrument Geometries of Illuminating and Viewing 33
2.2.6 The Dual-Beam Spectrophotometer 35
2.2.7 Instrument Calibration 35
2.2.8 Specifications of Spectrophotometers 36
2.2.8.1 Instrument Specifications 36
2.2.8.2 Performance Specifications 36
2.3 Digital Camera Characterisation 37
2.3.1 Digital Still Camera 37
2.3.2 Device Independent and Dependent Colour Space 39
2.3.3 Digital Camera Characterisation Models 39
2.3.3.1 Polynomial Model 41
2.4 Linear Models for Reflectance Representation 45
2.4.1 Principal Component Analysis (PCA) 45
2.4.2 Metrics for Spectral Match Quality 48
2.4.3 Basis Functions of Munsell Reflectance Spectra 50
2.4.4 Basis Functions of Paint and Artwork Reflectance 53
2.4.5 Basis Functions of Human Skins 55
2.4.6 Basis Functions of Natural Objects 55
2.5 Reflectance Estimation Techniques 60
2.5.1 Moore-Penrose Pseudo-Inverse 61
2.5.2 Smoothness Constraint 62
2.5.3 Basis Function 64
2.5.4 Basis Function with Smoothness Constraint 65
2.5.5 Wiener Estimation Method 66
2.5.6 Adaptive Methods Based on Training Set Selection 66
2.5.6.1 Shen et al.’s Adaptive Wiener Estimation 67
2.5.6.2 Babaei et al.’s Weighted Matrix 68
2.6 Summary 69
Chapter 3 Experimental Preparation 71
3.1 Instruments 72
3.1.1 The Spectrophotometers Used in This Study 72
3.1.1.1 GretagMacbethTM COLOR-EYE® 7000A Spectrophotometer (CE7000A) 72
3.1.1.2 Datacolor Spectraflash 600 (SF600) 73
3.1.1.3 X-RiteTM Color ITM7 73
3.1.1.4 X-RiteTM 962 74
3.1.1.5 SpectroEye 74
3.1.1.6 Overview 74
3.1.2 Camera Measurement System 76
3.1.2.1 Collection of Raw Camera Response with Linearization and Spatial Correction 77
3.2 Sample Sets 80
3.2.1 Sample Sets for Evaluation of Instrument Performance 82
3.2.1.1 Ceramic Colour Standard – Series II (CCSII) 82
3.2.1.2 Ceram Gray Scale (CGS) 83
3.2.1.3 EBU 84
3.2.1.4 10 DuPont Paint Pairs 85
3.2.1.5 122 Metamer Set 86
3.2.1.6 ColorChecker® (CC) 87
3.2.1.7 Digital ColorChecker® Semi Gloss (SG) 88
3.2.2 Sample Sets for Development of Characterisation Models and Reflectance Estimation from Camera Responses 89
3.2.2.1 GretagMacbethTM ColorChecker® DC (MCDC) 89
3.2.2.2 Munsell Book of Color – Glossy Collection 90
3.2.2.3 Professional Colour Communicator (PCC) 92
3.2.3 Sample Sets for Spectral Representation and Determination of the Best Computational Method for Reflectance Estimation 93
3.2.3.1 Paint Group 94
3.2.3.1.1 DuPont SpectraMaster® 94
3.2.3.1.2 Munsell II (Munsell Limit Color Cascade) 94
3.2.3.1.3 NCS 95
3.2.3.1.4 DIN 95
3.2.3.1.5 SOCS – Paint 95
3.2.3.2 Graphic Group 96
3.2.3.2.1 Packaging Ink 96
3.2.3.2.2 SOCS – Graphic 96
3.2.3.2.3 SOCS – Photo 97
3.2.3.2.4 SOCS – Printer 97
3.2.3.3 Plastic Group 97
3.2.3.3.1 Industry Plastic 98
3.2.3.4 Textile Group 98
3.2.3.4.1 Industry Cotton 98
3.2.3.4.2 Pantone® Cotton 99
3.2.3.4.3 SOCS – Textile 99
3.2.3.5 Skin Group 99
3.2.3.5.1 Oulu Skin 100
3.2.3.5.2 RIT Skin 100
3.2.3.5.3 SOCS – Face 100
3.2.3.6 Natural Group 101
3.2.3.6.1 Natural I (Cheung, 2000) 101
3.2.3.6.2 Natural II (Westland et al., 2000) 102
Chapter 4 Inter-Instrument Agreement 103
4.1 Instruments 104
4.1.1 Measuring Reflectance 104
4.2 Data Collection for Performance Evaluation 105
4.3 Results and Discussion 106
4.3.1 Uniform Correction of the Camera System 106
4.3.2 Accuracy 107
4.3.3 Short Term Repeatability 109
4.3.4 Long Term Repeatability 110
4.3.5 Inter-Instrument Agreement 111
4.3.6 Relative Colour Difference 113
4.3.7 Sphere vs. 45oa:0o 113
4.4 Conclusions 114
Chapter 5 Development of Colorimetric Models and Colour Targets 116
5.1 Data Collections 117
5.2 Camera Characterisation Model 118
5.2.1 Order of the Polynomial Model 118
5.3 Colour Selections for the Training Target 119
5.3.1 Hardeberg Method 119
5.3.2 MAXMINC Method 120
5.3.3 The Colour Difference Iteration (CDI) Method 121
5.4 Comparison among Colour Selection Methods 123
5.5 Colour Selection Guideline 129
5.6 Conclusions 134
Chapter 6 Spectral Representation of Databases 135
6.1 Spectral Datasets 136
6.2 Spectral Representation 137
6.2.1 Basis Functions for an Individual Set 138
6.2.1.1 Spectral Representation of Paint Samples 140
6.2.1.2 Spectral Representation of Graphic Samples 143
6.2.1.3 Spectral Representation of Plastic Samples 146
6.2.1.4 Spectral Representation of Textile Samples 147
6.2.1.5 Spectral Representation of Skin Samples 149
6.2.1.6 Spectral Representation of Natural Samples 151
6.2.2 Development of a Set of Basis Functions for All Materials 153
6.2.2.1 Number of Basis Functions for Representing Material Groups 153
6.2.2.2 Selection of the Mixture Set to Represent All Spectra 154
6.2.2.3 Spectral Representation for All Materials 159
6.3 Conclusions 167
Chapter 7 Reflectance Estimation from Colorimetric Values 169
7.1 Reflectance Estimation Method 170
7.1.1 Model Performance Varied with Number of Basis Functions 172
7.1.2 Model Performance Varied with Number of Localised Training Samples 174
7.2 Methods Comparison 181
7.3 Conclusions 189
Chapter 8 Reflectance Estimation from Camera Responses 190
8.1 Spectral Characterisation Approaches 191
8.1.1 Finlayson et al.’s method 192
8.1.2 Pseudo-Inverse method 194
8.1.3 Two-stage method 194
8.2 Experiment 194
8.2.1 Finlayson et al.’s Spectral Sensitivity Functions 195
8.3 Methods Comparison 199
8.4 Conclusions 203
Chapter 9 Conclusions and Future Work 204
9.1 Conclusions 205
9.2 Future Work 209
References 210
Author Biography 221
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