Analysis of Ancient Mosaic Images for Dedicated ... - Institut Fresnel

Filippo Stanco, Sebastiano Battiato, Giovanni Gallo 

Digital Imaging for Cultural Heritage 

Preservation

Contributors 

Sebastiano Battiato 

University of Catania 

Catania, Italy 

Lamia Benyoussef 

Ecole Centrale Marseille 

Marseille, France 

Simone Bianco 

University of Milano Bicocca 

Milan, Italy 

Vittoria Bruni 

Consiglio Nazionale delle Ricerche 

Rome, Italy 

Yang Cai 

Carnegie Mellon University 

Pittsburgh, USA 

Roberto Caldelli 

University of Florence 

Florence, Italy 

Marco Callieri 


Pisa, Italy 

Vito Cappellini 



Paolo Cignoni 


Pisa, Italy 

Jacob Collins 

c/o Adelson Galleries 

New York, USA 

Alessandro Colombo 


Milan, Italy 

David Corrigan 

Trinity College 

Dublin, Ireland 

Massimiliano Corsini 


Pisa, Italy 

Andrew Crawford 


Rome, Italy 

Paul Debevec 

University of Southern California 

Playa Vista, USA 

Andrea Del Mastio 



Andrey Del Pozo 

Univesity of Illinois at Urbana-Champaign 

Urbana, USA 

Matteo Dellepiane 


Pisa, Italy 

Stéphane Derrode 

École Centrale Marseille 


Gianpiero Di Blasi 

University of Palermo 

Palermo, Italy 

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viii 

Marco Duarte 

Princeton University 

Princeton, USA 

Per Einarsson 



Marcos Fajardo 



Yasuo Furuichi 

Kanagawa, Japan 

Giovanni Gallo 



Andrew Gardner 



Francesca Gasparini 


Milan, Italy 

Tim Hawkins 



Andrew Jones 



Dave Kale 

Stanford University 

Stanford, USA 

Anil Kokaram 



Ashutosh Kulkarni 

Stanford University 

Stanford, USA 

Therese Lundgren 



Philippe Martinez 

Ecole Normale Superieure 

Paris, France 

Nikos Nikolaidis 

Aristotle University of Thessaloniki 

Thessaloniki, Greece 

Ioannis Pitas 



Francois Pitie 



Charis Poullis 



Giovanni Puglisi 



Giovanni Ramponi 

University of Trieste 

Trieste, Italy 

Fabio Remondino 

B. Kessler Foundation (FBK) 

Trento, Italy 

Alfredo Restrepo Palacios 

Universidad de los Andes 

Bogotá, Colombia 

M. Dirk Robinson 

Ricoh Innovations 

Menlo Park, USA 

Silvio Savarese 

University of Michigan 

Ann Arbor, USA 

Sara J. Schechner 

Harvard University

Cambridge, USA 

Raimondo Schettini 


Milan, Italy 

Roberto Scopigno 


Pisa, Italy 

Ron Spronk 

Queen’s University, 

Kingston, Canada 

Filippo Stanco 



David G. Stork 

Ricoh Innovations 

Menlo Park, USA 

Jessi Stumpfel 



Davide Tanasi 



Chris Tchou 



David Tingdahl 

Katholieke Universiteit Leuven 

Leuven, Belgium 

Efthymia Tsamoura 



Christopher W. Tyler 

Smith-Kettlewell Eye Research Institute 

San Francisco, USA 

Francesca Uccheddu 



Luc Van Gool 

Katholieke Universiteit Leuven 


Maarten Vergauwen 

GeoAutomation 


Domenico Vitulano 


Rome, Italy 

Nathaniel Yun 



Nicholas C. Williams 

Towan Headland 

Cornwall, UK 

Andrew R. Willis 

University of North Carolina at Charlotte 

Charlotte, USA 

Silvia Zuffi 

ITC CNR 

Milan, Italy 

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Contents 

1 Experiencing the Past. Computer Graphics in Archaeology 1 

Filippo Stanco, Davide Tanasi 

1.1 The Past and the Future. Archaeology and Computer Science . . . . . . . . . . . . . . . . 1 

1.2 From the Field to the Screen: 3D Computer Graphics and the Archaeological Heritage . . 2 

1.2.1 Computer Graphics 3D and the Archaeological Fieldwork . . . . . . . . . . . . . 2 

1.2.2 Monitoring the Heritage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

1.2.3 The Virtual Museum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

1.2.4 3D Modeling as Cognitive Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

1.3 The Archeomatica Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

1.4 Archaeological 3D Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

1.5 Haghia Triada, Crete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 

1.5.1 Propylon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

1.5.2 House of the Razed Rooms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

1.5.3 VAP house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

1.6 Polizzello Mountain, Sicily . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

1.6.1 Buildings A, B, C, D, E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

1.6.2 Temenos and Room III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

1.6.3 Precinct F, East House, Temenos House . . . . . . . . . . . . . . . . . . . . . . . 16 

1.6.4 The Virtual Acropolis and the Multilayered 3D Model . . . . . . . . . . . . . . . 18 

1.7 Digital Restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

1.7.1 Minoan Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

1.7.2 Asclepius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

1.7.3 Female Torso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

1.7.4 Hellenistic Thysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

1.8 Dealing with Image Data in Archaeology: New Perspectives . . . . . . . . . . . . . . . . . 27 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

2 Using Digital 3D Models for Study and Restoration of Cultural Heritage Artifacts 39 

Matteo Dellepiane, Marco Callieri, Massimiliano Corsini, Roberto Scopigno 

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

2.2 Visual Communication of Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

2.2.1 Computer-generated Animations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

2.2.2 Interactive Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

2.2.3 Geographic Web Browsers Deploying 3D Models . . . . . . . . . . . . . . . . . 45 

2.3 Art Catalogs and Digital Repositories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

2.4 Digital 3D as a Tool for Art Scholars . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 

2.4.1 Using 3D Scanning to Analyze an Attribution Proposal . . . . . . . . . . . . . . . . 47 

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2.4.2 The CENOBIUM Project: an Integrated Visual Comparison of Historiated Capitals 50 

2.4.3 Classifying and Archiving Carved Faces: the Bayon Digital Archival Project . . . . 51 

2.5 Physical Reproduction from the Digital Model . . . . . . . . . . . . . . . . . . . . . . . 52 

2.6 Virtual Reconstruction and Re-assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

2.6.1 Virtual Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

2.6.2 Virtual Re-assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

2.6.3 Virtual Repainting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 

2.7 Supporting the Restoration Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 

2.7.1 Tools for Investigation and Diagnostics . . . . . . . . . . . . . . . . . . . . . . . 59 

2.7.2 Tools Supporting Knowledge Management . . . . . . . . . . . . . . . . . . . . . 62 

2.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 

3 Accurate and Detailed Image-Based 3D Documentation of Large Sites and Complex Objects 73 

Fabio Remondino 

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 

3.2 Reality-based 3D Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 

3.2.1 Techniques and Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 

3.2.2 Multi-sensor and Multi-source Data Integration . . . . . . . . . . . . . . . . . . . 76 

3.2.3 Standards in Digital 3D Documentation . . . . . . . . . . . . . . . . . . . . . . . 78 

3.3 Photogrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 

3.3.1 Image Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 

3.3.2 Camera Calibration and Image Orientation . . . . . . . . . . . . . . . . . . . . . 82 

3.3.3 3D Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 

3.3.4 Structuring and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 

3.3.5 Texturing and Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 

3.3.6 Main Applications and Actual Problems . . . . . . . . . . . . . . . . . . . . . . . 93 

3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 

4 Processing Sampled 3D Data: Reconstruction and Visualization Technologies 105 

Marco Callieri, Matteo Dellepiane, Paolo Cignoni, Roberto Scopigno 

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 

4.1.1 Sources of Sampled 3D Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 

4.2 Basic Geometric Processing of Scanned Data . . . . . . . . . . . . . . . . . . . . . . . . 108 

4.2.1 The 3D Scanning Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 

4.2.2 Implementing Range Maps Alignment as an Automatic Process . . . . . . . . . . 113 

4.2.3 Enhancing Incomplete Surface Sampling . . . . . . . . . . . . . . . . . . . . . . 115 

4.3 Color Sampling and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 

4.3.1 Basic Acquisition of Color Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 

4.3.2 Recovering Camera Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 

4.3.3 Mapping Complex Photographic Detail on 3D Models . . . . . . . . . . . . . . . 119 

4.3.4 Advanced Color Data Acquisition: Sampling Surface Reflection Properties . . . . 120 

4.4 MeshLab: an Open Source Tool for Processing 3D Scanned Data . . . . . . . . . . . . . . 121 

4.5 Efficient Visualization and Management of Sampled 3D Data . . . . . . . . . . . . . . . 123 

4.5.1 Simplification and Multiresolution Management of Huge Models . . . . . . . . . 124

4.5.2 Mesh-based vs. Point-based Encoding and Rendering . . . . . . . . . . . . . . . . 125 

4.5.3 Usability of Virtual Heritage Worlds . . . . . . . . . . . . . . . . . . . . . . . . . 126 

4.5.4 Not Just 3D Data: Adding Other Knowledge . . . . . . . . . . . . . . . . . . . . . 127 

4.5.5 Presenting 3D Data on the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 

4.6 3D Digitization: How to Improve Current Procedures and Make It More Practical and 

Successful . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 

4.6.1 Technology - Limitations Perceived by Practitioners . . . . . . . . . . . . . . . . 128 

4.6.2 Misuse of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 

4.6.3 Better Management of 3D Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 

4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 

5 Digitizing the Parthenon: Estimating Surface Reflectance under Measured Natural Illumination 

137 

Paul Debevec, Chris Tchou, Andrew Gardner, Tim Hawkins, Charis Poullis, Jessi Stumpfel, Andrew Jones, 

Nathaniel Yun, Per Einarsson, Therese Lundgren, Marcos Fajardo and Philippe Martinez 

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 

5.2 Background and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 

5.3 Data Acquisiton and Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 

5.3.1 Camera Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 

5.3.2 BRDF Measurement and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 141 

5.3.3 Natural Illumination Capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 

5.3.4 3D Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 

5.3.5 Photograph Acquisition and Alignment . . . . . . . . . . . . . . . . . . . . . . . . 151 

5.4 Reflectometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 

5.4.1 General Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 

5.4.2 Multiresolution Reflectance Solving . . . . . . . . . . . . . . . . . . . . . . . . . 153 

5.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 

5.6 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 

5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 

6 ARC3D: a Public Web Service that Turns Photos into 3D Models 163 

David Tingdahl, Maarten Vergauwen, and Luc Van Gool 

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 

6.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 

6.2.1 System Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 

6.2.2 Upload Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 

6.2.3 Modelviewer Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 

6.3 Automatic Reconstruction Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 

6.3.1 Pipeline Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 

6.3.2 Opportunistic Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 

6.3.3 Hierarchical Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 

6.3.4 Parallel Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 

6.4 Practical Guidelines for Shooting Images . . . . . . . . . . . . . . . . . . . . . . . . . . 173 

6.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 

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6.4.2 Image Shooting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 

6.4.3 Scene Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 

6.5 Case Study: Reconstruction of the Mogao Caves of Dunhuang . . . . . . . . . . . . . . . 175 

6.5.1 3D Reconstruction of Mogao Cave 322 . . . . . . . . . . . . . . . . . . . . . . . 175 

6.5.2 Image Capturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 

6.5.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 

6.6 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 

6.6.1 A Complete Building: Arc de Triomphe . . . . . . . . . . . . . . . . . . . . . . . . 181 

6.6.2 Environment Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 

6.6.3 Further Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 

6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 

7 Pattern Discovery from Eroded Rock Art 191 

Yang Cai 

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 

7.2 Surface Imaging Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 

7.2.1 Laser Scan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 

7.2.2 Pattern Projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 

7.2.3 Stick Shadow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 

7.2.4 Multiview Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 

7.2.5 Polynomial Texture Maps (PTM) . . . . . . . . . . . . . . . . . . . . . . . . . . 199 

7.3 Pattern Discovery Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 

7.3.1 Simulated Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 

7.3.2 Data Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 

7.3.3 Transformation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 

7.3.4 Off-Line Handwriting Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 205 

7.3.5 Interactive Versus Automatic Discovery . . . . . . . . . . . . . . . . . . . . . . . . 207 

7.4 From Reconstruction to Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 

7.5 Interaction Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 

7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 

8 Computational Analysis of Archaeological Ceramic Vessels and their Fragments 215 

Andrew R. Willis 

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 

8.1.1 Significance of Generic Artifact Reconstruction . . . . . . . . . . . . . . . . . . . . 217 

8.1.2 Significance of Ceramic Vessel Reconstruction . . . . . . . . . . . . . . . . . . . . 217 

8.1.3 Traditional Puzzle Solving and Artifact Reconstruction . . . . . . . . . . . . . . . 218 

8.2 Artifact Reconstruction Systems: Basic Components and Concepts . . . . . . . . . . . . 219 

8.2.1 Digitizing Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 

8.2.2 Approaches for Computational Sherd Analysis . . . . . . . . . . . . . . . . . . . 220 

8.2.3 Computerized Typology for Automated Sherd Classification . . . . . . . . . . . . 222 

8.2.4 Computerized Vessel Reconstruction by Fragment Matching . . . . . . . . . . . . 224 

8.3 Computational Models for Vessels and their Fragments . . . . . . . . . . . . . . . . . . . 225 

8.3.1 Modeling Ceramic Vessels as Surfaces of Revolution . . . . . . . . . . . . . . . . 225

8.3.2 Estimating the Vessel Axis from Digitized Sherds . . . . . . . . . . . . . . . . . . 226 

8.3.3 Estimating Profile Curves from Digitized Sherds . . . . . . . . . . . . . . . . . . . 227 

8.3.4 Simultaneously Solving for the Axis and Profile Curve . . . . . . . . . . . . . . . 230 

8.3.5 Dealing with Asymmetries in Archaeological Ceramic Sherds . . . . . . . . . . . 232 

8.3.6 Vessel Reconstruction by Matching Axis/Profile Curve Models . . . . . . . . . . . 233 

8.4 Vessel Reconstruction by Sherd Matching: 3D Puzzle Solving . . . . . . . . . . . . . . . 234 

8.4.1 The Bayesian Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 

8.4.2 Searching for the Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 

8.5 Current Trends in Computational Artifact Reconstruction . . . . . . . . . . . . . . . . . . . 237 

8.5.1 Going Beyond Geometry: Textures and Patterns on Sherd Surfaces . . . . . . . . . 238 

8.5.2 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 

8.5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 

9 Digital Reconstruction and Mosaicing of Cultural Artifacts 245 

Efthymia Tsamoura, Nikos Nikolaidis, Ioannis Pitas 

9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 

9.2 The Three-step Object Reconstruction Procedure . . . . . . . . . . . . . . . . . . . . . . 250 

9.2.1 Fragments Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 

9.2.2 Matching of Candidate Adjacent Fragments . . . . . . . . . . . . . . . . . . . . . 253 

9.2.3 Multi-fragment Merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 

9.3 Approaches for Object Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 

9.3.1 Torn by Hand Document Reassembly . . . . . . . . . . . . . . . . . . . . . . . . 255 

9.3.2 3D Objects Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 

9.3.3 2D Puzzles Reassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 

9.3.4 2D Objects Reassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 

9.4 Automatic Color Based Reassembly of Fragmented Images and Paintings . . . . . . . . . 259 

9.4.1 Discovery of Adjacent Image Fragments . . . . . . . . . . . . . . . . . . . . . . . 259 

9.4.2 Discovery of Matching Contour Segments of Adjacent Image Fragments . . . . . 260 

9.4.3 Contour Alignment of Fragments . . . . . . . . . . . . . . . . . . . . . . . . . . 262 

9.4.4 Overall Image Reassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 

9.4.5 Image Reassembly Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 

9.5 Reduced Complexity Image Mosaicing Utilizing Spanning Trees . . . . . . . . . . . . . . 266 

9.5.1 Two-Image Mosaicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 

9.5.2 Spanning Tree Mosaicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 

9.5.3 Sub-graph Spanning Tree Mosaicing . . . . . . . . . . . . . . . . . . . . . . . . . 270 

9.5.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 

9.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

10 Virtual Restoration of Antique Books and Photographs 281 

Filippo Stanco, Alfredo Restrepo Palacios, and Giovanni Ramponi 

10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 

10.1.1 Photographic Prints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 

10.1.2 Antique Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 

10.2 Detection of the Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 

xv

xvi 

10.2.1 Foxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 

10.2.2 Water Blotches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 

10.2.3 Cracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 

10.2.4 Fragmented Glass Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 

10.3 Virtual Restoration of Antique Photographic Prints Affected by Foxing and Water Blotches 293 

10.3.1 Inpainting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 

10.3.2 Additive/Multiplicative Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 

10.3.3 Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 

10.4 Restoration of the Fragmented Glass Plate Photographs . . . . . . . . . . . . . . . . . . . 296 

10.5 Restoration of Yellowing and Foxing in Antique Books . . . . . . . . . . . . . . . . . . . 299 

10.5.1 Foxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 

10.5.2 Page Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 

10.5.3 OCR in Antique Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 

10.6 On Image Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 

10.6.1 On the Measurement of Local Luminance and Local Contrast (Statistics of Location 

and Dispersion) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 

10.6.2 On the Relationship Between Local Contrast and Local Luminance . . . . . . . . 308 

10.6.3 Effect of γ-Correction on Scatter Plots of Local Contrast Versus Local Luminance 310 

10.6.4 Word Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 

10.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 

11 Advances in Automated Restoration of Archived Video 321 

Anil Kokaram, Francois Pitie, David Corrigan and Domenico Vitulano, Vittoria Bruni, Andrew Crawford 

11.1 Dirt and Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 

11.1.1 Simple Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 

11.1.2 Better Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 

11.1.3 Further Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 

11.2 Semi-transparent Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 

11.2.1 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 

11.3 Line Scratches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 

11.4 Global Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 

11.4.1 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 

11.5 An Evolving Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 

12 Applications of Spectral Imaging and Reproduction to Cultural Heritage 347 

Simone Bianco, Alessandro Colombo, Francesca Gasparini, Raimondo Schettini, and Silvia Zuffi 

12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 

12.2 Colorimetric and Multispectral Color Imaging . . . . . . . . . . . . . . . . . . . . . . . 349 

12.3 Capturing a Multispectral Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 

12.4 Imaging and Signal Processing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 353 

12.4.1 ’Narrow-band’ Multispectral Imaging . . . . . . . . . . . . . . . . . . . . . . . . 353 

12.4.2 ’Wide-band’ Multispectral Imaging . . . . . . . . . . . . . . . . . . . . . . . . . 354 

12.4.3 Training Set Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 

12.4.4 Filters Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

12.5 Recovery Multispectral Information from RGB Images . . . . . . . . . . . . . . . . . . . 359 

12.5.1 Spectral Based Color Imaging Using RGB Digital Still Cameras . . . . . . . . . . 359 

12.6 Storing a Multispectral Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 

12.7 Evaluating System Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 

12.8 Multispectral Image Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 

12.8.1 Colorimetric Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 

12.8.2 Spectral Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 

12.8.3 Spectral Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 

12.8.4 Viewpoint and Lighting Position Invariant Reproduction . . . . . . . . . . . . . . 366 

12.9 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 

13 Did Early Renaissance Painters Trace Optically Projected Images? The Conclusion of Independent 

Scientists, Art Historians and Artists 379 

David G. Stork, Jacob Collins, Marco Duarte, Yasuo Furuichi, Dave Kale, Ashutosh Kulkarni, M. Dirk 

Robinson, Christopher W. Tyler, Sara J. Schechner, and Nicholas C. Williams 

13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 

13.2 The Projection Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 

13.2.1 Philosophical and Logical Foundations of the Projection Theory . . . . . . . . . . 382 

13.3 Image Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 

13.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 

13.3.2 Lorenzo Lotto, Husband and wife (1543) . . . . . . . . . . . . . . . . . . . . . . . 387 

13.3.3 Jan van Eyck, Portrait of Giovanni (?) Arnolfini and his wife (1434) . . . . . . . . 389 

13.3.4 Jan van Eyck, Portrait of Niccolò Albergati (1431 & 1432) . . . . . . . . . . . . . . 391 

13.3.5 Robert Campin, The Mérode altarpiece (1430) . . . . . . . . . . . . . . . . . . . 392 

13.3.6 Georges de la Tour, Christ in the carpenter’s studio (1645) . . . . . . . . . . . . . 393 

13.3.7 Caravaggio, The calling of St. Matthew (1599–1600) . . . . . . . . . . . . . . . . 394 

13.3.8 Hans Memling, Flower still-life (c. 1490) . . . . . . . . . . . . . . . . . . . . . . 395 

13.3.9 Hans Holbein, The Ambassadors (1533) . . . . . . . . . . . . . . . . . . . . . . . 395 

13.3.10 Hans Holbein, George Gisze (1532) . . . . . . . . . . . . . . . . . . . . . . . . . 396 

13.4 Documentary Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 

13.5 Material Culture and Renactments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 

13.5.1 Reenactments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 

13.6 Non-Optical Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 

13.7 The “Value” in Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 

13.8 Scholarly Consensus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 

13.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 

14 A Computer Analysis of the Mirror in Hans Memling’s Virgin and Child and Maarten van 

Nieuwenhove 409 

Silvio Savarese, David G. Stork, Andrey Del Pozo, and Ron Spronk 

14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 

14.2 Memling’s Diptych . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 

14.3 Computer Vision Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 

14.4 Modeling Reflections Off a Mirror Surface . . . . . . . . . . . . . . . . . . . . . . . . . 416 

xvii

xviii 

14.4.1 Direct Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 

14.4.2 Inverse Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 

14.4.3 Experimental Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 

14.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 

14.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 

15 Digital Reproduction of Ancient Mosaics 431 

Sebastiano Battiato, Gianpiero Di Blasi, and Giovanni Gallo, Giovanni Puglisi 

15.1 Art and Computer Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 

15.2 History of Ancient Mosaics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 

15.3 The Digital Mosaic Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 

15.4 The Crystallization Mosaics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 

15.5 The Ancient Mosaics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 

15.6 The Ancient Mosaics in a 3D environment . . . . . . . . . . . . . . . . . . . . . . . . . 442 

15.7 Final Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 

15.7.1 Final Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 

16 Analysis of Ancient Mosaic Images for Dedicated Applications 453 

Lamia Benyoussef and Stéphane Derrode 

16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 

16.1.1 Some Historical Facts about Mosaics . . . . . . . . . . . . . . . . . . . . . . . . 454 

16.1.2 Mosaics, Images and Digital Applications . . . . . . . . . . . . . . . . . . . . . . 455 

16.2 Recent Image Processing Projects Concerned with Mosaics . . . . . . . . . . . . . . . . . 457 

16.2.1 St. Vitus Cathedral Mosaic Restoration . . . . . . . . . . . . . . . . . . . . . . . 458 

16.2.2 Arabo-moresque and Islamic Mosaic Pattern Classification . . . . . . . . . . . . . 458 

16.2.3 Roman Mosaics Indexation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 

16.3 Tesserae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 

16.4 Tessera-based Segmentation and Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 462 

16.4.1 Segmentation of Ancient Mosaic Images . . . . . . . . . . . . . . . . . . . . . . 464 

16.4.2 Tessera-based Coding and Lossy Compression . . . . . . . . . . . . . . . . . . . 464 

16.5 Guidelines Estimation for Mosaic Structure Retrieval . . . . . . . . . . . . . . . . . . . . 468 

16.6 Open Issues and Research Directions in Mosaic Image Analysis . . . . . . . . . . . . . . 470 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 

17 Copyright Protection of Digital Images of Cultural Heritage 479 

Vito Cappellini, Roberto Caldelli, Andrea Del Mastio, Francesca Uccheddu 

17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 

17.1.1 A Brief History of Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . 480 

17.1.2 Watermarking Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 

17.1.3 Watermarking Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 482 

17.2 2D Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 

17.2.1 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 

17.2.2 Insertion of Multiple Watermarking Codes . . . . . . . . . . . . . . . . . . . . . . 487 

17.2.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 

17.2.4 Blind and Non-blind Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

17.2.5 Private and Public Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 

17.2.6 Readable and Detectable Watermarks . . . . . . . . . . . . . . . . . . . . . . . . 489 

17.2.7 Invertibility and Quasi-invertibility . . . . . . . . . . . . . . . . . . . . . . . . . 490 

17.2.8 Reversibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 

17.2.9 Asymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 

17.2.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 

17.3 3D Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 

17.3.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 

17.3.2 3D Objects Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 

17.3.3 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 

17.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 

Index 515 

xix

16 

Analysis of Ancient Mosaic Images for Dedicated 

Applications 

Lamia Benyoussef and Stéphane Derrode 

Institut Fresnel (CNRS UMR 6133) 

École Centrale Marseille 


Email: lbenyoussef@ec-marseille.fr, sderrode@ec-marseille.fr 

CONTENTS 

16.1 Introduction . . ................................................. . . . . ....................... 453 

16.1.1 Some Historical Facts about Mosaics . . ............................................... 454 

16.1.2 Mosaics, Images and Digital Applications . . ........................................... 455 

16.2 Recent Image Processing Projects Concerned with Mosaics . . ................................... 457 

16.2.1 St. Vitus Cathedral Mosaic Restoration . . .............................................. 457 

16.2.2 Arabo-moresque and Islamic Mosaic Pattern Classification . . ............................ 458 

16.2.3 Roman Mosaics Indexation . . ................................................. . . . .... 459 

16.3 Tesserae Extraction . .................................................. . . . ................. 460 

16.4 Tessera-based Segmentation and Coding . . ................................................. . . 462 

16.4.1 Segmentation of Ancient Mosaic Images . . ............................................ 462 

16.4.2 Tessera-based Coding and Lossy Compression . . ....................................... 464 

16.5 Guidelines Estimation for Mosaic Structure Retrieval . . ........................................ 468 

16.6 Open Issues and Research Directions in Mosaic Image Analysis . . ............................... 470 

Bibliography . . ................................................. . . . ....................... 474 

16.1 Introduction 

Mosaic is a form of art to produce decorative images or patterns out of small components. It requires a great 

deal of time and labor and it is often very expensive. Mosaic is, in return, far more durable in time than 

painting, so it can be used in places where painting cannot be of practical use, such as floors. Mosaic moreover 

allows the realization of light effects that are impossible with other media. The success of the mosaic art form 

through the ages is at the origin of large collections in several museums, among them we can cite the “Great 

Palace Mosaic Museum” (Istanbul, Turkey) and the “Musée du Bardo” (Tunis, Tunisia). 

Recently, questions rise about the virtual conservation and management of collections and their accessibility 

to experts such as archeologists or art historians, and even to the large public. Requirements concern 

facilities 

• for cataloging collections and distant consultation, and for intelligent retrieval tools (e.g., the extraction 

of objects with a semantic meaning, such as animal or human, in a complex mosaic scene); 

453

454 Digital Imaging for Cultural Heritage Preservation 

• for virtual conservation (e.g., restoration of tesserae color), reconstruction (e.g., missing patches) and 

anastylosis of ancient mosaics. 

The aim of this Chapter is to draw an overview of image processing based methods dedicated to the specificities 

of mosaic images for applications to restoration and cataloging of ancient mosaics. 

16.1.1 Some Historical Facts about Mosaics 

The following description only intends to give some basis entries about history of mosaics. And so, it is quite 

superficial and partial. Interested readers should consult the numerous books covering the subject, among 

them we can cite the following books [11,16,26]. The worldwide web gives also access to very complete lists 

of bibliographical references on ancient mosaics (e.g., [44]). 

The earliest mosaics were installed as floors or pavements, occasionally as walls. Later, ceilings were also 

decorated. Originally, mosaics were used strictly in an architectural context. Mosaicking smaller, portable 

items such as panels or portraits is a later development, though there are a few rare instances of items such as 

icons from the late Byzantine era. The first mosaics that we know of are from what is now the Middle East. 

Temple columns in ancient Babylon (present day Iraq) had thousands of small clay cones pressed into wet 

plaster in decorative and geometric patterns. These date from five thousand years ago. From these humble 

beginnings, mosaic developed into a major art form. It was the Greeks, in the four centuries BC, who raised 

the pebble technique to an art form, with precise geometric patterns and detailed scenes of people and animals. 

By 200 BC, specially manufactured pieces “tessera” were being used to give extra detail and range of color to 

the work. Using small tesserae, sometimes only a few millimeters in size, meant that mosaics could imitate 

paintings. Many of the mosaics preserved at, for example, Pompeii were the work of Greek artists. The 

expansion of the Roman Empire took mosaics further afield, although the level of skill and artistry was diluted. 

Typically Roman subjects were scenes celebrating their gods, domestic themes and geometric designs. 

With the rise of the Byzantine Empire from the 5 th century onwards, centered on Byzantium (present day 

Istanbul, Turkey), the art form took on new characteristics. These included Eastern influences in style and 

the use of special glass tesserae called “smalti”, manufactured in northern Italy, made from thick sheets of 

colored glass. Smalti have a rough surface and contain tiny air bubbles. They are sometimes backed with 

reflective silver or gold leaf. Whereas Roman mosaics were mostly used as floors, the Byzantines specialized 

in covering walls and ceilings. The smalti were ungrouted, allowing light to reflect and refract within the 

glass. Also, they were set at slight angles to the wall, so that they caught the light in different ways. The 

gold tesserae sparkle as the viewer moves around within the building. Roman images were absorbed into the 

typical Christian themes of the Byzantine mosaics, although some work is decorative and some incorporates 

portraits of Emperors and Empresses. 

In the west of Europe, the Moors brought Islamic mosaic and tile art into the Iberian peninsula in the 8 th 

century, while elsewhere in the Muslim world, stone, glass and ceramic were all used in mosaics. In contrast 

to the figurative representations in Byzantine art, Islamic motifs are mainly geometric. Examples can be seen 

in Spain at the Great Mosque at Cordoba, the Alhambra Palace and Meknes in Morocco (Figure 16.4(b)). 

In Arabic countries a distinctive decorative style called “zillij” uses purpose-made ceramic shapes that are 

further worked by hand to allow them to tessellate (fit together perfectly to cover a surface). 

In the rest of Europe, mosaic went into decline throughout the Middle Ages, although some tiling patterns 

in abbeys, for example, used mosaic effects. In the 19 th century there was a revival of interest, particularly in 

the Byzantine style, with buildings such as Westminster Cathedral in London and Sacre-Cœur in Paris. The 

“Art Nouveau” movement also embraced mosaic art. In Barcelona, Antoni Gaudí worked with Josep Maria 

Jujol to produce the stunning ceramic mosaics of the Guell Park in the first two decades of the 20 th century. 

These used a technique known as trencadis in which tiles (purpose-made and waste tiles) covered surfaces of

Analysis of Ancient Mosaic Images for Dedicated Applications 455 

buildings. They also incorporated broken crockery and other found objects, a revolutionary idea in formal art 

and architecture. 

Mosaic still continue to interest artists but also crafts people because it is a very accessible, non-elitist 

form of creativity. The field is rich with new ideas and approaches, and organisations such as the British 

Association for Modern Mosaic (BAMM) [2] and the Society of American Mosaic Artists (SAMA) [1] exist 

to promote mosaic. As a recent example, we can cite the development of algorithms for computer-aided 

generation of mosaic images from a raster image, simulating ancient or modern styles [6]. 

16.1.2 Mosaics, Images and Digital Applications 

Mosaics are made of colored tiles, called tessera or tessella, usually formed in the shape of a cube of materials 

separated by a joint of mortar. The earliest known mosaic materials were small cones of clay pressed into 

wet plaster. Semi-precious stones such as lapis lazuli and onyx, as well as shells and terra cotta were also 

used. As the art developed, glass, ceramic and stone tesserae were the most common materials, along with 

pebbles. Modernly, any small singular component can be used: traditional materials, glass or ceramic cast or 

cut into tiles, plus plastic, polymer clay (such as Sculpey or Fimo), beads, buttons, bottle caps, pearls, etc. 

The physical study of materials used in mosaics continue to be an active research field [10, 17, 38]. 

Structure characteristics 

Mosaics can be built on different natural ground made of soil or rock, or on top of a previous pavement. 

The mosaic itself is composed of a variety of foundation or preparatory layers and a layer of tesserae. Also, 

the mosaic surface exhibits irregular hollows (tesserae) and bumps (mortar) through the scene. Hence, a 

mosaic can not be considered as a plane surface, as for paintings, but has numerous forms of irregularities, 

typical of the artwork style: 

• Shape of tesserae, The shapes of tesserae are irregular, from square shapes to polygonal ones. 

• Organization of tesserae, Tesserae are not positioned according to a regular lattice. On the contrary, the 

smart and judicious choice in the orientation, size and positioning of tesserae characterize the artwork 

style and exhibit the “general flow” (guidelines) of the mosaic chosen by the mosaicist. 

• Mortar joint, This positioning makes the joints appear as an irregular network with numerous interconnections 

throughout the mosaic. Network color intensity, mainly middle gray, is however not uniform 

through the scene. 

• Color of tesserae, Because of materials used and their oldness, tesserae of ancient mosaics are generally 

characterized by pastel colors, with low contrast. 

Oldness characteristics 

Other artifacts can be associated to the wear, erosion and oldness of ancient mosaics (some of them are 

illustrated by images in Figures 16.1 and 16.2): 

• Disaggregated tesserae, Some tesserae can display a loss of cohesion of their surfaces, disintegrated into 

powder or small grains. Also erosion and patinae deteriorations are commonly encountered in studying 

ancient mosaics. 

• Missing tessera patches, Most of mosaics are not completely preserved and, as a consequence of 

disaggregation, lacunas generally corrupted mosaic scenes. 

• Color alteration, Alteration of the mosaic surface characterized by a localized change in color (due to fire 

damage, graffiti, etc.).


FIGURE 16.1 

Excerpt of an ancient Greek mosaic scene (from Paphos, Cyprus), showing missing patches and geometric 

deformation due to slant acquisition [25], public domain image. 

(a) Disaggregated (b) Eroded (c) Altered in color 

FIGURE 16.2 

Three examples of deteriorated tesserae, from [34].


Data acquisition 

For latter processing and virtual anastylosis, mosaic scene must first be digitized using camera. All 

peculiarities cited above have a strong and complex impact on acquisition and the way mosaic scenes appear 

in an image with a limited resolution: 

• Relief shadows, During snapshot acquisition, the relief of tesserae generates shadows on mortar which 

does not appear nearly uniform in intensity all over the image, as it should be. 

• Indoor/outdoor acquisition, Previous item is further accentuated by indoor acquisition with flash. In 

outdoor acquisition, light is not controlled and one should expect severe contrast variations in acquisitions. 

• Geometric deformation, Another degradation comes from the snapshot acquisition angle, especially for 

very large mosaic scenes lying on the floor. This point produces perspective (rotation, scaling and shearing) 

deformations of tesserae shape in image and the thinning down of the mortar width (till disappearing). 

• Tessera resolution, The resolution of tesserae in an image (i.e., the number of pixels to describe a tessera) 

should not be too low for latter processing. On the other hand, a too high resolution will only able to 

capture partial scene. 

As a consequence, very large mosaic scenes have to be recorded using multiple overlapping images that 

are subsequently co-registered in geometry and color to produce the entire digital scene. An interesting 

complementary approach to camera acquisition is given by laser scanning. This technique, used recently for 

ancient mosaics analysis [23, 37], can produce a 3D mesh of the mosaic surface and material for a detailed 

shape analysis. 

Hence, dedicated processing methods are required to take into account the specificities of mosaic and 

the specificities of mosaic images, and to adopt image processing strategies suited to the tiling organisation 

of tesserae. This way, one can expect better performances than general purpose algorithms. The nature and 

the artifacts in mosaic images make segmentation methods based on pixel intensities inefficient (e.g., pixels 

associated to the mortar interfere with the classification process of tesserae). A strategy better suited to mosaic 

images is to consider tesserae as indivisible entities with an almost uniform gray-level value. 

The remaining of the Chapter starts in Section 16.2 with a brief description of several projects involving 

image processing methods for restoration, preservation and indexation of ancient mosaics. Then we present 

in details some fondamental applications of the tessera-oriented point-of-view on mosaic images proposed 

by the authors, according to the diagram in Figure 16.3. Section 16.3 presents an effective way to extract 

tesserae, while Section 16.4 exposes two direct applications of this strategy for mosaic image segmentation 

and coding. Section 16.5 presents an effective way to retrieve the main guidelines of a mosaic which can be 

helpful for delimitating semantic objects present in a complex scene. Finally, the conclusion in Section 16.6 

examines some open issues and suggests future research directions to succeed in providing appropriated tools 

to museums and experts. 

16.2 Recent Image Processing Projects Concerned with Mosaics 

A few project involving computer science and image processing for mosaic conservation, restoration or 

cataloging are reported in the literature. Here is a short description of them.


FIGURE 16.3 

Organization of Chapter with the processing pipeline. 

16.2.1 St. Vitus Cathedral Mosaic Restoration 

The more accomplished project concerns the restoration of the St. Vitus cathedral mosaics, in Prague (Czech 

Republic), reported in [49]. The Golden Gate (the southern entrance to the cathedral) is decorated with a 

unique work of art: a colored, richly gilded mosaic representing the Last Judgement, see Figure 16.4(a). In 

1992 the Office of the President of the Czech Republic and the Getty Conservation Institute [3] began to 

cooperate to restore and conserve the mosaic. 

Having the historical photo of the mosaic from the end of the 19 th century and the photos of the current 

state, authors study the evolution of the mosaic, which was several times reconstructed and conserved. Firstly 

they tried to restore the historical photograph - remove noise , deblur the image, increase the contrast. Then, 

they remove the geometrical difference between images by means of the multi-modal registration using 

mutual information. Finally, they identify mutual differences between the photos, which indicate the changes 

on the mosaic during the centuries. 

16.2.2 Arabo-moresque and Islamic Mosaic Pattern Classification 

Lot of attention has also been paid to islamic mosaics, of which artwork style is illustrated through Figure 

16.4(b). The particularities of such mosaics come from the periodicity and symmetry of tile patterns. 

A. Zarghili et al, [47, 48] propose a method to index an Arabo-Moresque decor database which is not 

based on symmetry. They use a supervised mosaicking technique to capture the whole principal geometric 

information (connected set of polygonal shapes, called “spine”) of a pattern. The spine is then described 

by using Fourier shape descriptors [35] to allow retrieving of images even under translation, rotation and 

scale. But, according to [14], the method cannot be automatized and does not allow the classification of these 

patterns according to any criterion. 

In [21], the authors propose image processing techniques to restore mosaic patterns. Image analysis 

tools are developed to obtain information about design patterns which are used to recover missing motifs or


(a) (b) 

FIGURE 16.4 

(a) Photo of the “Last Judgement” mosaic in the St. Vitus cathedral (Prague, Czech Republic), from [4]. (b) 

Islamic mosaic (Meknes, Morocco). 

tesserae. One difficulty was to propose methods robust to the discrepancies between equal object shapes (due 

to manual artwork, oldness, etc.). The symmetry, once recovered, allows virtual reconstruction by inpainting 

methods and physical restoration of damaged parts of mosaics. 

To study Islamic geometrical patterns, and all periodic patterns such as those encountered in textile 

patterns [41] or wallpapers, several works are based on the symmetry group theory. In [14], authors first 

classify patterns into one of the three following categories: (i) pattern generated by translation along one 

dimension, (ii) patterns which contain translational symmetries in two independent directions (refers to the 

seventeen wallpaper groups), and (iii) “rosettes” that describes patterns which begin at a central point and 

grow radially outward. For every pattern, authors extract the symmetry group and the fundamental region (i.e., 

a representative region in the image from which the whole image can be regenerated). Finally, they describe 

the fundamental region by a simple color histogram and build the feature vector which is a combination of the 

symmetry feature and histogram information. Authors shows promising experiments for either classification 

or indexing. 

In [15], authors exploit symmetry and auto-similarity of motifs to design a system to index Arabo- 

Moresque mosaic images based on the fractal dimension. Mosaics are first segmented automatically using 

color information. The classification decomposes the initial motif into a set of basic shapes. Contours of those 

shapes are then characterized by their fractal dimension which gives, according to the authors, a relevant 

measure of the geometric structure of the tile pattern. Some retrieval performances are also reported. 

16.2.3 Roman Mosaics Indexation 

Recently, two Content-Based Image Retrieval (CBIR) systems have been proposed to catalog and index 

Roman mosaic images. 

The first one, proposed in [32], details a complete CBIR system which include (i) object extraction from 

a complex mosaic scene by using unsupervised statistical segmentation and (ii) invariant description of 

semantic objects using the analytical Fourier-Mellin transform [13, 20]. Similarity between querying mosaic 

and the database is based on an index constructed from the invariant descriptors and an appropriate metric 

(Euclidean and Hausdorff).


(a) (b) 

FIGURE 16.5 

Excerpt of an ancient mosaic showing a wild boar (a) and a zoom on its hind legs (b). 

The second CBIR [27, 28] is a general system to index and retrieve by the content historic document 

images. While the object annotation in database images is done manually and off-line, the indexation is done 

automatically using an extended curvature scale space descriptor [33] suitable for concave and convex shapes. 

The query/retrieval of pertinent shapes from the database starts with a user drawing query (with a computer 

mouse or a pen) that is compared to entries in the database using a fuzzy similarity measure. The system 

integrates an XML Database conform to the MPEG7 standard, and experiments on large databases providing 

by the National Library of Tunisia and some Tunisian museums are reported. 

One key-point for such systems to be effective is to improve semantic objects extraction. Pixel-based 

methods, as the one used in [32], require heavy post-processing to detect shapes of interest from pixels classe. 

Recently, a new viewpoint on mosaic images, based directly on tesserae, have been proposed [8]. Once tessera 

extraction is achieved, this strategy facilitates basic steps toward CBIR applications, as described in the 

following. 

16.3 Tesserae Extraction 

A “natural way” to describe mosaics is to consider tesserae in their own instead of groups of isolated pixels, 

allowing to develop tessera-oriented processing. Indeed, a mosaic image can be seen as an irregular lattice 

in which a node is a tessera (Figure 16.5). The grid structure has been superposed to the zoomed image in 

Figure 16.5(b) to illustrate the complexity of the neighborhood system, both in the number of neighbors and 

in their orientation. To adopt this point of view on mosaic images, a robust method is required to extract 

tesserae from the network of mortar surrounding them. 

A way to consider this problem is to deal with the dual problem (i.e., the extraction of the network of 

mortar) which is close to recurrent problems encountered in several image processing applications (i.e., in 

medical imaging: vascular network segmentation from angiographies [12, 46], or in satellite imaging: road 

extraction in urban scenes [29, 30]). 

Several approaches have been proposed. Methods based on contour extraction are widely used and mainly


FIGURE 16.6 

Processing chain involved in tessera extraction, using gray-scale morphology tools. 

rely on the assumption that the network pixels and neighboring ones have different gray levels in order to 

compute gradients. But methods based on high-pass filter, such as Harris’s corner detector, highlight pixels 

belonging to the network, not connected components. Higher-level processings detect lines with varying 

widths [18, 39]. Strategies that track the entire network from a starting point [7, 45] are difficult to justify in 

the mosaic image case due to the high number of interconnections in a mosaic network graph. 

Numerous methods based on Markov modeling [19,40] or active contours [36,42] have also been proposed. 

These methods are quite efficient but time consuming. In the case of mosaics, these methods are not suited 

because of the high density of the network to be extracted in images. In [19], a Markov model is applied 

on a graph of adjacency crests, detected by a Watershed Transformation applied on a criterion image. This 

criterion image, computed from the original one, exhibits the potential of each pixel to belong to the network. 

A study of tessera extraction has been conducted in [8]. The solution adopted, and preferred over other 

experimented strategies, is based on gray-level morphology which is suited to the tiling organisation of 

mosaics. The Watershed Transformation (WT) approach [31] appeared interesting for mosaic images since 

this method is a good compromise between low-level methods (contour detection) and approaches by energy 

minimization (Markov model or active contours). But to work well, the WT needs to be computed on a 

criterion image (processed from the original one) that shows tesserae as catchment basins and the mortar 

network as watershed crests. 

The entire algorithm is sketched in Figure 16.6. The goal is to present to the WT a potential image that 

exhibits tesserae as catchment basins and the network as a crest crossing the entire image. The pre-processings 

are: (i) Contrast enhancement, based on top-hat and bottom-hat transforms; (ii) Potential criterion computation 

to exhibit the network as a crest; and (iii) Area closing [43] to reduce over-detection by WT. 

The extraction quality highly depends on the way mosaic images have been acquired. It should be taken 

not too far from the mosaic since individual tiles will not be visible: according to experiments, tesserae should 

appear with a resolution of not less than 10 × 10 pixels. Parameter h is a threshold used by the area closing


FIGURE 16.7 

Criterion image obtained from Figure 16.5. This processing improves the contrat between tessera and the 

network of mortar. 

operator [43]. Its value has an impact on the number of extracted tiles: a small value gives an over-detection 

whereas a big one produces an under-detection one. Hence, it should be (roughly) adjusted according to the 

mean size α of tesserae in the image (in number of pixels), assuming that α almost constant in a mosaic. 

Parameter h should be less than α 2 to avoid small tesserae to be deleted by the morphological operator. In 

experiments, value h = α 2 /2 gives good results. 

Figure 16.7 shows the criterion image obtained by applying the method to the wild boar image. As can be 

seen in this example, the network appears in dark. However tesserae are not uniform in texture and show local 

gray-level crests that should be deleted before WT in order to avoid over-segmentation. Following [19], an 

area closing [43] in first computed on the criterion image. This processing gives fewer minima while retaining 

crest locations as illustrated in Figure 16.8. The crest contours now represent correctly the network, which is 

confirmed by the zoom in Figure 16.9(a). To determine the width of the network (and not only a one-pixel 

skeleton as done by WT), which varies through the image, a simple threshold is applied on neighboring pixels 

of crests: a pixel is aggregated to the crest if its gray value is no more different by 10% of the skeleton mean 

gray value. The result of applying such a threshold can be observed in Figure 16.9(b). 

A second example of tessera extraction based on gray-level morphological processing in given in Figure 

16.10. 

16.4 Tessera-based Segmentation and Coding 

This Section describes two simple and direct applications of the tessera-oriented representation of ancient 

mosaic images: segmentation and coding. Especially, these applications preserve the irregularity of tiles shape 

and so the mosaic effect perception. From now, we assume that all tesserae in a mosaic image have been 

extracted successfully (i.e., one can access the coordinates of every pixel belonging to the contour of each tile 

in the mosaic). Each pixel that does not belong to a tessera is assumed to be a background pixel (i.e., a mortar 

network pixel).


(a) (b) 

FIGURE 16.8 

Extraction of tesserae from the criterion image in Figure 16.7, without (a) and with (b) area closing operator. 

(a) (b) 

FIGURE 16.9 

Result of tessera extraction on the zoom in Figure 16.5(a) in (a), and network/tesserae classification in (b).


(a) (b) 

FIGURE 16.10 

Illustration of the tessera extraction algorithm on the mosaic image of a Deer. 

16.4.1 Segmentation of Ancient Mosaic Images 

Instead of using all pixels from the tesserae, and since tesserae are almost homogeneous in color, classification 

can be performed directly on tesserae, not on pixels, by using some dedicated features. Tesserae can be 

characterized by several features, among them the mean and variance of gray-level values. Also one can take 

into account the number of pixels in the tessera, using a multi-features classification. All pixels belonging to 

the mortar network are directly associated to a unique class (they do not participate to the classification). The 

class color corresponds to the mean color of the mortar pixels. 

In Figure 16.11, a simple K-means algorithm based on the mean gray-level value of tesserae was sufficient 

to get an interesting segmentation. From a semantic point of view, this segmentation outperforms the result 

obtained by a classical pixel-based K-means strategy. A second example that confirms such interesting 

behavior is provided in Figure 16.12. Once again, the segmentation quality depends on the robustness of 

tessera extraction. 

16.4.2 Tessera-based Coding and Lossy Compression 

Another direct application of the WT tessera labeling is to develop a compact and efficient coding representation 

of ancient mosaic images [9]. This kind of module can be useful for rapid thumbnails transmission in 

case of distant catalog browsing application. The compression proposed is lossy but preserves the specificities 

of mosaic images, especially the irregularity of tesserae shape. In a way, the strategy described below is better 

suited to ancient mosaic images than a general purpose lossy compression algorithm, such asjpeg which is 

based on square-blocks. 

We still consider that all tesserae in a mosaic have been extracted successfully (i.e., we can access the 

coordinates of every pixel belonging to the contour of each tile in the mosaic). The network can be seen as 

the mosaic image background (i.e., each pixel that does not belong to a tessera is assumed to be a background 

pixel). Let I be a mosaic image with dimensions L × C, made of N colored tiles. The tessera-based 

lossy-compression specificity has been designed in order to take into account the two following observations: 

• A tile n, n ∈ [1, N], is usually formed in the shape of a cube of material, mostly with uniform color.


(a) Pixel-based K-means segmentation (b) Tile-based K-means segmentation 

FIGURE 16.11 

Segmentation comparison of the mosaic image in Figure 16.5(a) with a pixel-based strategy (a) and the 

tessera-based strategy presented in Section 16.3 (b). Both pixel- and tile-based segmentations make use of an 

unsupervised K-means classification algorithm, the first one with 3 classes, and the second one only with 2 

classes since the mortar is already extracted. 

Hence a vector of three intensity colors (Rn, Gn, Bn) is enough to describe the color of tessera n. This 

color can be easily estimated by computing the mean intensity of pixels belonging to the tessera. 

• The mortar intensity, mainly middle gray, is not uniform throughout the image because of shadows due to 

non-flat mosaic surfaces and snapshot acquisition angle. However, the network intensity should appear 

the same everywhere in the image. Hence, all pixels belonging to the mortar network is coded by the 

same color vector (Rb, Gb, Bb), b stands for background. This value can be estimated by computing 

the mean intensity of network. 

Coding file structure 

Thecms (“Compressed MoSaic”) format is structured as follows (see Figure 16.13) : 

• Header: The header is made of 

1. The original image size in pixels (2 × 2 bytes). 

2. The number N of tesserae in the image (2 bytes). 

3. The mean color values (Rb, Gb, Bb) of the network (3 bytes). 

Hence, nine bytes are necessary to code the header. Other information such as the name of the original 

image or a comment can be added. 

• Body: each tessera n has the following code structure 

1. Tessera header contains the number of pixels Nn of the contour (1 byte), and the mean color values 

(Rn, Gn, Bn) of the tessera (3 bytes).


(a) (b) (c) 

FIGURE 16.12 

K-means segmentation of a mosaic representing a bird (a) with the pixel-based (b) and the tile-based (c) 

strategies. 

FIGURE 16.13 

Sketch of thecms file format to compress images of ancient mosaics (without Huffman coding). 

2. Tessera body codes the closed-contour using the coordinates (l1,c1) of the first pixel (4 bytes) and 

Freeman chain code (or whatever code) for the subsequent Nn − 1 pixels. If no Huffman coding 

technic is applied, only 3 bits per contour pixels are necessary. 

(Nn−1) 3 

Counting the number of bytes necessary to code a tessera n gives: 8 + rounded to the ceil 

8 

value. The last byte is filled in to start the next tessera coding on a new byte. Hence, 21 bytes are needed 

to code a tessera with Nn = 34. 

Mosaic image reconstruction from the compressed file is done by reconstructing the image background 

using the network color intensity, on which each tessera with its own color is superposed. The order tesserae 

are saved or read in the body of thecms file has no influence on the reconstruction quality, but can be changed 

for a desired application. For example, one can think organizing tesserae by grouping tiles in the file according 

to the semantic object they belong to (e.g., human, animal, etc.). Hence reconstruction will progressively show 

the different objects present in the mosaic scene. Tesserae dedicated to the background, mainly of uniform 

color as illustrated in Figures 16.5(a), 16.10(a) and 16.12(a), can be saved at the end of the file since their 

semantic meaning is, in general, less interesting than the one from objects tesserae. Specific organization 

of tesserae within the file can also be of interest for mosaic database indexing and text- or content-based 

retrieval, see Section 16.6. Hence, we get a compact and easy-to-handle lossy-compressed representation of 

mosaic image. The compression of a mosaic image and its reconstruction from acms file are summed up in 

Algorithms 1 and 2. 

Experimental results 

To evaluate the compression algorithm, we process on the Deer mosaic image in Figure 16.10(a). The 

decoding result is shown in Figure 16.14 and must to be compared with the original image. Pixels attributed


Algorithm 1cms format coding. 

Require: Mosaic image I. 

Ensure: Tessera-oriented coding of I in filef. 

Tessera extraction (cf. Section 16.3). 

for n = 1, . . . , N do 

Write the number of contour pixels Nn ←f. 

Write the mean color value Vn ←f. 

Write the first contour pixel coordinates ln, cn ←f. 

for i = 2, . . . , Nn do 

Compute chain code direction for pixel i. 

Write direction ←f. 

end for 


Algorithm 2cms format decoding. 

Require: Compressed filef. 

Ensure: Tessera-oriented decoding of filefinto image I. 

Read file header → L, C, N, Rb, Gb, Bb. 

Create an image I with dimensions L × C and background color (Rb, Gb, Bb). 

for n = 1, . . . , N do 

Read tessera header → Nn, Rn, Gn, Bn, ln, cn. 

Set pixel (ln, cn) with color (Rn, Gn, Bn). 

for i = 2, . . . , Nn do 

Compute coordinates (li, ci). 

Set pixel (li, ci) with color (Rn, Gn, Bn). 


Fill in the closed contour with color (Rn, Gn, Bn). 


FIGURE 16.14 

Result of coding/decoding of the Deer image in Figure 16.10(a).


to the network represent 27.5% of the total number of pixels in the image (255 × 394). The number of 

tesserae detected is 314. The image produces a cms file size of 8766 bytes, which gives a compression 

factor of about 11.5 with respect to a raw coding of pixels (e.g.,ppm file format). The compression ratio 

depends on the mean tessera size in the mosaic image. The bigger the tiles, the higher the compression ratio 

is expected to be. 

From a qualitative point of view, some remarks can be pointed out. As expected the shape of the network 

and tesserae is kept very similar to the original ones. The mean colors used to describe network and tesserae 

intensities makes the decoded image appears pastel with respect to the original one, since artifacts such as 

shadow or dirties are cleaned. Nevertheless the global aspect of the mosaic is respected and of sufficient 

quality to understand the scene content. Lacunas and missing patches are not taken into account by the actual 

algorithm. It considers holes as tesserae but should better associate them to the mortar network since we 

observe the preparatory layer mortar appears behind. One solution is to fuse the network with tesserae that 

show mean color close to the mean network color. 

16.5 Guidelines Estimation for Mosaic Structure Retrieval 

Ancient mosaicists avoided to align their tiles according to rectangular grids. Indeed, such grids emphasize 

only horizontal and vertical lines and may distract the observer from seeing the overall picture. Hence, they 

placed tiles in order to emphasis the strong edges of the subject to be represented, influencing the overall 

perception of the mosaic [5]. Hence, organization and positioning of tesserae are interesting information for 

experts since they emphasize the main directional guidelines chosen by the artist (characteristic of the style). 

This information is of crucial interest for mosaic dedicated applications such as content-based retrieval of 

semantic elements or region-based mosaic image coding. 

To get directional guidelines, one can first think using the principal axes of an ellipse-equivalent shape of 

each tessera, using well-known formulae based on geometrical moments (minor and majors axes). However, 

ancient mosaic tesserae are not box- neither regular-shaped and principal axes quickly appear not enough 

robust. One major drawback of such method is that it does not take into account information of neighboring 

tesserae, which is of great importance for regularization and for recovering the main guidelines that emphasize 

the “general flow” of a mosaic. 

Hence, an energy-based contextual algorithm for retrieving main directional guidelines in a mosaic has 

been proposed [8]. The energy to be minimized is constructed by using two key-features: the mean-gray value 

and borders directions of each tessera. The optimization is done either by gradient descent or by simulated 

annealing. 

Methodology 

Each tessera n is represented by 

• Its barycenter (xn, yn) computed on the support Ωn of n. 

• The list of its neighboring tesserae: Vn = {vn,1, . . . , vn,Tn}. A neighbor is a tessera that shares at 

least one pixel of mortar network with n. 

It should be noted that the number of neighbors Tn is different from one tile to the other since tesserae are not 

organized according to a regular grid. Each tessera n is characterized by an energy of configuration which 

links itself to each of its neighbor vn,t ∈ Vn. This energy, denoted by En,t with t ∈ [1, . . . , Tn], is the 

sum of two complementary terms:


FIGURE 16.15 

Plot of the regularized and normalized histogram of the contour orientation of one tile from the boar mosaic 

image in Figure 16.5(a). 

• The first term Q is based on the mean-gray value of tesserae. It is proportional to the sum of the difference 

of gray-level means (1) between n and vn,t, and (2) between n and the symmetrical tessera of vn,t with 

respect to n. This feature favors alinement of tesserae with low contrast, which is a characteristic of 

directional guidelines. 

• The second term R is based on the orientation of tessera contours. We compute the histogram of the 

orientation of segments constituting the contour of tessera n. This histogram is regularized using a 

Gaussian kernel, result of which is illustrated in Figure 16.15. It should be noted that the two modes of 

the histogram, at approximatively 90 0 each other, correspond to the two ambiguous orthogonal main 

directions of a square-shaped tile. It is then possible to estimate the p.d.f. at angle αn,t given by the 

barycenter of n and the one of vn,t. 

Terms Q and R are normalized to belong to range [0, 1]. We can then initialize the “main direction” of a tile 

(i.e., the direction of the neighboring tessera which gives the highest Q + R value): 

tn,max = arg max 

t∈[1,...,Tn] En,t 

(16.1) 

The energy of a tessera is then defined as Cn = (2 − En,tn,max) + λ Vn, with λ a weighting factor 

set manually. Term Vn is defined as 

Vn = 1 

Tn 

Tn� � 

t=1 

� αn,tn,max 

� 

− � αt,tt,max 

(16.2) 

which is the normalized sum of absolute difference between the main direction of tile n and the main direction 

of its neighbor t. 

The next step to to minimize the mosaic energy, defined as the sum of Cn for all tesserae in the mosaic. 

This is done by selecting the tessera n which gives the highest value for Vn. To reduce the contribution of 

this tessera, we try another main direction and recompute the mosaic energy. At that point, two strategies 

have been tested:


• Deterministic framework (Gradient Descent): if the mosaic energy reduces then the new main direction is 

validated, otherwise another main direction is tested. When all directions for this tile have been tested, we 

repeat the process for the next tile with high Vn value. 

• Stochastic framework (Simulated Annealing): a configuration which gives a higher mosaic energy can 

be validated according to the simulated annealing principle [24]. This strategy allows to search for the 

global minimum, which can not be reached with previous strategy since the mosaic energy function is not 

convex. 

The process is iterated until the mosaic energy is almost constant. The cartography of tessera orientation is 

made of the main direction of each tile at the last iteration. 

Figure 16.16 illustrates the application of the tessera orientation methodology on the detail of the boar 

image in Figure 16.5(a). From the initial configuration of tesserae (a) we get the final configuration (b) using 

Simulated Annealing (SA) for optimization. Figure 16.16(c) shows the evolution of the computed energy 

during iterations of both gradient descent (GD) algorithm and simulated annealing one. As expected, SA 

reaches a lower minimum than GD but to the detriment of numerous additional iterations (approximately 150 

for SA versus 50 for GD). Indeed, GD searches for a local minimum and is highly dependent on the initial 

configuration, whereas SA is expected to reach the global minimum due to its stochastic nature. 

The tessera cartography obtained with SA optimization is really satisfying when visually compared to the 

main directional guidelines of the mosaic. This is especially true for regions at the borders between classes. 

A second example of cartography is proposed in Figure 16.17, after both simulated annealing and gradient 

descent optimizations. Results are very similar except for some guidelines where SA gives a better result. 

Once again, the tessera orientation estimation methodology, which makes use of contextual information, gives 

regularized results that emphasize the mosaic guidelines. Nevertheless, confusions can be found on areas 

with homogeneous colors and where tesserae are square-like shaped. Indeed, for those kinds of tesserae two 

orthogonal directions are equally probable, which generally gives ambiguous results. However, these areas of 

uniform color are of limited interest for object-based scene applications, such as mosaic pattern recognition. 

Finally, Figure 16.18 shows a failure case in tessera orientation estimation. Indeed, the result will not allow 

to find the main directional guidelines in the mosaic, that can be more easily observed in Figure 16.12(a). The 

main reason comes from an over-detection of tesserae from the extraction step. This behavior is observed on 

mosaics built with tesserae of different sizes (e.g. large tesserae for the background and small ones for objects 

or details). Hence the shape of extracted tiles does not correspond to the shape of tesserae and the orientation 

is corrupted, showing no particular guideline in the mosaic. 

16.6 Open Issues and Research Directions in Mosaic Image Analysis 

In this Chapter, an overview of image processing methods for images of ancient mosaics has been proposed. 

The analysis of such images is a difficult task due to many reasons, among them the irregularity of shapes, the 

particular organization of tesserae and the degradation of tiles due to their oldness. Also, the way snapshot 

acquisition is performed has a crucial impact on the subsequent processings. 

A few works only are reported in the literature regarding the numerous problems that experts (museum 

curators, archeologists, art historians, etc.) face about possibly virtual restoration/lifting, conservation and 

cataloging of ancient mosaics. Each artwork style and period requires the development of dedicated processing 

methods to take into account a priori information of materials, shape, color, etc. An interesting point of view


(a) Configuration at initialization (b) Configuration after SA 

(c) Energy evolution for GD and SA 

FIGURE 16.16 

Cartography of tesserae orientation. Each tessera is characterized by its center of mass (circle) and its 

orientation (segment crossing the circle).


(a) Configuration after SA (b) Configuration after GD 

FIGURE 16.17 

Another example of tesserae orientation cartography with (a) simulated annealing optimization and (b) 

gradient descent optimization. 

FIGURE 16.18 

Result of guidelines detection for the “bird” mosaic in Figure 16.12(a).


FIGURE 16.19 

Example of querying a distant mosaic catalog by keyword. 

on the problem is to consider, within the processings, that a mosaic is a collection of tiles and not only a series 

of image pixels. If this tessera-oriented strategy seems suited in numerous cases, improvement regarding the 

robustness of tesserae extraction are still required, especially when mosaic tiles show a large difference in 

size and a high level of degradation. 

The algorithms described within Sections 16.3 to 16.5 are only a few basic, but crucial, steps toward 

complete applications expected by experts such as (i) ancient mosaic restoration with virtual “inpainting” 

tools taking into account main guidelines and tesserae shapes, and (ii) multi-site and distant catalog browsing 

with “intelligent” retrieval capabilities of semantic objects. A simplehtml interface has been designed to 

illustrate the possible use of basic modules for browsing, in situ, a distant database of Roman-style mosaics. A 

simple query using textual keywords like “human”, “face” or “animal” allows to visualize the corresponding 

mosaics in a distant database. Figure 16.19 shows an example of this application with the word “bird” as 

query. Of course, one goal of the project is to replace keywords by self-content indexing using semantic 

objects extraction and characterization, based on segmentation, main guidelines estimation and connected 

components labeling algorithms [22]. The mosaic coding strategy exposed above can be used to construct a 

tile-oriented CBIR application. If the CBIR can work directly on the coded stream (without reconstruction), 

one can expect to reduce drastically the RAM and/or hard disk memory requirements. 

The problem of quality of processings for segmentation and lossy coding has not been addressed in this 

Chapter. Particular attention should be paid to this issue in order to quantify the effectiveness of the approach. 

To that goal, specific quality metrics for tesserae shape and color have to be defined with experts and evaluated 

against image resolution. Cumulative error impact should then be analyzed along the complete processing 

chain to evaluate the level of accuracy for the targeted application. Note that fully automatic tools are not 

always required and applications piloted by users can be of interest. 

An interesting open issue is the construction of an invariant representation of semantic mosaic objects 

which is robust to the tiling organisation of shapes. Such a module will help CBIR systems to retrieve mosaic


objects independently of their orientation, pose and size in the scenes. Dedicated registration algorithms 

to reconstruct a complete mosaic scene from a set of sub-images can also be of interest in case of in situ 

acquisition of a large mosaic for example. 

Finally, one very promising research direction is to develop new image processing methods to automatically 

learn ancient mosaicists’ way of working and style (e.g., tessera color and size, organisation, guidelines, 

etc.). These tools could first be used to virtually fill in missing patches or restore deteriorated patches in 

a mosaic scene, using tessera-dedicated inpainting methods. They could also be exploited for emulating a 

mosaic artwork style from a raster input image using computer-aided algorithms, cf. [6] for an overview of 

digital mosaic creation. 

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Index 

γ-Correction, 310 

2D, 5, 47, 51, 58, 63, 65, 74, 75, 79, 80, 86, 87, 92, 

95, 117, 121, 128, 129, 151, 153, 193, 220, 

222–225, 229, 230, 232, 234–236, 238, 240, 

246–248, 250, 253, 256, 258, 259, 268, 271, 

275, 338, 480, 485, 506, 508 

Objects reassembly, 258 

Puzzles reassembly, 258 

3D, 2, 5, 20, 27, 40, 41, 45, 46, 52, 53, 58, 73, 74, 79, 

95, 106, 109, 116, 121, 123, 124, 128, 130, 

139, 142, 149, 151, 153, 164, 165, 169, 174, 

175, 181, 189, 193, 201, 216, 225, 238, 246, 

275, 289, 329, 336, 366, 368, 416, 432, 442, 

457, 480, 499, 506 

Acquisition, 106, 107 

Catalogs, 45 

Investigation and diagnostics, 59 

Modeling, 2, 5, 24, 40, 52, 53, 74, 80, 96, 123, 

126 

Objects reconstruction, 255 

Puzzle Solving, 234 

Scanner, 128, 129 

Scanning pipeline, 109, 111, 113 

Scanning technologies, 106 

Visualization, 124 

Watermarking, 496 

Web, 127 

Aberration 

Astigmatism, 389 

Coma, 389 

Off-axis, 388 

Spherical, 383 

Additional parameters, 82, 85 

Additive/Multiplicative Model, 295 

Alvarez extinction theory, 385 

Anamorphic, 395, 396, 419 

Antique book, 284, 285, 299, 304 

Arc de Triomphe, 181 

ARC3D, 107, 164–166, 169, 173, 175, 181 

Archaeology, 1–5, 27, 253 

Archeomatica, 5, 21, 22, 25 

Area closing, 461 

Artificial Lighting, 158, 195, 205 

Asclepius, 22 

Asymmetry, 230, 491 

Authentication, 482, 483, 485, 488, 504 

Automatic reproduction, 52 

Betacam Dropout, 322, 323 

Blender, 6, 21, 25, 26, 43, 123, 181 

Blind techniques, 489, 490, 508 

Blotch, 282, 283, 288, 294–296, 322, 324, 325, 327– 

332, 336, 338 

Water, 282–285, 287, 288, 293, 297 

Bobeche, see Candelier389 

Bosch, Hieronymus, 410 

BRDF, 42, 92, 113, 120, 121, 138–144, 152, 153, 156, 

159, 366–369 

Break point, 253, 255 

Bruegel, Pieter, 410 

BTF, 367 

Bundle adjustment, 79, 82, 85, 87 

Cabanel, Alexandre, 386 

Camera 

Calibration, 79, 80, 82, 83, 85–87, 140, 196, 198 

Lucida, 386 

Campin, Robert, 383, 392, 398, 410 

Capacity, 95, 217, 480, 483, 485–487, 489, 503, 505 

Caravaggio, 394, 410 

Carracci, Annibale, 392 

CBIR, 459, 473 

CCD, 81, 82, 106, 289, 338, 348, 352, 368 

Chandelier, 384 

Candle holder, 389 

Perspective, 384 

515


Chiaroscuro, 400 

Christus, Petrus, 410 

CIE, 348, 350 

Closing by reconstruction, 288 

CMOS, 81, 82 

Color, 289 

Apparent, 112, 116, 120 

Filtering, 303 

Mapping, 65, 110, 112, 117, 118, 121 

Resampling, 120 

Sampling, 107, 116, 119 

Computer graphics, 2, 4, 5, 28, 74, 92, 120, 138, 223, 

366, 394, 410, 414, 415, 431, 434, 438, 496 

Computer-generated animations, 41 

Contrast, 198, 202, 282, 289, 306, 329, 331, 333, 455, 

458, 461, 469, 488 

Copyright, v, 46, 53, 479, 485 

Cormon, Fernand, 386 

Crack, 60, 62, 191, 240, 282, 283, 285, 288 

Cranach, Lucas, 387 

Crocket, 384 

Cultural artifacts, 245, 275 

Dürer, Albrecht, 392 

Data preservation, 45 

Decentering distortion, 83 

Depth of field, 176, 388, 395, 401, 412 

Detection, 79, 113, 114, 122, 198, 200, 205, 207, 210, 

232, 285, 287–289, 291–293, 295, 302, 314, 

322, 324, 326, 327, 329, 333, 441, 461, 470, 

482, 489–491, 498, 502, 506 

Dinanderie, 384 

Dirt, 191–193, 232, 321–324, 327, 338, 340, 341, 388, 

468 

Disparity, 173 

Documents reassembly, 255 

Donatello, 111, 400 

DTM/DSM, 76, 87, 89 

Dunhuang, 175 

Eroded Rock, 192, 207, 208 

Exterior parameters, 82 

Eyck, Jan van, 383–385, 387, 389–392, 397–401, 410, 

412, 414, 416 

Fabriano, Gentile da, 387, 397 

Film tear, 322, 323 

Fingerprinting, 482–484 

Flash, 120, 322, 457 

Flicker, 322, 336, 341 

Foxing, 282–285, 287, 293–295, 299, 300, 302–305 

Fracture, 55, 234, 235, 238–240, 246, 248, 250, 251, 

253, 255–257, 283, 284, 298 

Fragment, 4, 10, 12, 13, 21, 22, 55, 57, 106, 108, 216, 

218–220, 224, 225, 227, 231–235, 237, 238, 

245, 247, 250, 251, 253, 255, 259, 260, 263, 

266, 285, 292, 296, 432 

Fromentin, Eugène, 386 

Furtenagel, Laux, 410 

Fusion, 77, 78, 89, 201, 291, 329 

Gabor Filter Bank, 289, 291 

Geographic Web browsers, 45 

Geometric triangulation, 106 

GIS, 74, 93 

Glass plate, 283, 292, 296 

Gradient Vector, 238, 294, 442 

Graph Cuts, 257, 331, 441, 442 

Grid construction, 386 

Haghia Triada, 6 

Handwriting Recognition, 205 

Hartrick, A. S., 386 

Hidden notations, 485 

Histogram, 206, 260, 285, 303, 307, 339, 459, 469, 

488 

Hockney, David, 380, 410 

Holbein, Hans, 395, 396 

Hole filling, 110, 115 

Hot spots, 127 

Hypermedia, 127 

Image 

Matching, 76, 87, 89 

Quality, 306, 313, 314 

Image-based rendering, 121, 152, 248 

Image-to-geometry registration, 92, 117, 119 

Ingres, Jean Auguste Dominiques, 386 

Inpainting, 116, 253, 294, 336, 459 

Interactive visualization, 43, 92, 112, 202, 369 

Interior parameters, 82 

Interpolation, 111, 125, 156, 294–296, 310, 322, 334, 

335, 338, 341, 366 

Invertibility, 481, 490

Copyright Protection of Digital Images of CH 517 

Iron, 283, 285 

Iterative Closest Point, 76, 149, 254, 262 

Kemp, Martin, 401 

Kinescope Moire, 322 

Laser scanner, 3, 5, 21, 24, 25, 45, 75, 200, 250, 368 

Laser scanner, 193, 221 

Least squares matching, 89 

Light field rendering, 121 

Lighting analysis, 394 

Local contrast, 307, 310 

Local luminance, 307, 310 

LOD, 77, 93 

Long term preservation, 130 

Lotto, Lorenzo, 387–389 

Mahl stick, 393 

Mantegna, 387 

Mapping photos on 3D models, 119 

Melozzo da Forlì, 387 

Memling, Hans, 395, 410, 413 

Mesh, 40, 65, 90, 110, 112, 116, 123, 124, 139, 150, 

165, 193, 200, 220, 457, 496, 501 

MeshLab, 60, 62, 64, 65, 116, 121, 123, 124, 164, 

165, 181, 201 

Metadata, 46, 127, 129, 141 

Metsys, Quentin, 410 

Michelangelo, 43, 65, 117, 400 

Microcrack, 288, 289 

Mirror 

Concave, 388, 398 

Convex, 384, 399 

Modelviewer, 165 

Mogao caves, 175 

Monet, Claude, 401 

Moroni, Giovanni Battista, 387 

Mosaic, 246, 247, 282, 292, 348, 431–435, 437–442, 

446–449, 453–462, 464–470, 472–474 

Acquisition, 457 

Ancient, 432–434, 438–445, 448, 454, 455, 457, 

460, 462, 464, 466, 468, 470, 473, 474 

Byzantine, 454 

Coding, 464, 465 

Deformation, 457 

Digital, 432, 434 

Greek, 454 

Guidelines, 468 

Inpainting, 459 

Islamic, 454 

Material, 455 

Medieval, 458 

Photo-mosaic, 434, 448 

Puzzle Image Mosaic, 434 

Resolution, 457 

Restoration, 458 

Roman, 454, 459 

Segmentation, 464 

Symmetry, 459 

Zillij, 454 

Mosaicing, 248, 266, 268–272, 274, 275, 352, 368 

Spanning tree, 269 

Sub-graph spanning tree, 270 

Multi-image matching, 89 

Multimedia, 3, 5, 24, 41, 50, 58, 124, 127, 480, 485, 

496 

Multiresolution, 44, 65, 75, 77, 110, 112, 119, 124, 

138, 140, 151, 153, 502 

Narrow-band, 350, 353 

Neugebauer, 365 

Nieuwenhove, Maarten van, 410, 412–414 

Noise, 76, 85, 110, 125, 129, 155, 165, 176, 206, 223, 

230, 260, 307, 322, 326, 335, 340, 352, 355, 

360, 439, 458, 488, 497 

Non-blind techniques, 489, 490, 502 

Non-falsifiability, 383 

Non-Photorealistic Rendering, 431 

Non-pictorial matching, 253 

OCR, 205, 210, 299, 300, 304–306 

Opening by reconstruction, 288 

Optical style in painting, 382, 400 

Opus 

Musivum, 438, 439 

Palladium, 442 

Vermiculatum, 438, 439 

Ownership of the rights, 482 

Parmigianino, 410 

Parthenon, 3, 21, 41, 138, 157 

Pathological Motion, 327 

PCA, 143, 355, 357, 361, 362 

Perspective


Analysis, 395, 396, 412 

Error, 383 

Photogrammetry, 45, 52, 55, 74, 79, 95, 111, 140, 

142, 250, 384 

Photographic Prints, 282, 285, 293 

Pictorial matching, 253 

Pinprick hole evidence, 392 

Pisanello, 387 

Poisson Reconstruction, 181 

Polizzello Mountain, 6 

Porta, Giambattista della, 389, 398 

Portaels, Jean-François, 386 

Private techniques, 489 

Public techniques, 489 

Queuing system, 172 

Radial distortion, 83 

Range map, 108, 109, 111, 113–115, 117, 123–125, 

368 

Alignment, 109, 111, 113 

Automatic alignment, 113 

Global registration, 111 

Local registration, 111 

Merge, 110, 112 

Reconstruction, 112 

Reconstruction from, 110 

Raphael, 387 

Rapid prototyping, 52 

Rational filter, 288, 302, 303 

Re-ordering, 498 

Re-triangulation, 497 

Reconstruction pipeline, 169 

Red chrominance matrix, 285, 287 

Reducing compass, 385, 392 

Refinement, 326, 327, 385, 499 

Relative alignment, 238, 257, 263, 264 

Relative offset evidence, 391 

Rembrandt, 400 

Remeshing, 497, 499, 501, 506, 507 

Rendering, 6, 40, 42, 47, 55, 77, 92, 111, 117, 124, 

125, 138, 140, 144, 147, 152, 156, 159, 193, 

200, 246, 365, 368, 394, 431, 439, 496 

Restoration, 3, 5, 12, 20, 22, 25, 40, 43, 46, 57–60, 

62, 63, 65, 74, 78, 93, 95, 117, 138, 218, 

246, 248, 274, 282, 284, 289, 292–296, 299, 

300, 302, 303, 306, 314, 321, 322, 325, 327, 

329, 331, 335, 338, 340, 348, 368, 369, 454, 

457, 459 

Reversibility, 491 

Reynolds, Joshua, 397 

Robustness, 116, 169, 223, 231, 237, 309, 350, 464, 

479, 485, 487, 502 

Rock Art, 193 

Rock art, 191, 192, 194, 198, 200, 202, 205, 208 

ROD, 324, 325 

Roman de la Rose, 398 

Roto-translation, 65, 114, 296 

Sampling, 64, 77, 90, 106, 108, 116, 123, 129, 143, 

156, 171, 349, 350, 354, 361, 436, 499 

Scaling, 65, 85, 141, 172, 198, 248, 266, 385, 457, 

488, 497 

Scratch, 333 

SDI, 324 

Segmentation, 127, 231, 235, 257, 288, 436, 448, 457, 

459, 460, 462, 464–466, 473 

Self-calibration, 173 

Sepia, 285, 288, 289 

Sfumato, 400 

Shadow, 87, 116, 139, 152, 181, 193, 250, 367, 393, 

394, 412, 457, 465 

Shake, 322, 336, 341 

Similarity, 48, 89, 117, 119, 200, 223, 250, 255, 261, 

432, 440, 459, 499 

Simplification, 77, 93, 110, 123, 124, 129, 164, 230, 

497, 506 

simplification, 499 

Smith Waterman algorithm, 261 

Spanning tree, 266, 269–272, 274 

Sparkle, 322–324, 433, 454 

Spectacle, 381, 382, 399, 400 

Spectral, 77, 80, 92, 140, 146, 156, 300, 348–350, 

353–366, 368, 369, 506 

Stelae, 183 

Structure from motion, 80, 82 

Structure-from-motion, 164 

Surface reflection properties, 112, 116, 120, 128 

Surveying, 74–76, 78, 80, 93, 200, 382 

Tessera, 454, 455, 457, 460–466, 468–471, 473, 474 

Altered, 456 

Disaggregated, 456 

Extraction, 460

Copyright Protection of Digital Images of CH 519 

Smalti, 454 

Wear, 456 

Texture, 63, 74, 75, 80, 112, 116, 119–121, 123, 139, 

150, 153–155, 175, 176, 183, 193, 195, 199, 

238, 250, 253, 283, 288, 300, 312, 322, 335, 

367, 397, 419, 446, 462, 496, 499, 505, 508 

Thresholding, 206, 287, 288, 292, 293, 303, 334 

Thysia, 25 

TOF, 106, 107, 110, 125 

Tonal adjustment, 303, 304 

Tour, Georges de la, 393 

Tracing theory 

Burden of proof, 386 

Direct tracing, 381 

Documentary evidence, 382 

Ghent symposium, 381, 401 

Indirect influence, 381, 402 

Philosophical foundations, 382 

Revisions, 385 

Timing, 387 

Trade secrets, 387, 398 

Triangulation, 21, 24, 52, 53, 75, 85, 106, 119, 124, 

138, 167, 193, 208, 220 

UAV, 81, 82, 95 

Upload tool, 165 

Usability, 40, 44, 75, 76, 90, 126, 282 

UV, 58, 63, 77, 299 

Velàzquez, Diego, 392 

Vermeer, Jan, 382, 412 

Vinegar Syndrome, 322, 323 

Virtual 

Inspector, 43, 64, 65, 125, 126 

Re-assembly, 55 

Reality, 4, 27, 28, 45, 80, 209 

Reconstruction, 10, 12, 53, 55, 247, 459 

Repainting, 57 

Visual communication, 3, 41, 46 

Voronoi, 435, 436, 441 

Diagrams, 435, 439, 440 

Water-tight reconstruction, 116 

Watermarking, 479, 482, 485–490, 496, 502, 506 

Watershed, 461 

Web GIS, 45 

Web service, 165 

WebGL, 127 

Wide-band, 350–354, 359 

Yellowing, 282, 284, 299, 300, 303, 306

Analysis of Ancient Mosaic Images for Dedicated ... - Institut Fresnel

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