Analysis of Ancient Mosaic Images for Dedicated ... - Institut Fresnel
Analysis of Ancient Mosaic Images for Dedicated ... - Institut Fresnel
Analysis of Ancient Mosaic Images for Dedicated ... - Institut Fresnel
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Filippo Stanco, Sebastiano Battiato, Giovanni Gallo<br />
Digital Imaging <strong>for</strong> Cultural Heritage<br />
Preservation
Contributors<br />
Sebastiano Battiato<br />
University <strong>of</strong> Catania<br />
Catania, Italy<br />
Lamia Benyoussef<br />
Ecole Centrale Marseille<br />
Marseille, France<br />
Simone Bianco<br />
University <strong>of</strong> Milano Bicocca<br />
Milan, Italy<br />
Vittoria Bruni<br />
Consiglio Nazionale delle Ricerche<br />
Rome, Italy<br />
Yang Cai<br />
Carnegie Mellon University<br />
Pittsburgh, USA<br />
Roberto Caldelli<br />
University <strong>of</strong> Florence<br />
Florence, Italy<br />
Marco Callieri<br />
Consiglio Nazionale delle Ricerche<br />
Pisa, Italy<br />
Vito Cappellini<br />
University <strong>of</strong> Florence<br />
Florence, Italy<br />
Paolo Cignoni<br />
Consiglio Nazionale delle Ricerche<br />
Pisa, Italy<br />
Jacob Collins<br />
c/o Adelson Galleries<br />
New York, USA<br />
Alessandro Colombo<br />
University <strong>of</strong> Milano Bicocca<br />
Milan, Italy<br />
David Corrigan<br />
Trinity College<br />
Dublin, Ireland<br />
Massimiliano Corsini<br />
Consiglio Nazionale delle Ricerche<br />
Pisa, Italy<br />
Andrew Craw<strong>for</strong>d<br />
Consiglio Nazionale delle Ricerche<br />
Rome, Italy<br />
Paul Debevec<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Andrea Del Mastio<br />
University <strong>of</strong> Florence<br />
Florence, Italy<br />
Andrey Del Pozo<br />
Univesity <strong>of</strong> Illinois at Urbana-Champaign<br />
Urbana, USA<br />
Matteo Dellepiane<br />
Consiglio Nazionale delle Ricerche<br />
Pisa, Italy<br />
Stéphane Derrode<br />
École Centrale Marseille<br />
Marseille, France<br />
Gianpiero Di Blasi<br />
University <strong>of</strong> Palermo<br />
Palermo, Italy<br />
vii
viii<br />
Marco Duarte<br />
Princeton University<br />
Princeton, USA<br />
Per Einarsson<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Marcos Fajardo<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Yasuo Furuichi<br />
Kanagawa, Japan<br />
Giovanni Gallo<br />
University <strong>of</strong> Catania<br />
Catania, Italy<br />
Andrew Gardner<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Francesca Gasparini<br />
University <strong>of</strong> Milano Bicocca<br />
Milan, Italy<br />
Tim Hawkins<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Andrew Jones<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Dave Kale<br />
Stan<strong>for</strong>d University<br />
Stan<strong>for</strong>d, USA<br />
Anil Kokaram<br />
Trinity College<br />
Dublin, Ireland<br />
Ashutosh Kulkarni<br />
Stan<strong>for</strong>d University<br />
Stan<strong>for</strong>d, USA<br />
Therese Lundgren<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Philippe Martinez<br />
Ecole Normale Superieure<br />
Paris, France<br />
Nikos Nikolaidis<br />
Aristotle University <strong>of</strong> Thessaloniki<br />
Thessaloniki, Greece<br />
Ioannis Pitas<br />
Aristotle University <strong>of</strong> Thessaloniki<br />
Thessaloniki, Greece<br />
Francois Pitie<br />
Trinity College<br />
Dublin, Ireland<br />
Charis Poullis<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Giovanni Puglisi<br />
University <strong>of</strong> Catania<br />
Catania, Italy<br />
Giovanni Ramponi<br />
University <strong>of</strong> Trieste<br />
Trieste, Italy<br />
Fabio Remondino<br />
B. Kessler Foundation (FBK)<br />
Trento, Italy<br />
Alfredo Restrepo Palacios<br />
Universidad de los Andes<br />
Bogotá, Colombia<br />
M. Dirk Robinson<br />
Ricoh Innovations<br />
Menlo Park, USA<br />
Silvio Savarese<br />
University <strong>of</strong> Michigan<br />
Ann Arbor, USA<br />
Sara J. Schechner<br />
Harvard University
Cambridge, USA<br />
Raimondo Schettini<br />
University <strong>of</strong> Milano Bicocca<br />
Milan, Italy<br />
Roberto Scopigno<br />
Consiglio Nazionale delle Ricerche<br />
Pisa, Italy<br />
Ron Spronk<br />
Queen’s University,<br />
Kingston, Canada<br />
Filippo Stanco<br />
University <strong>of</strong> Catania<br />
Catania, Italy<br />
David G. Stork<br />
Ricoh Innovations<br />
Menlo Park, USA<br />
Jessi Stumpfel<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Davide Tanasi<br />
University <strong>of</strong> Catania<br />
Catania, Italy<br />
Chris Tchou<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
David Tingdahl<br />
Katholieke Universiteit Leuven<br />
Leuven, Belgium<br />
Efthymia Tsamoura<br />
Aristotle University <strong>of</strong> Thessaloniki<br />
Thessaloniki, Greece<br />
Christopher W. Tyler<br />
Smith-Kettlewell Eye Research <strong>Institut</strong>e<br />
San Francisco, USA<br />
Francesca Uccheddu<br />
University <strong>of</strong> Florence<br />
Florence, Italy<br />
Luc Van Gool<br />
Katholieke Universiteit Leuven<br />
Leuven, Belgium<br />
Maarten Vergauwen<br />
GeoAutomation<br />
Leuven, Belgium<br />
Domenico Vitulano<br />
Consiglio Nazionale delle Ricerche<br />
Rome, Italy<br />
Nathaniel Yun<br />
University <strong>of</strong> Southern Cali<strong>for</strong>nia<br />
Playa Vista, USA<br />
Nicholas C. Williams<br />
Towan Headland<br />
Cornwall, UK<br />
Andrew R. Willis<br />
University <strong>of</strong> North Carolina at Charlotte<br />
Charlotte, USA<br />
Silvia Zuffi<br />
ITC CNR<br />
Milan, Italy<br />
ix
Contents<br />
1 Experiencing the Past. Computer Graphics in Archaeology 1<br />
Filippo Stanco, Davide Tanasi<br />
1.1 The Past and the Future. Archaeology and Computer Science . . . . . . . . . . . . . . . . 1<br />
1.2 From the Field to the Screen: 3D Computer Graphics and the Archaeological Heritage . . 2<br />
1.2.1 Computer Graphics 3D and the Archaeological Fieldwork . . . . . . . . . . . . . 2<br />
1.2.2 Monitoring the Heritage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br />
1.2.3 The Virtual Museum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br />
1.2.4 3D Modeling as Cognitive Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . 4<br />
1.3 The Archeomatica Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br />
1.4 Archaeological 3D Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br />
1.5 Haghia Triada, Crete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6<br />
1.5.1 Propylon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8<br />
1.5.2 House <strong>of</strong> the Razed Rooms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9<br />
1.5.3 VAP house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10<br />
1.6 Polizzello Mountain, Sicily . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br />
1.6.1 Buildings A, B, C, D, E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15<br />
1.6.2 Temenos and Room III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16<br />
1.6.3 Precinct F, East House, Temenos House . . . . . . . . . . . . . . . . . . . . . . . 16<br />
1.6.4 The Virtual Acropolis and the Multilayered 3D Model . . . . . . . . . . . . . . . 18<br />
1.7 Digital Restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20<br />
1.7.1 Minoan Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br />
1.7.2 Asclepius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br />
1.7.3 Female Torso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br />
1.7.4 Hellenistic Thysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br />
1.8 Dealing with Image Data in Archaeology: New Perspectives . . . . . . . . . . . . . . . . . 27<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29<br />
2 Using Digital 3D Models <strong>for</strong> Study and Restoration <strong>of</strong> Cultural Heritage Artifacts 39<br />
Matteo Dellepiane, Marco Callieri, Massimiliano Corsini, Roberto Scopigno<br />
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39<br />
2.2 Visual Communication <strong>of</strong> Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41<br />
2.2.1 Computer-generated Animations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41<br />
2.2.2 Interactive Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43<br />
2.2.3 Geographic Web Browsers Deploying 3D Models . . . . . . . . . . . . . . . . . 45<br />
2.3 Art Catalogs and Digital Repositories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45<br />
2.4 Digital 3D as a Tool <strong>for</strong> Art Scholars . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46<br />
2.4.1 Using 3D Scanning to Analyze an Attribution Proposal . . . . . . . . . . . . . . . . 47<br />
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xii<br />
2.4.2 The CENOBIUM Project: an Integrated Visual Comparison <strong>of</strong> Historiated Capitals 50<br />
2.4.3 Classifying and Archiving Carved Faces: the Bayon Digital Archival Project . . . . 51<br />
2.5 Physical Reproduction from the Digital Model . . . . . . . . . . . . . . . . . . . . . . . 52<br />
2.6 Virtual Reconstruction and Re-assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . 53<br />
2.6.1 Virtual Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53<br />
2.6.2 Virtual Re-assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55<br />
2.6.3 Virtual Repainting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57<br />
2.7 Supporting the Restoration Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58<br />
2.7.1 Tools <strong>for</strong> Investigation and Diagnostics . . . . . . . . . . . . . . . . . . . . . . . 59<br />
2.7.2 Tools Supporting Knowledge Management . . . . . . . . . . . . . . . . . . . . . 62<br />
2.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67<br />
3 Accurate and Detailed Image-Based 3D Documentation <strong>of</strong> Large Sites and Complex Objects 73<br />
Fabio Remondino<br />
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73<br />
3.2 Reality-based 3D Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74<br />
3.2.1 Techniques and Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75<br />
3.2.2 Multi-sensor and Multi-source Data Integration . . . . . . . . . . . . . . . . . . . 76<br />
3.2.3 Standards in Digital 3D Documentation . . . . . . . . . . . . . . . . . . . . . . . 78<br />
3.3 Photogrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79<br />
3.3.1 Image Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80<br />
3.3.2 Camera Calibration and Image Orientation . . . . . . . . . . . . . . . . . . . . . 82<br />
3.3.3 3D Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87<br />
3.3.4 Structuring and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89<br />
3.3.5 Texturing and Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90<br />
3.3.6 Main Applications and Actual Problems . . . . . . . . . . . . . . . . . . . . . . . 93<br />
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96<br />
4 Processing Sampled 3D Data: Reconstruction and Visualization Technologies 105<br />
Marco Callieri, Matteo Dellepiane, Paolo Cignoni, Roberto Scopigno<br />
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106<br />
4.1.1 Sources <strong>of</strong> Sampled 3D Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106<br />
4.2 Basic Geometric Processing <strong>of</strong> Scanned Data . . . . . . . . . . . . . . . . . . . . . . . . 108<br />
4.2.1 The 3D Scanning Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109<br />
4.2.2 Implementing Range Maps Alignment as an Automatic Process . . . . . . . . . . 113<br />
4.2.3 Enhancing Incomplete Surface Sampling . . . . . . . . . . . . . . . . . . . . . . 115<br />
4.3 Color Sampling and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116<br />
4.3.1 Basic Acquisition <strong>of</strong> Color Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 116<br />
4.3.2 Recovering Camera Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117<br />
4.3.3 Mapping Complex Photographic Detail on 3D Models . . . . . . . . . . . . . . . 119<br />
4.3.4 Advanced Color Data Acquisition: Sampling Surface Reflection Properties . . . . 120<br />
4.4 MeshLab: an Open Source Tool <strong>for</strong> Processing 3D Scanned Data . . . . . . . . . . . . . . 121<br />
4.5 Efficient Visualization and Management <strong>of</strong> Sampled 3D Data . . . . . . . . . . . . . . . 123<br />
4.5.1 Simplification and Multiresolution Management <strong>of</strong> Huge Models . . . . . . . . . 124
4.5.2 Mesh-based vs. Point-based Encoding and Rendering . . . . . . . . . . . . . . . . 125<br />
4.5.3 Usability <strong>of</strong> Virtual Heritage Worlds . . . . . . . . . . . . . . . . . . . . . . . . . 126<br />
4.5.4 Not Just 3D Data: Adding Other Knowledge . . . . . . . . . . . . . . . . . . . . . 127<br />
4.5.5 Presenting 3D Data on the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127<br />
4.6 3D Digitization: How to Improve Current Procedures and Make It More Practical and<br />
Successful . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128<br />
4.6.1 Technology - Limitations Perceived by Practitioners . . . . . . . . . . . . . . . . 128<br />
4.6.2 Misuse <strong>of</strong> Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129<br />
4.6.3 Better Management <strong>of</strong> 3D Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 129<br />
4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131<br />
5 Digitizing the Parthenon: Estimating Surface Reflectance under Measured Natural Illumination<br />
137<br />
Paul Debevec, Chris Tchou, Andrew Gardner, Tim Hawkins, Charis Poullis, Jessi Stumpfel, Andrew Jones,<br />
Nathaniel Yun, Per Einarsson, Therese Lundgren, Marcos Fajardo and Philippe Martinez<br />
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138<br />
5.2 Background and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139<br />
5.3 Data Acquisiton and Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140<br />
5.3.1 Camera Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140<br />
5.3.2 BRDF Measurement and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 141<br />
5.3.3 Natural Illumination Capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145<br />
5.3.4 3D Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149<br />
5.3.5 Photograph Acquisition and Alignment . . . . . . . . . . . . . . . . . . . . . . . . 151<br />
5.4 Reflectometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152<br />
5.4.1 General Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152<br />
5.4.2 Multiresolution Reflectance Solving . . . . . . . . . . . . . . . . . . . . . . . . . 153<br />
5.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155<br />
5.6 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156<br />
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160<br />
6 ARC3D: a Public Web Service that Turns Photos into 3D Models 163<br />
David Tingdahl, Maarten Vergauwen, and Luc Van Gool<br />
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164<br />
6.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165<br />
6.2.1 System Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165<br />
6.2.2 Upload Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167<br />
6.2.3 Modelviewer Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167<br />
6.3 Automatic Reconstruction Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169<br />
6.3.1 Pipeline Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171<br />
6.3.2 Opportunistic Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171<br />
6.3.3 Hierarchical Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171<br />
6.3.4 Parallel Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172<br />
6.4 Practical Guidelines <strong>for</strong> Shooting <strong>Images</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . 173<br />
6.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173<br />
xiii
xiv<br />
6.4.2 Image Shooting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173<br />
6.4.3 Scene Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174<br />
6.5 Case Study: Reconstruction <strong>of</strong> the Mogao Caves <strong>of</strong> Dunhuang . . . . . . . . . . . . . . . 175<br />
6.5.1 3D Reconstruction <strong>of</strong> Mogao Cave 322 . . . . . . . . . . . . . . . . . . . . . . . 175<br />
6.5.2 Image Capturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176<br />
6.5.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181<br />
6.6 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181<br />
6.6.1 A Complete Building: Arc de Triomphe . . . . . . . . . . . . . . . . . . . . . . . . 181<br />
6.6.2 Environment Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183<br />
6.6.3 Further Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183<br />
6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189<br />
7 Pattern Discovery from Eroded Rock Art 191<br />
Yang Cai<br />
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191<br />
7.2 Surface Imaging Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192<br />
7.2.1 Laser Scan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193<br />
7.2.2 Pattern Projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195<br />
7.2.3 Stick Shadow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196<br />
7.2.4 Multiview Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197<br />
7.2.5 Polynomial Texture Maps (PTM) . . . . . . . . . . . . . . . . . . . . . . . . . . 199<br />
7.3 Pattern Discovery Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200<br />
7.3.1 Simulated Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201<br />
7.3.2 Data Trans<strong>for</strong>mation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201<br />
7.3.3 Trans<strong>for</strong>mation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202<br />
7.3.4 Off-Line Handwriting Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 205<br />
7.3.5 Interactive Versus Automatic Discovery . . . . . . . . . . . . . . . . . . . . . . . . 207<br />
7.4 From Reconstruction to Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208<br />
7.5 Interaction Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208<br />
7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210<br />
8 Computational <strong>Analysis</strong> <strong>of</strong> Archaeological Ceramic Vessels and their Fragments 215<br />
Andrew R. Willis<br />
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216<br />
8.1.1 Significance <strong>of</strong> Generic Artifact Reconstruction . . . . . . . . . . . . . . . . . . . . 217<br />
8.1.2 Significance <strong>of</strong> Ceramic Vessel Reconstruction . . . . . . . . . . . . . . . . . . . . 217<br />
8.1.3 Traditional Puzzle Solving and Artifact Reconstruction . . . . . . . . . . . . . . . 218<br />
8.2 Artifact Reconstruction Systems: Basic Components and Concepts . . . . . . . . . . . . 219<br />
8.2.1 Digitizing Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220<br />
8.2.2 Approaches <strong>for</strong> Computational Sherd <strong>Analysis</strong> . . . . . . . . . . . . . . . . . . . 220<br />
8.2.3 Computerized Typology <strong>for</strong> Automated Sherd Classification . . . . . . . . . . . . 222<br />
8.2.4 Computerized Vessel Reconstruction by Fragment Matching . . . . . . . . . . . . 224<br />
8.3 Computational Models <strong>for</strong> Vessels and their Fragments . . . . . . . . . . . . . . . . . . . 225<br />
8.3.1 Modeling Ceramic Vessels as Surfaces <strong>of</strong> Revolution . . . . . . . . . . . . . . . . 225
8.3.2 Estimating the Vessel Axis from Digitized Sherds . . . . . . . . . . . . . . . . . . 226<br />
8.3.3 Estimating Pr<strong>of</strong>ile Curves from Digitized Sherds . . . . . . . . . . . . . . . . . . . 227<br />
8.3.4 Simultaneously Solving <strong>for</strong> the Axis and Pr<strong>of</strong>ile Curve . . . . . . . . . . . . . . . 230<br />
8.3.5 Dealing with Asymmetries in Archaeological Ceramic Sherds . . . . . . . . . . . 232<br />
8.3.6 Vessel Reconstruction by Matching Axis/Pr<strong>of</strong>ile Curve Models . . . . . . . . . . . 233<br />
8.4 Vessel Reconstruction by Sherd Matching: 3D Puzzle Solving . . . . . . . . . . . . . . . 234<br />
8.4.1 The Bayesian Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235<br />
8.4.2 Searching <strong>for</strong> the Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236<br />
8.5 Current Trends in Computational Artifact Reconstruction . . . . . . . . . . . . . . . . . . . 237<br />
8.5.1 Going Beyond Geometry: Textures and Patterns on Sherd Surfaces . . . . . . . . . 238<br />
8.5.2 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238<br />
8.5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241<br />
9 Digital Reconstruction and <strong>Mosaic</strong>ing <strong>of</strong> Cultural Artifacts 245<br />
Efthymia Tsamoura, Nikos Nikolaidis, Ioannis Pitas<br />
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245<br />
9.2 The Three-step Object Reconstruction Procedure . . . . . . . . . . . . . . . . . . . . . . 250<br />
9.2.1 Fragments Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253<br />
9.2.2 Matching <strong>of</strong> Candidate Adjacent Fragments . . . . . . . . . . . . . . . . . . . . . 253<br />
9.2.3 Multi-fragment Merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254<br />
9.3 Approaches <strong>for</strong> Object Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254<br />
9.3.1 Torn by Hand Document Reassembly . . . . . . . . . . . . . . . . . . . . . . . . 255<br />
9.3.2 3D Objects Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255<br />
9.3.3 2D Puzzles Reassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258<br />
9.3.4 2D Objects Reassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258<br />
9.4 Automatic Color Based Reassembly <strong>of</strong> Fragmented <strong>Images</strong> and Paintings . . . . . . . . . 259<br />
9.4.1 Discovery <strong>of</strong> Adjacent Image Fragments . . . . . . . . . . . . . . . . . . . . . . . 259<br />
9.4.2 Discovery <strong>of</strong> Matching Contour Segments <strong>of</strong> Adjacent Image Fragments . . . . . 260<br />
9.4.3 Contour Alignment <strong>of</strong> Fragments . . . . . . . . . . . . . . . . . . . . . . . . . . 262<br />
9.4.4 Overall Image Reassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263<br />
9.4.5 Image Reassembly Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 264<br />
9.5 Reduced Complexity Image <strong>Mosaic</strong>ing Utilizing Spanning Trees . . . . . . . . . . . . . . 266<br />
9.5.1 Two-Image <strong>Mosaic</strong>ing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268<br />
9.5.2 Spanning Tree <strong>Mosaic</strong>ing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269<br />
9.5.3 Sub-graph Spanning Tree <strong>Mosaic</strong>ing . . . . . . . . . . . . . . . . . . . . . . . . . 270<br />
9.5.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271<br />
9.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275<br />
10 Virtual Restoration <strong>of</strong> Antique Books and Photographs 281<br />
Filippo Stanco, Alfredo Restrepo Palacios, and Giovanni Ramponi<br />
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282<br />
10.1.1 Photographic Prints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282<br />
10.1.2 Antique Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284<br />
10.2 Detection <strong>of</strong> the Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285<br />
xv
xvi<br />
10.2.1 Foxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285<br />
10.2.2 Water Blotches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287<br />
10.2.3 Cracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288<br />
10.2.4 Fragmented Glass Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292<br />
10.3 Virtual Restoration <strong>of</strong> Antique Photographic Prints Affected by Foxing and Water Blotches 293<br />
10.3.1 Inpainting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294<br />
10.3.2 Additive/Multiplicative Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295<br />
10.3.3 Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295<br />
10.4 Restoration <strong>of</strong> the Fragmented Glass Plate Photographs . . . . . . . . . . . . . . . . . . . 296<br />
10.5 Restoration <strong>of</strong> Yellowing and Foxing in Antique Books . . . . . . . . . . . . . . . . . . . 299<br />
10.5.1 Foxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302<br />
10.5.2 Page Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303<br />
10.5.3 OCR in Antique Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304<br />
10.6 On Image Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306<br />
10.6.1 On the Measurement <strong>of</strong> Local Luminance and Local Contrast (Statistics <strong>of</strong> Location<br />
and Dispersion) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307<br />
10.6.2 On the Relationship Between Local Contrast and Local Luminance . . . . . . . . 308<br />
10.6.3 Effect <strong>of</strong> γ-Correction on Scatter Plots <strong>of</strong> Local Contrast Versus Local Luminance 310<br />
10.6.4 Word Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311<br />
10.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315<br />
11 Advances in Automated Restoration <strong>of</strong> Archived Video 321<br />
Anil Kokaram, Francois Pitie, David Corrigan and Domenico Vitulano, Vittoria Bruni, Andrew Craw<strong>for</strong>d<br />
11.1 Dirt and Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322<br />
11.1.1 Simple Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324<br />
11.1.2 Better Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325<br />
11.1.3 Further Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327<br />
11.2 Semi-transparent Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329<br />
11.2.1 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331<br />
11.3 Line Scratches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333<br />
11.4 Global Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336<br />
11.4.1 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339<br />
11.5 An Evolving Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341<br />
12 Applications <strong>of</strong> Spectral Imaging and Reproduction to Cultural Heritage 347<br />
Simone Bianco, Alessandro Colombo, Francesca Gasparini, Raimondo Schettini, and Silvia Zuffi<br />
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348<br />
12.2 Colorimetric and Multispectral Color Imaging . . . . . . . . . . . . . . . . . . . . . . . 349<br />
12.3 Capturing a Multispectral Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350<br />
12.4 Imaging and Signal Processing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 353<br />
12.4.1 ’Narrow-band’ Multispectral Imaging . . . . . . . . . . . . . . . . . . . . . . . . 353<br />
12.4.2 ’Wide-band’ Multispectral Imaging . . . . . . . . . . . . . . . . . . . . . . . . . 354<br />
12.4.3 Training Set Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356<br />
12.4.4 Filters Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357
12.5 Recovery Multispectral In<strong>for</strong>mation from RGB <strong>Images</strong> . . . . . . . . . . . . . . . . . . . 359<br />
12.5.1 Spectral Based Color Imaging Using RGB Digital Still Cameras . . . . . . . . . . 359<br />
12.6 Storing a Multispectral Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361<br />
12.7 Evaluating System Per<strong>for</strong>mance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363<br />
12.8 Multispectral Image Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364<br />
12.8.1 Colorimetric Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364<br />
12.8.2 Spectral Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364<br />
12.8.3 Spectral Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366<br />
12.8.4 Viewpoint and Lighting Position Invariant Reproduction . . . . . . . . . . . . . . 366<br />
12.9 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369<br />
13 Did Early Renaissance Painters Trace Optically Projected <strong>Images</strong>? The Conclusion <strong>of</strong> Independent<br />
Scientists, Art Historians and Artists 379<br />
David G. Stork, Jacob Collins, Marco Duarte, Yasuo Furuichi, Dave Kale, Ashutosh Kulkarni, M. Dirk<br />
Robinson, Christopher W. Tyler, Sara J. Schechner, and Nicholas C. Williams<br />
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380<br />
13.2 The Projection Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382<br />
13.2.1 Philosophical and Logical Foundations <strong>of</strong> the Projection Theory . . . . . . . . . . 382<br />
13.3 Image Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387<br />
13.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387<br />
13.3.2 Lorenzo Lotto, Husband and wife (1543) . . . . . . . . . . . . . . . . . . . . . . . 387<br />
13.3.3 Jan van Eyck, Portrait <strong>of</strong> Giovanni (?) Arnolfini and his wife (1434) . . . . . . . . 389<br />
13.3.4 Jan van Eyck, Portrait <strong>of</strong> Niccolò Albergati (1431 & 1432) . . . . . . . . . . . . . . 391<br />
13.3.5 Robert Campin, The Mérode altarpiece (1430) . . . . . . . . . . . . . . . . . . . 392<br />
13.3.6 Georges de la Tour, Christ in the carpenter’s studio (1645) . . . . . . . . . . . . . 393<br />
13.3.7 Caravaggio, The calling <strong>of</strong> St. Matthew (1599–1600) . . . . . . . . . . . . . . . . 394<br />
13.3.8 Hans Memling, Flower still-life (c. 1490) . . . . . . . . . . . . . . . . . . . . . . 395<br />
13.3.9 Hans Holbein, The Ambassadors (1533) . . . . . . . . . . . . . . . . . . . . . . . 395<br />
13.3.10 Hans Holbein, George Gisze (1532) . . . . . . . . . . . . . . . . . . . . . . . . . 396<br />
13.4 Documentary Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397<br />
13.5 Material Culture and Renactments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398<br />
13.5.1 Reenactments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399<br />
13.6 Non-Optical Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400<br />
13.7 The “Value” in Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400<br />
13.8 Scholarly Consensus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401<br />
13.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402<br />
14 A Computer <strong>Analysis</strong> <strong>of</strong> the Mirror in Hans Memling’s Virgin and Child and Maarten van<br />
Nieuwenhove 409<br />
Silvio Savarese, David G. Stork, Andrey Del Pozo, and Ron Spronk<br />
14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410<br />
14.2 Memling’s Diptych . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412<br />
14.3 Computer Vision <strong>Analysis</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415<br />
14.4 Modeling Reflections Off a Mirror Surface . . . . . . . . . . . . . . . . . . . . . . . . . 416<br />
xvii
xviii<br />
14.4.1 Direct Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419<br />
14.4.2 Inverse Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419<br />
14.4.3 Experimental Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421<br />
14.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423<br />
14.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426<br />
15 Digital Reproduction <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong>s 431<br />
Sebastiano Battiato, Gianpiero Di Blasi, and Giovanni Gallo, Giovanni Puglisi<br />
15.1 Art and Computer Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431<br />
15.2 History <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong>s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432<br />
15.3 The Digital <strong>Mosaic</strong> Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434<br />
15.4 The Crystallization <strong>Mosaic</strong>s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435<br />
15.5 The <strong>Ancient</strong> <strong>Mosaic</strong>s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438<br />
15.6 The <strong>Ancient</strong> <strong>Mosaic</strong>s in a 3D environment . . . . . . . . . . . . . . . . . . . . . . . . . 442<br />
15.7 Final Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446<br />
15.7.1 Final Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449<br />
16 <strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong> Applications 453<br />
Lamia Benyoussef and Stéphane Derrode<br />
16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453<br />
16.1.1 Some Historical Facts about <strong>Mosaic</strong>s . . . . . . . . . . . . . . . . . . . . . . . . 454<br />
16.1.2 <strong>Mosaic</strong>s, <strong>Images</strong> and Digital Applications . . . . . . . . . . . . . . . . . . . . . . 455<br />
16.2 Recent Image Processing Projects Concerned with <strong>Mosaic</strong>s . . . . . . . . . . . . . . . . . 457<br />
16.2.1 St. Vitus Cathedral <strong>Mosaic</strong> Restoration . . . . . . . . . . . . . . . . . . . . . . . 458<br />
16.2.2 Arabo-moresque and Islamic <strong>Mosaic</strong> Pattern Classification . . . . . . . . . . . . . 458<br />
16.2.3 Roman <strong>Mosaic</strong>s Indexation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459<br />
16.3 Tesserae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460<br />
16.4 Tessera-based Segmentation and Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 462<br />
16.4.1 Segmentation <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> . . . . . . . . . . . . . . . . . . . . . . 464<br />
16.4.2 Tessera-based Coding and Lossy Compression . . . . . . . . . . . . . . . . . . . 464<br />
16.5 Guidelines Estimation <strong>for</strong> <strong>Mosaic</strong> Structure Retrieval . . . . . . . . . . . . . . . . . . . . 468<br />
16.6 Open Issues and Research Directions in <strong>Mosaic</strong> Image <strong>Analysis</strong> . . . . . . . . . . . . . . 470<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474<br />
17 Copyright Protection <strong>of</strong> Digital <strong>Images</strong> <strong>of</strong> Cultural Heritage 479<br />
Vito Cappellini, Roberto Caldelli, Andrea Del Mastio, Francesca Uccheddu<br />
17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480<br />
17.1.1 A Brief History <strong>of</strong> Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . 480<br />
17.1.2 Watermarking Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480<br />
17.1.3 Watermarking Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 482<br />
17.2 2D Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485<br />
17.2.1 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486<br />
17.2.2 Insertion <strong>of</strong> Multiple Watermarking Codes . . . . . . . . . . . . . . . . . . . . . . 487<br />
17.2.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487<br />
17.2.4 Blind and Non-blind Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 489
17.2.5 Private and Public Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489<br />
17.2.6 Readable and Detectable Watermarks . . . . . . . . . . . . . . . . . . . . . . . . 489<br />
17.2.7 Invertibility and Quasi-invertibility . . . . . . . . . . . . . . . . . . . . . . . . . 490<br />
17.2.8 Reversibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491<br />
17.2.9 Asymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491<br />
17.2.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496<br />
17.3 3D Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496<br />
17.3.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496<br />
17.3.2 3D Objects Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499<br />
17.3.3 State <strong>of</strong> the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502<br />
17.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508<br />
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509<br />
Index 515<br />
xix
16<br />
<strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong><br />
Applications<br />
Lamia Benyoussef and Stéphane Derrode<br />
<strong>Institut</strong> <strong>Fresnel</strong> (CNRS UMR 6133)<br />
École Centrale Marseille<br />
Marseille, France<br />
Email: lbenyoussef@ec-marseille.fr, sderrode@ec-marseille.fr<br />
CONTENTS<br />
16.1 Introduction . . ................................................. . . . . ....................... 453<br />
16.1.1 Some Historical Facts about <strong>Mosaic</strong>s . . ............................................... 454<br />
16.1.2 <strong>Mosaic</strong>s, <strong>Images</strong> and Digital Applications . . ........................................... 455<br />
16.2 Recent Image Processing Projects Concerned with <strong>Mosaic</strong>s . . ................................... 457<br />
16.2.1 St. Vitus Cathedral <strong>Mosaic</strong> Restoration . . .............................................. 457<br />
16.2.2 Arabo-moresque and Islamic <strong>Mosaic</strong> Pattern Classification . . ............................ 458<br />
16.2.3 Roman <strong>Mosaic</strong>s Indexation . . ................................................. . . . .... 459<br />
16.3 Tesserae Extraction . .................................................. . . . ................. 460<br />
16.4 Tessera-based Segmentation and Coding . . ................................................. . . 462<br />
16.4.1 Segmentation <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> . . ............................................ 462<br />
16.4.2 Tessera-based Coding and Lossy Compression . . ....................................... 464<br />
16.5 Guidelines Estimation <strong>for</strong> <strong>Mosaic</strong> Structure Retrieval . . ........................................ 468<br />
16.6 Open Issues and Research Directions in <strong>Mosaic</strong> Image <strong>Analysis</strong> . . ............................... 470<br />
Bibliography . . ................................................. . . . ....................... 474<br />
16.1 Introduction<br />
<strong>Mosaic</strong> is a <strong>for</strong>m <strong>of</strong> art to produce decorative images or patterns out <strong>of</strong> small components. It requires a great<br />
deal <strong>of</strong> time and labor and it is <strong>of</strong>ten very expensive. <strong>Mosaic</strong> is, in return, far more durable in time than<br />
painting, so it can be used in places where painting cannot be <strong>of</strong> practical use, such as floors. <strong>Mosaic</strong> moreover<br />
allows the realization <strong>of</strong> light effects that are impossible with other media. The success <strong>of</strong> the mosaic art <strong>for</strong>m<br />
through the ages is at the origin <strong>of</strong> large collections in several museums, among them we can cite the “Great<br />
Palace <strong>Mosaic</strong> Museum” (Istanbul, Turkey) and the “Musée du Bardo” (Tunis, Tunisia).<br />
Recently, questions rise about the virtual conservation and management <strong>of</strong> collections and their accessibility<br />
to experts such as archeologists or art historians, and even to the large public. Requirements concern<br />
facilities<br />
• <strong>for</strong> cataloging collections and distant consultation, and <strong>for</strong> intelligent retrieval tools (e.g., the extraction<br />
<strong>of</strong> objects with a semantic meaning, such as animal or human, in a complex mosaic scene);<br />
453
454 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
• <strong>for</strong> virtual conservation (e.g., restoration <strong>of</strong> tesserae color), reconstruction (e.g., missing patches) and<br />
anastylosis <strong>of</strong> ancient mosaics.<br />
The aim <strong>of</strong> this Chapter is to draw an overview <strong>of</strong> image processing based methods dedicated to the specificities<br />
<strong>of</strong> mosaic images <strong>for</strong> applications to restoration and cataloging <strong>of</strong> ancient mosaics.<br />
16.1.1 Some Historical Facts about <strong>Mosaic</strong>s<br />
The following description only intends to give some basis entries about history <strong>of</strong> mosaics. And so, it is quite<br />
superficial and partial. Interested readers should consult the numerous books covering the subject, among<br />
them we can cite the following books [11,16,26]. The worldwide web gives also access to very complete lists<br />
<strong>of</strong> bibliographical references on ancient mosaics (e.g., [44]).<br />
The earliest mosaics were installed as floors or pavements, occasionally as walls. Later, ceilings were also<br />
decorated. Originally, mosaics were used strictly in an architectural context. <strong>Mosaic</strong>king smaller, portable<br />
items such as panels or portraits is a later development, though there are a few rare instances <strong>of</strong> items such as<br />
icons from the late Byzantine era. The first mosaics that we know <strong>of</strong> are from what is now the Middle East.<br />
Temple columns in ancient Babylon (present day Iraq) had thousands <strong>of</strong> small clay cones pressed into wet<br />
plaster in decorative and geometric patterns. These date from five thousand years ago. From these humble<br />
beginnings, mosaic developed into a major art <strong>for</strong>m. It was the Greeks, in the four centuries BC, who raised<br />
the pebble technique to an art <strong>for</strong>m, with precise geometric patterns and detailed scenes <strong>of</strong> people and animals.<br />
By 200 BC, specially manufactured pieces “tessera” were being used to give extra detail and range <strong>of</strong> color to<br />
the work. Using small tesserae, sometimes only a few millimeters in size, meant that mosaics could imitate<br />
paintings. Many <strong>of</strong> the mosaics preserved at, <strong>for</strong> example, Pompeii were the work <strong>of</strong> Greek artists. The<br />
expansion <strong>of</strong> the Roman Empire took mosaics further afield, although the level <strong>of</strong> skill and artistry was diluted.<br />
Typically Roman subjects were scenes celebrating their gods, domestic themes and geometric designs.<br />
With the rise <strong>of</strong> the Byzantine Empire from the 5 th century onwards, centered on Byzantium (present day<br />
Istanbul, Turkey), the art <strong>for</strong>m took on new characteristics. These included Eastern influences in style and<br />
the use <strong>of</strong> special glass tesserae called “smalti”, manufactured in northern Italy, made from thick sheets <strong>of</strong><br />
colored glass. Smalti have a rough surface and contain tiny air bubbles. They are sometimes backed with<br />
reflective silver or gold leaf. Whereas Roman mosaics were mostly used as floors, the Byzantines specialized<br />
in covering walls and ceilings. The smalti were ungrouted, allowing light to reflect and refract within the<br />
glass. Also, they were set at slight angles to the wall, so that they caught the light in different ways. The<br />
gold tesserae sparkle as the viewer moves around within the building. Roman images were absorbed into the<br />
typical Christian themes <strong>of</strong> the Byzantine mosaics, although some work is decorative and some incorporates<br />
portraits <strong>of</strong> Emperors and Empresses.<br />
In the west <strong>of</strong> Europe, the Moors brought Islamic mosaic and tile art into the Iberian peninsula in the 8 th<br />
century, while elsewhere in the Muslim world, stone, glass and ceramic were all used in mosaics. In contrast<br />
to the figurative representations in Byzantine art, Islamic motifs are mainly geometric. Examples can be seen<br />
in Spain at the Great Mosque at Cordoba, the Alhambra Palace and Meknes in Morocco (Figure 16.4(b)).<br />
In Arabic countries a distinctive decorative style called “zillij” uses purpose-made ceramic shapes that are<br />
further worked by hand to allow them to tessellate (fit together perfectly to cover a surface).<br />
In the rest <strong>of</strong> Europe, mosaic went into decline throughout the Middle Ages, although some tiling patterns<br />
in abbeys, <strong>for</strong> example, used mosaic effects. In the 19 th century there was a revival <strong>of</strong> interest, particularly in<br />
the Byzantine style, with buildings such as Westminster Cathedral in London and Sacre-Cœur in Paris. The<br />
“Art Nouveau” movement also embraced mosaic art. In Barcelona, Antoni Gaudí worked with Josep Maria<br />
Jujol to produce the stunning ceramic mosaics <strong>of</strong> the Guell Park in the first two decades <strong>of</strong> the 20 th century.<br />
These used a technique known as trencadis in which tiles (purpose-made and waste tiles) covered surfaces <strong>of</strong>
<strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong> Applications 455<br />
buildings. They also incorporated broken crockery and other found objects, a revolutionary idea in <strong>for</strong>mal art<br />
and architecture.<br />
<strong>Mosaic</strong> still continue to interest artists but also crafts people because it is a very accessible, non-elitist<br />
<strong>for</strong>m <strong>of</strong> creativity. The field is rich with new ideas and approaches, and organisations such as the British<br />
Association <strong>for</strong> Modern <strong>Mosaic</strong> (BAMM) [2] and the Society <strong>of</strong> American <strong>Mosaic</strong> Artists (SAMA) [1] exist<br />
to promote mosaic. As a recent example, we can cite the development <strong>of</strong> algorithms <strong>for</strong> computer-aided<br />
generation <strong>of</strong> mosaic images from a raster image, simulating ancient or modern styles [6].<br />
16.1.2 <strong>Mosaic</strong>s, <strong>Images</strong> and Digital Applications<br />
<strong>Mosaic</strong>s are made <strong>of</strong> colored tiles, called tessera or tessella, usually <strong>for</strong>med in the shape <strong>of</strong> a cube <strong>of</strong> materials<br />
separated by a joint <strong>of</strong> mortar. The earliest known mosaic materials were small cones <strong>of</strong> clay pressed into<br />
wet plaster. Semi-precious stones such as lapis lazuli and onyx, as well as shells and terra cotta were also<br />
used. As the art developed, glass, ceramic and stone tesserae were the most common materials, along with<br />
pebbles. Modernly, any small singular component can be used: traditional materials, glass or ceramic cast or<br />
cut into tiles, plus plastic, polymer clay (such as Sculpey or Fimo), beads, buttons, bottle caps, pearls, etc.<br />
The physical study <strong>of</strong> materials used in mosaics continue to be an active research field [10, 17, 38].<br />
Structure characteristics<br />
<strong>Mosaic</strong>s can be built on different natural ground made <strong>of</strong> soil or rock, or on top <strong>of</strong> a previous pavement.<br />
The mosaic itself is composed <strong>of</strong> a variety <strong>of</strong> foundation or preparatory layers and a layer <strong>of</strong> tesserae. Also,<br />
the mosaic surface exhibits irregular hollows (tesserae) and bumps (mortar) through the scene. Hence, a<br />
mosaic can not be considered as a plane surface, as <strong>for</strong> paintings, but has numerous <strong>for</strong>ms <strong>of</strong> irregularities,<br />
typical <strong>of</strong> the artwork style:<br />
• Shape <strong>of</strong> tesserae, The shapes <strong>of</strong> tesserae are irregular, from square shapes to polygonal ones.<br />
• Organization <strong>of</strong> tesserae, Tesserae are not positioned according to a regular lattice. On the contrary, the<br />
smart and judicious choice in the orientation, size and positioning <strong>of</strong> tesserae characterize the artwork<br />
style and exhibit the “general flow” (guidelines) <strong>of</strong> the mosaic chosen by the mosaicist.<br />
• Mortar joint, This positioning makes the joints appear as an irregular network with numerous interconnections<br />
throughout the mosaic. Network color intensity, mainly middle gray, is however not uni<strong>for</strong>m<br />
through the scene.<br />
• Color <strong>of</strong> tesserae, Because <strong>of</strong> materials used and their oldness, tesserae <strong>of</strong> ancient mosaics are generally<br />
characterized by pastel colors, with low contrast.<br />
Oldness characteristics<br />
Other artifacts can be associated to the wear, erosion and oldness <strong>of</strong> ancient mosaics (some <strong>of</strong> them are<br />
illustrated by images in Figures 16.1 and 16.2):<br />
• Disaggregated tesserae, Some tesserae can display a loss <strong>of</strong> cohesion <strong>of</strong> their surfaces, disintegrated into<br />
powder or small grains. Also erosion and patinae deteriorations are commonly encountered in studying<br />
ancient mosaics.<br />
• Missing tessera patches, Most <strong>of</strong> mosaics are not completely preserved and, as a consequence <strong>of</strong><br />
disaggregation, lacunas generally corrupted mosaic scenes.<br />
• Color alteration, Alteration <strong>of</strong> the mosaic surface characterized by a localized change in color (due to fire<br />
damage, graffiti, etc.).
456 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
FIGURE 16.1<br />
Excerpt <strong>of</strong> an ancient Greek mosaic scene (from Paphos, Cyprus), showing missing patches and geometric<br />
de<strong>for</strong>mation due to slant acquisition [25], public domain image.<br />
(a) Disaggregated (b) Eroded (c) Altered in color<br />
FIGURE 16.2<br />
Three examples <strong>of</strong> deteriorated tesserae, from [34].
<strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong> Applications 457<br />
Data acquisition<br />
For latter processing and virtual anastylosis, mosaic scene must first be digitized using camera. All<br />
peculiarities cited above have a strong and complex impact on acquisition and the way mosaic scenes appear<br />
in an image with a limited resolution:<br />
• Relief shadows, During snapshot acquisition, the relief <strong>of</strong> tesserae generates shadows on mortar which<br />
does not appear nearly uni<strong>for</strong>m in intensity all over the image, as it should be.<br />
• Indoor/outdoor acquisition, Previous item is further accentuated by indoor acquisition with flash. In<br />
outdoor acquisition, light is not controlled and one should expect severe contrast variations in acquisitions.<br />
• Geometric de<strong>for</strong>mation, Another degradation comes from the snapshot acquisition angle, especially <strong>for</strong><br />
very large mosaic scenes lying on the floor. This point produces perspective (rotation, scaling and shearing)<br />
de<strong>for</strong>mations <strong>of</strong> tesserae shape in image and the thinning down <strong>of</strong> the mortar width (till disappearing).<br />
• Tessera resolution, The resolution <strong>of</strong> tesserae in an image (i.e., the number <strong>of</strong> pixels to describe a tessera)<br />
should not be too low <strong>for</strong> latter processing. On the other hand, a too high resolution will only able to<br />
capture partial scene.<br />
As a consequence, very large mosaic scenes have to be recorded using multiple overlapping images that<br />
are subsequently co-registered in geometry and color to produce the entire digital scene. An interesting<br />
complementary approach to camera acquisition is given by laser scanning. This technique, used recently <strong>for</strong><br />
ancient mosaics analysis [23, 37], can produce a 3D mesh <strong>of</strong> the mosaic surface and material <strong>for</strong> a detailed<br />
shape analysis.<br />
Hence, dedicated processing methods are required to take into account the specificities <strong>of</strong> mosaic and<br />
the specificities <strong>of</strong> mosaic images, and to adopt image processing strategies suited to the tiling organisation<br />
<strong>of</strong> tesserae. This way, one can expect better per<strong>for</strong>mances than general purpose algorithms. The nature and<br />
the artifacts in mosaic images make segmentation methods based on pixel intensities inefficient (e.g., pixels<br />
associated to the mortar interfere with the classification process <strong>of</strong> tesserae). A strategy better suited to mosaic<br />
images is to consider tesserae as indivisible entities with an almost uni<strong>for</strong>m gray-level value.<br />
The remaining <strong>of</strong> the Chapter starts in Section 16.2 with a brief description <strong>of</strong> several projects involving<br />
image processing methods <strong>for</strong> restoration, preservation and indexation <strong>of</strong> ancient mosaics. Then we present<br />
in details some fondamental applications <strong>of</strong> the tessera-oriented point-<strong>of</strong>-view on mosaic images proposed<br />
by the authors, according to the diagram in Figure 16.3. Section 16.3 presents an effective way to extract<br />
tesserae, while Section 16.4 exposes two direct applications <strong>of</strong> this strategy <strong>for</strong> mosaic image segmentation<br />
and coding. Section 16.5 presents an effective way to retrieve the main guidelines <strong>of</strong> a mosaic which can be<br />
helpful <strong>for</strong> delimitating semantic objects present in a complex scene. Finally, the conclusion in Section 16.6<br />
examines some open issues and suggests future research directions to succeed in providing appropriated tools<br />
to museums and experts.<br />
16.2 Recent Image Processing Projects Concerned with <strong>Mosaic</strong>s<br />
A few project involving computer science and image processing <strong>for</strong> mosaic conservation, restoration or<br />
cataloging are reported in the literature. Here is a short description <strong>of</strong> them.
458 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
FIGURE 16.3<br />
Organization <strong>of</strong> Chapter with the processing pipeline.<br />
16.2.1 St. Vitus Cathedral <strong>Mosaic</strong> Restoration<br />
The more accomplished project concerns the restoration <strong>of</strong> the St. Vitus cathedral mosaics, in Prague (Czech<br />
Republic), reported in [49]. The Golden Gate (the southern entrance to the cathedral) is decorated with a<br />
unique work <strong>of</strong> art: a colored, richly gilded mosaic representing the Last Judgement, see Figure 16.4(a). In<br />
1992 the Office <strong>of</strong> the President <strong>of</strong> the Czech Republic and the Getty Conservation <strong>Institut</strong>e [3] began to<br />
cooperate to restore and conserve the mosaic.<br />
Having the historical photo <strong>of</strong> the mosaic from the end <strong>of</strong> the 19 th century and the photos <strong>of</strong> the current<br />
state, authors study the evolution <strong>of</strong> the mosaic, which was several times reconstructed and conserved. Firstly<br />
they tried to restore the historical photograph - remove noise , deblur the image, increase the contrast. Then,<br />
they remove the geometrical difference between images by means <strong>of</strong> the multi-modal registration using<br />
mutual in<strong>for</strong>mation. Finally, they identify mutual differences between the photos, which indicate the changes<br />
on the mosaic during the centuries.<br />
16.2.2 Arabo-moresque and Islamic <strong>Mosaic</strong> Pattern Classification<br />
Lot <strong>of</strong> attention has also been paid to islamic mosaics, <strong>of</strong> which artwork style is illustrated through Figure<br />
16.4(b). The particularities <strong>of</strong> such mosaics come from the periodicity and symmetry <strong>of</strong> tile patterns.<br />
A. Zarghili et al, [47, 48] propose a method to index an Arabo-Moresque decor database which is not<br />
based on symmetry. They use a supervised mosaicking technique to capture the whole principal geometric<br />
in<strong>for</strong>mation (connected set <strong>of</strong> polygonal shapes, called “spine”) <strong>of</strong> a pattern. The spine is then described<br />
by using Fourier shape descriptors [35] to allow retrieving <strong>of</strong> images even under translation, rotation and<br />
scale. But, according to [14], the method cannot be automatized and does not allow the classification <strong>of</strong> these<br />
patterns according to any criterion.<br />
In [21], the authors propose image processing techniques to restore mosaic patterns. Image analysis<br />
tools are developed to obtain in<strong>for</strong>mation about design patterns which are used to recover missing motifs or
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(a) (b)<br />
FIGURE 16.4<br />
(a) Photo <strong>of</strong> the “Last Judgement” mosaic in the St. Vitus cathedral (Prague, Czech Republic), from [4]. (b)<br />
Islamic mosaic (Meknes, Morocco).<br />
tesserae. One difficulty was to propose methods robust to the discrepancies between equal object shapes (due<br />
to manual artwork, oldness, etc.). The symmetry, once recovered, allows virtual reconstruction by inpainting<br />
methods and physical restoration <strong>of</strong> damaged parts <strong>of</strong> mosaics.<br />
To study Islamic geometrical patterns, and all periodic patterns such as those encountered in textile<br />
patterns [41] or wallpapers, several works are based on the symmetry group theory. In [14], authors first<br />
classify patterns into one <strong>of</strong> the three following categories: (i) pattern generated by translation along one<br />
dimension, (ii) patterns which contain translational symmetries in two independent directions (refers to the<br />
seventeen wallpaper groups), and (iii) “rosettes” that describes patterns which begin at a central point and<br />
grow radially outward. For every pattern, authors extract the symmetry group and the fundamental region (i.e.,<br />
a representative region in the image from which the whole image can be regenerated). Finally, they describe<br />
the fundamental region by a simple color histogram and build the feature vector which is a combination <strong>of</strong> the<br />
symmetry feature and histogram in<strong>for</strong>mation. Authors shows promising experiments <strong>for</strong> either classification<br />
or indexing.<br />
In [15], authors exploit symmetry and auto-similarity <strong>of</strong> motifs to design a system to index Arabo-<br />
Moresque mosaic images based on the fractal dimension. <strong>Mosaic</strong>s are first segmented automatically using<br />
color in<strong>for</strong>mation. The classification decomposes the initial motif into a set <strong>of</strong> basic shapes. Contours <strong>of</strong> those<br />
shapes are then characterized by their fractal dimension which gives, according to the authors, a relevant<br />
measure <strong>of</strong> the geometric structure <strong>of</strong> the tile pattern. Some retrieval per<strong>for</strong>mances are also reported.<br />
16.2.3 Roman <strong>Mosaic</strong>s Indexation<br />
Recently, two Content-Based Image Retrieval (CBIR) systems have been proposed to catalog and index<br />
Roman mosaic images.<br />
The first one, proposed in [32], details a complete CBIR system which include (i) object extraction from<br />
a complex mosaic scene by using unsupervised statistical segmentation and (ii) invariant description <strong>of</strong><br />
semantic objects using the analytical Fourier-Mellin trans<strong>for</strong>m [13, 20]. Similarity between querying mosaic<br />
and the database is based on an index constructed from the invariant descriptors and an appropriate metric<br />
(Euclidean and Hausdorff).
460 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
(a) (b)<br />
FIGURE 16.5<br />
Excerpt <strong>of</strong> an ancient mosaic showing a wild boar (a) and a zoom on its hind legs (b).<br />
The second CBIR [27, 28] is a general system to index and retrieve by the content historic document<br />
images. While the object annotation in database images is done manually and <strong>of</strong>f-line, the indexation is done<br />
automatically using an extended curvature scale space descriptor [33] suitable <strong>for</strong> concave and convex shapes.<br />
The query/retrieval <strong>of</strong> pertinent shapes from the database starts with a user drawing query (with a computer<br />
mouse or a pen) that is compared to entries in the database using a fuzzy similarity measure. The system<br />
integrates an XML Database con<strong>for</strong>m to the MPEG7 standard, and experiments on large databases providing<br />
by the National Library <strong>of</strong> Tunisia and some Tunisian museums are reported.<br />
One key-point <strong>for</strong> such systems to be effective is to improve semantic objects extraction. Pixel-based<br />
methods, as the one used in [32], require heavy post-processing to detect shapes <strong>of</strong> interest from pixels classe.<br />
Recently, a new viewpoint on mosaic images, based directly on tesserae, have been proposed [8]. Once tessera<br />
extraction is achieved, this strategy facilitates basic steps toward CBIR applications, as described in the<br />
following.<br />
16.3 Tesserae Extraction<br />
A “natural way” to describe mosaics is to consider tesserae in their own instead <strong>of</strong> groups <strong>of</strong> isolated pixels,<br />
allowing to develop tessera-oriented processing. Indeed, a mosaic image can be seen as an irregular lattice<br />
in which a node is a tessera (Figure 16.5). The grid structure has been superposed to the zoomed image in<br />
Figure 16.5(b) to illustrate the complexity <strong>of</strong> the neighborhood system, both in the number <strong>of</strong> neighbors and<br />
in their orientation. To adopt this point <strong>of</strong> view on mosaic images, a robust method is required to extract<br />
tesserae from the network <strong>of</strong> mortar surrounding them.<br />
A way to consider this problem is to deal with the dual problem (i.e., the extraction <strong>of</strong> the network <strong>of</strong><br />
mortar) which is close to recurrent problems encountered in several image processing applications (i.e., in<br />
medical imaging: vascular network segmentation from angiographies [12, 46], or in satellite imaging: road<br />
extraction in urban scenes [29, 30]).<br />
Several approaches have been proposed. Methods based on contour extraction are widely used and mainly
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FIGURE 16.6<br />
Processing chain involved in tessera extraction, using gray-scale morphology tools.<br />
rely on the assumption that the network pixels and neighboring ones have different gray levels in order to<br />
compute gradients. But methods based on high-pass filter, such as Harris’s corner detector, highlight pixels<br />
belonging to the network, not connected components. Higher-level processings detect lines with varying<br />
widths [18, 39]. Strategies that track the entire network from a starting point [7, 45] are difficult to justify in<br />
the mosaic image case due to the high number <strong>of</strong> interconnections in a mosaic network graph.<br />
Numerous methods based on Markov modeling [19,40] or active contours [36,42] have also been proposed.<br />
These methods are quite efficient but time consuming. In the case <strong>of</strong> mosaics, these methods are not suited<br />
because <strong>of</strong> the high density <strong>of</strong> the network to be extracted in images. In [19], a Markov model is applied<br />
on a graph <strong>of</strong> adjacency crests, detected by a Watershed Trans<strong>for</strong>mation applied on a criterion image. This<br />
criterion image, computed from the original one, exhibits the potential <strong>of</strong> each pixel to belong to the network.<br />
A study <strong>of</strong> tessera extraction has been conducted in [8]. The solution adopted, and preferred over other<br />
experimented strategies, is based on gray-level morphology which is suited to the tiling organisation <strong>of</strong><br />
mosaics. The Watershed Trans<strong>for</strong>mation (WT) approach [31] appeared interesting <strong>for</strong> mosaic images since<br />
this method is a good compromise between low-level methods (contour detection) and approaches by energy<br />
minimization (Markov model or active contours). But to work well, the WT needs to be computed on a<br />
criterion image (processed from the original one) that shows tesserae as catchment basins and the mortar<br />
network as watershed crests.<br />
The entire algorithm is sketched in Figure 16.6. The goal is to present to the WT a potential image that<br />
exhibits tesserae as catchment basins and the network as a crest crossing the entire image. The pre-processings<br />
are: (i) Contrast enhancement, based on top-hat and bottom-hat trans<strong>for</strong>ms; (ii) Potential criterion computation<br />
to exhibit the network as a crest; and (iii) Area closing [43] to reduce over-detection by WT.<br />
The extraction quality highly depends on the way mosaic images have been acquired. It should be taken<br />
not too far from the mosaic since individual tiles will not be visible: according to experiments, tesserae should<br />
appear with a resolution <strong>of</strong> not less than 10 × 10 pixels. Parameter h is a threshold used by the area closing
462 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
FIGURE 16.7<br />
Criterion image obtained from Figure 16.5. This processing improves the contrat between tessera and the<br />
network <strong>of</strong> mortar.<br />
operator [43]. Its value has an impact on the number <strong>of</strong> extracted tiles: a small value gives an over-detection<br />
whereas a big one produces an under-detection one. Hence, it should be (roughly) adjusted according to the<br />
mean size α <strong>of</strong> tesserae in the image (in number <strong>of</strong> pixels), assuming that α almost constant in a mosaic.<br />
Parameter h should be less than α 2 to avoid small tesserae to be deleted by the morphological operator. In<br />
experiments, value h = α 2 /2 gives good results.<br />
Figure 16.7 shows the criterion image obtained by applying the method to the wild boar image. As can be<br />
seen in this example, the network appears in dark. However tesserae are not uni<strong>for</strong>m in texture and show local<br />
gray-level crests that should be deleted be<strong>for</strong>e WT in order to avoid over-segmentation. Following [19], an<br />
area closing [43] in first computed on the criterion image. This processing gives fewer minima while retaining<br />
crest locations as illustrated in Figure 16.8. The crest contours now represent correctly the network, which is<br />
confirmed by the zoom in Figure 16.9(a). To determine the width <strong>of</strong> the network (and not only a one-pixel<br />
skeleton as done by WT), which varies through the image, a simple threshold is applied on neighboring pixels<br />
<strong>of</strong> crests: a pixel is aggregated to the crest if its gray value is no more different by 10% <strong>of</strong> the skeleton mean<br />
gray value. The result <strong>of</strong> applying such a threshold can be observed in Figure 16.9(b).<br />
A second example <strong>of</strong> tessera extraction based on gray-level morphological processing in given in Figure<br />
16.10.<br />
16.4 Tessera-based Segmentation and Coding<br />
This Section describes two simple and direct applications <strong>of</strong> the tessera-oriented representation <strong>of</strong> ancient<br />
mosaic images: segmentation and coding. Especially, these applications preserve the irregularity <strong>of</strong> tiles shape<br />
and so the mosaic effect perception. From now, we assume that all tesserae in a mosaic image have been<br />
extracted successfully (i.e., one can access the coordinates <strong>of</strong> every pixel belonging to the contour <strong>of</strong> each tile<br />
in the mosaic). Each pixel that does not belong to a tessera is assumed to be a background pixel (i.e., a mortar<br />
network pixel).
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(a) (b)<br />
FIGURE 16.8<br />
Extraction <strong>of</strong> tesserae from the criterion image in Figure 16.7, without (a) and with (b) area closing operator.<br />
(a) (b)<br />
FIGURE 16.9<br />
Result <strong>of</strong> tessera extraction on the zoom in Figure 16.5(a) in (a), and network/tesserae classification in (b).
464 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
(a) (b)<br />
FIGURE 16.10<br />
Illustration <strong>of</strong> the tessera extraction algorithm on the mosaic image <strong>of</strong> a Deer.<br />
16.4.1 Segmentation <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong><br />
Instead <strong>of</strong> using all pixels from the tesserae, and since tesserae are almost homogeneous in color, classification<br />
can be per<strong>for</strong>med directly on tesserae, not on pixels, by using some dedicated features. Tesserae can be<br />
characterized by several features, among them the mean and variance <strong>of</strong> gray-level values. Also one can take<br />
into account the number <strong>of</strong> pixels in the tessera, using a multi-features classification. All pixels belonging to<br />
the mortar network are directly associated to a unique class (they do not participate to the classification). The<br />
class color corresponds to the mean color <strong>of</strong> the mortar pixels.<br />
In Figure 16.11, a simple K-means algorithm based on the mean gray-level value <strong>of</strong> tesserae was sufficient<br />
to get an interesting segmentation. From a semantic point <strong>of</strong> view, this segmentation outper<strong>for</strong>ms the result<br />
obtained by a classical pixel-based K-means strategy. A second example that confirms such interesting<br />
behavior is provided in Figure 16.12. Once again, the segmentation quality depends on the robustness <strong>of</strong><br />
tessera extraction.<br />
16.4.2 Tessera-based Coding and Lossy Compression<br />
Another direct application <strong>of</strong> the WT tessera labeling is to develop a compact and efficient coding representation<br />
<strong>of</strong> ancient mosaic images [9]. This kind <strong>of</strong> module can be useful <strong>for</strong> rapid thumbnails transmission in<br />
case <strong>of</strong> distant catalog browsing application. The compression proposed is lossy but preserves the specificities<br />
<strong>of</strong> mosaic images, especially the irregularity <strong>of</strong> tesserae shape. In a way, the strategy described below is better<br />
suited to ancient mosaic images than a general purpose lossy compression algorithm, such asjpeg which is<br />
based on square-blocks.<br />
We still consider that all tesserae in a mosaic have been extracted successfully (i.e., we can access the<br />
coordinates <strong>of</strong> every pixel belonging to the contour <strong>of</strong> each tile in the mosaic). The network can be seen as<br />
the mosaic image background (i.e., each pixel that does not belong to a tessera is assumed to be a background<br />
pixel). Let I be a mosaic image with dimensions L × C, made <strong>of</strong> N colored tiles. The tessera-based<br />
lossy-compression specificity has been designed in order to take into account the two following observations:<br />
• A tile n, n ∈ [1, N], is usually <strong>for</strong>med in the shape <strong>of</strong> a cube <strong>of</strong> material, mostly with uni<strong>for</strong>m color.
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(a) Pixel-based K-means segmentation (b) Tile-based K-means segmentation<br />
FIGURE 16.11<br />
Segmentation comparison <strong>of</strong> the mosaic image in Figure 16.5(a) with a pixel-based strategy (a) and the<br />
tessera-based strategy presented in Section 16.3 (b). Both pixel- and tile-based segmentations make use <strong>of</strong> an<br />
unsupervised K-means classification algorithm, the first one with 3 classes, and the second one only with 2<br />
classes since the mortar is already extracted.<br />
Hence a vector <strong>of</strong> three intensity colors (Rn, Gn, Bn) is enough to describe the color <strong>of</strong> tessera n. This<br />
color can be easily estimated by computing the mean intensity <strong>of</strong> pixels belonging to the tessera.<br />
• The mortar intensity, mainly middle gray, is not uni<strong>for</strong>m throughout the image because <strong>of</strong> shadows due to<br />
non-flat mosaic surfaces and snapshot acquisition angle. However, the network intensity should appear<br />
the same everywhere in the image. Hence, all pixels belonging to the mortar network is coded by the<br />
same color vector (Rb, Gb, Bb), b stands <strong>for</strong> background. This value can be estimated by computing<br />
the mean intensity <strong>of</strong> network.<br />
Coding file structure<br />
Thecms (“Compressed MoSaic”) <strong>for</strong>mat is structured as follows (see Figure 16.13) :<br />
• Header: The header is made <strong>of</strong><br />
1. The original image size in pixels (2 × 2 bytes).<br />
2. The number N <strong>of</strong> tesserae in the image (2 bytes).<br />
3. The mean color values (Rb, Gb, Bb) <strong>of</strong> the network (3 bytes).<br />
Hence, nine bytes are necessary to code the header. Other in<strong>for</strong>mation such as the name <strong>of</strong> the original<br />
image or a comment can be added.<br />
• Body: each tessera n has the following code structure<br />
1. Tessera header contains the number <strong>of</strong> pixels Nn <strong>of</strong> the contour (1 byte), and the mean color values<br />
(Rn, Gn, Bn) <strong>of</strong> the tessera (3 bytes).
466 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
(a) (b) (c)<br />
FIGURE 16.12<br />
K-means segmentation <strong>of</strong> a mosaic representing a bird (a) with the pixel-based (b) and the tile-based (c)<br />
strategies.<br />
FIGURE 16.13<br />
Sketch <strong>of</strong> thecms file <strong>for</strong>mat to compress images <strong>of</strong> ancient mosaics (without Huffman coding).<br />
2. Tessera body codes the closed-contour using the coordinates (l1,c1) <strong>of</strong> the first pixel (4 bytes) and<br />
Freeman chain code (or whatever code) <strong>for</strong> the subsequent Nn − 1 pixels. If no Huffman coding<br />
technic is applied, only 3 bits per contour pixels are necessary.<br />
(Nn−1) 3<br />
Counting the number <strong>of</strong> bytes necessary to code a tessera n gives: 8 + rounded to the ceil<br />
8<br />
value. The last byte is filled in to start the next tessera coding on a new byte. Hence, 21 bytes are needed<br />
to code a tessera with Nn = 34.<br />
<strong>Mosaic</strong> image reconstruction from the compressed file is done by reconstructing the image background<br />
using the network color intensity, on which each tessera with its own color is superposed. The order tesserae<br />
are saved or read in the body <strong>of</strong> thecms file has no influence on the reconstruction quality, but can be changed<br />
<strong>for</strong> a desired application. For example, one can think organizing tesserae by grouping tiles in the file according<br />
to the semantic object they belong to (e.g., human, animal, etc.). Hence reconstruction will progressively show<br />
the different objects present in the mosaic scene. Tesserae dedicated to the background, mainly <strong>of</strong> uni<strong>for</strong>m<br />
color as illustrated in Figures 16.5(a), 16.10(a) and 16.12(a), can be saved at the end <strong>of</strong> the file since their<br />
semantic meaning is, in general, less interesting than the one from objects tesserae. Specific organization<br />
<strong>of</strong> tesserae within the file can also be <strong>of</strong> interest <strong>for</strong> mosaic database indexing and text- or content-based<br />
retrieval, see Section 16.6. Hence, we get a compact and easy-to-handle lossy-compressed representation <strong>of</strong><br />
mosaic image. The compression <strong>of</strong> a mosaic image and its reconstruction from acms file are summed up in<br />
Algorithms 1 and 2.<br />
Experimental results<br />
To evaluate the compression algorithm, we process on the Deer mosaic image in Figure 16.10(a). The<br />
decoding result is shown in Figure 16.14 and must to be compared with the original image. Pixels attributed
<strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong> Applications 467<br />
Algorithm 1cms <strong>for</strong>mat coding.<br />
Require: <strong>Mosaic</strong> image I.<br />
Ensure: Tessera-oriented coding <strong>of</strong> I in filef.<br />
Tessera extraction (cf. Section 16.3).<br />
<strong>for</strong> n = 1, . . . , N do<br />
Write the number <strong>of</strong> contour pixels Nn ←f.<br />
Write the mean color value Vn ←f.<br />
Write the first contour pixel coordinates ln, cn ←f.<br />
<strong>for</strong> i = 2, . . . , Nn do<br />
Compute chain code direction <strong>for</strong> pixel i.<br />
Write direction ←f.<br />
end <strong>for</strong><br />
end <strong>for</strong><br />
Algorithm 2cms <strong>for</strong>mat decoding.<br />
Require: Compressed filef.<br />
Ensure: Tessera-oriented decoding <strong>of</strong> filefinto image I.<br />
Read file header → L, C, N, Rb, Gb, Bb.<br />
Create an image I with dimensions L × C and background color (Rb, Gb, Bb).<br />
<strong>for</strong> n = 1, . . . , N do<br />
Read tessera header → Nn, Rn, Gn, Bn, ln, cn.<br />
Set pixel (ln, cn) with color (Rn, Gn, Bn).<br />
<strong>for</strong> i = 2, . . . , Nn do<br />
Compute coordinates (li, ci).<br />
Set pixel (li, ci) with color (Rn, Gn, Bn).<br />
end <strong>for</strong><br />
Fill in the closed contour with color (Rn, Gn, Bn).<br />
end <strong>for</strong><br />
FIGURE 16.14<br />
Result <strong>of</strong> coding/decoding <strong>of</strong> the Deer image in Figure 16.10(a).
468 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
to the network represent 27.5% <strong>of</strong> the total number <strong>of</strong> pixels in the image (255 × 394). The number <strong>of</strong><br />
tesserae detected is 314. The image produces a cms file size <strong>of</strong> 8766 bytes, which gives a compression<br />
factor <strong>of</strong> about 11.5 with respect to a raw coding <strong>of</strong> pixels (e.g.,ppm file <strong>for</strong>mat). The compression ratio<br />
depends on the mean tessera size in the mosaic image. The bigger the tiles, the higher the compression ratio<br />
is expected to be.<br />
From a qualitative point <strong>of</strong> view, some remarks can be pointed out. As expected the shape <strong>of</strong> the network<br />
and tesserae is kept very similar to the original ones. The mean colors used to describe network and tesserae<br />
intensities makes the decoded image appears pastel with respect to the original one, since artifacts such as<br />
shadow or dirties are cleaned. Nevertheless the global aspect <strong>of</strong> the mosaic is respected and <strong>of</strong> sufficient<br />
quality to understand the scene content. Lacunas and missing patches are not taken into account by the actual<br />
algorithm. It considers holes as tesserae but should better associate them to the mortar network since we<br />
observe the preparatory layer mortar appears behind. One solution is to fuse the network with tesserae that<br />
show mean color close to the mean network color.<br />
16.5 Guidelines Estimation <strong>for</strong> <strong>Mosaic</strong> Structure Retrieval<br />
<strong>Ancient</strong> mosaicists avoided to align their tiles according to rectangular grids. Indeed, such grids emphasize<br />
only horizontal and vertical lines and may distract the observer from seeing the overall picture. Hence, they<br />
placed tiles in order to emphasis the strong edges <strong>of</strong> the subject to be represented, influencing the overall<br />
perception <strong>of</strong> the mosaic [5]. Hence, organization and positioning <strong>of</strong> tesserae are interesting in<strong>for</strong>mation <strong>for</strong><br />
experts since they emphasize the main directional guidelines chosen by the artist (characteristic <strong>of</strong> the style).<br />
This in<strong>for</strong>mation is <strong>of</strong> crucial interest <strong>for</strong> mosaic dedicated applications such as content-based retrieval <strong>of</strong><br />
semantic elements or region-based mosaic image coding.<br />
To get directional guidelines, one can first think using the principal axes <strong>of</strong> an ellipse-equivalent shape <strong>of</strong><br />
each tessera, using well-known <strong>for</strong>mulae based on geometrical moments (minor and majors axes). However,<br />
ancient mosaic tesserae are not box- neither regular-shaped and principal axes quickly appear not enough<br />
robust. One major drawback <strong>of</strong> such method is that it does not take into account in<strong>for</strong>mation <strong>of</strong> neighboring<br />
tesserae, which is <strong>of</strong> great importance <strong>for</strong> regularization and <strong>for</strong> recovering the main guidelines that emphasize<br />
the “general flow” <strong>of</strong> a mosaic.<br />
Hence, an energy-based contextual algorithm <strong>for</strong> retrieving main directional guidelines in a mosaic has<br />
been proposed [8]. The energy to be minimized is constructed by using two key-features: the mean-gray value<br />
and borders directions <strong>of</strong> each tessera. The optimization is done either by gradient descent or by simulated<br />
annealing.<br />
Methodology<br />
Each tessera n is represented by<br />
• Its barycenter (xn, yn) computed on the support Ωn <strong>of</strong> n.<br />
• The list <strong>of</strong> its neighboring tesserae: Vn = {vn,1, . . . , vn,Tn}. A neighbor is a tessera that shares at<br />
least one pixel <strong>of</strong> mortar network with n.<br />
It should be noted that the number <strong>of</strong> neighbors Tn is different from one tile to the other since tesserae are not<br />
organized according to a regular grid. Each tessera n is characterized by an energy <strong>of</strong> configuration which<br />
links itself to each <strong>of</strong> its neighbor vn,t ∈ Vn. This energy, denoted by En,t with t ∈ [1, . . . , Tn], is the<br />
sum <strong>of</strong> two complementary terms:
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FIGURE 16.15<br />
Plot <strong>of</strong> the regularized and normalized histogram <strong>of</strong> the contour orientation <strong>of</strong> one tile from the boar mosaic<br />
image in Figure 16.5(a).<br />
• The first term Q is based on the mean-gray value <strong>of</strong> tesserae. It is proportional to the sum <strong>of</strong> the difference<br />
<strong>of</strong> gray-level means (1) between n and vn,t, and (2) between n and the symmetrical tessera <strong>of</strong> vn,t with<br />
respect to n. This feature favors alinement <strong>of</strong> tesserae with low contrast, which is a characteristic <strong>of</strong><br />
directional guidelines.<br />
• The second term R is based on the orientation <strong>of</strong> tessera contours. We compute the histogram <strong>of</strong> the<br />
orientation <strong>of</strong> segments constituting the contour <strong>of</strong> tessera n. This histogram is regularized using a<br />
Gaussian kernel, result <strong>of</strong> which is illustrated in Figure 16.15. It should be noted that the two modes <strong>of</strong><br />
the histogram, at approximatively 90 0 each other, correspond to the two ambiguous orthogonal main<br />
directions <strong>of</strong> a square-shaped tile. It is then possible to estimate the p.d.f. at angle αn,t given by the<br />
barycenter <strong>of</strong> n and the one <strong>of</strong> vn,t.<br />
Terms Q and R are normalized to belong to range [0, 1]. We can then initialize the “main direction” <strong>of</strong> a tile<br />
(i.e., the direction <strong>of</strong> the neighboring tessera which gives the highest Q + R value):<br />
tn,max = arg max<br />
t∈[1,...,Tn] En,t<br />
(16.1)<br />
The energy <strong>of</strong> a tessera is then defined as Cn = (2 − En,tn,max) + λ Vn, with λ a weighting factor<br />
set manually. Term Vn is defined as<br />
Vn = 1<br />
Tn<br />
Tn� �<br />
t=1<br />
� αn,tn,max<br />
�<br />
− � αt,tt,max<br />
(16.2)<br />
which is the normalized sum <strong>of</strong> absolute difference between the main direction <strong>of</strong> tile n and the main direction<br />
<strong>of</strong> its neighbor t.<br />
The next step to to minimize the mosaic energy, defined as the sum <strong>of</strong> Cn <strong>for</strong> all tesserae in the mosaic.<br />
This is done by selecting the tessera n which gives the highest value <strong>for</strong> Vn. To reduce the contribution <strong>of</strong><br />
this tessera, we try another main direction and recompute the mosaic energy. At that point, two strategies<br />
have been tested:
470 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
• Deterministic framework (Gradient Descent): if the mosaic energy reduces then the new main direction is<br />
validated, otherwise another main direction is tested. When all directions <strong>for</strong> this tile have been tested, we<br />
repeat the process <strong>for</strong> the next tile with high Vn value.<br />
• Stochastic framework (Simulated Annealing): a configuration which gives a higher mosaic energy can<br />
be validated according to the simulated annealing principle [24]. This strategy allows to search <strong>for</strong> the<br />
global minimum, which can not be reached with previous strategy since the mosaic energy function is not<br />
convex.<br />
The process is iterated until the mosaic energy is almost constant. The cartography <strong>of</strong> tessera orientation is<br />
made <strong>of</strong> the main direction <strong>of</strong> each tile at the last iteration.<br />
Figure 16.16 illustrates the application <strong>of</strong> the tessera orientation methodology on the detail <strong>of</strong> the boar<br />
image in Figure 16.5(a). From the initial configuration <strong>of</strong> tesserae (a) we get the final configuration (b) using<br />
Simulated Annealing (SA) <strong>for</strong> optimization. Figure 16.16(c) shows the evolution <strong>of</strong> the computed energy<br />
during iterations <strong>of</strong> both gradient descent (GD) algorithm and simulated annealing one. As expected, SA<br />
reaches a lower minimum than GD but to the detriment <strong>of</strong> numerous additional iterations (approximately 150<br />
<strong>for</strong> SA versus 50 <strong>for</strong> GD). Indeed, GD searches <strong>for</strong> a local minimum and is highly dependent on the initial<br />
configuration, whereas SA is expected to reach the global minimum due to its stochastic nature.<br />
The tessera cartography obtained with SA optimization is really satisfying when visually compared to the<br />
main directional guidelines <strong>of</strong> the mosaic. This is especially true <strong>for</strong> regions at the borders between classes.<br />
A second example <strong>of</strong> cartography is proposed in Figure 16.17, after both simulated annealing and gradient<br />
descent optimizations. Results are very similar except <strong>for</strong> some guidelines where SA gives a better result.<br />
Once again, the tessera orientation estimation methodology, which makes use <strong>of</strong> contextual in<strong>for</strong>mation, gives<br />
regularized results that emphasize the mosaic guidelines. Nevertheless, confusions can be found on areas<br />
with homogeneous colors and where tesserae are square-like shaped. Indeed, <strong>for</strong> those kinds <strong>of</strong> tesserae two<br />
orthogonal directions are equally probable, which generally gives ambiguous results. However, these areas <strong>of</strong><br />
uni<strong>for</strong>m color are <strong>of</strong> limited interest <strong>for</strong> object-based scene applications, such as mosaic pattern recognition.<br />
Finally, Figure 16.18 shows a failure case in tessera orientation estimation. Indeed, the result will not allow<br />
to find the main directional guidelines in the mosaic, that can be more easily observed in Figure 16.12(a). The<br />
main reason comes from an over-detection <strong>of</strong> tesserae from the extraction step. This behavior is observed on<br />
mosaics built with tesserae <strong>of</strong> different sizes (e.g. large tesserae <strong>for</strong> the background and small ones <strong>for</strong> objects<br />
or details). Hence the shape <strong>of</strong> extracted tiles does not correspond to the shape <strong>of</strong> tesserae and the orientation<br />
is corrupted, showing no particular guideline in the mosaic.<br />
16.6 Open Issues and Research Directions in <strong>Mosaic</strong> Image <strong>Analysis</strong><br />
In this Chapter, an overview <strong>of</strong> image processing methods <strong>for</strong> images <strong>of</strong> ancient mosaics has been proposed.<br />
The analysis <strong>of</strong> such images is a difficult task due to many reasons, among them the irregularity <strong>of</strong> shapes, the<br />
particular organization <strong>of</strong> tesserae and the degradation <strong>of</strong> tiles due to their oldness. Also, the way snapshot<br />
acquisition is per<strong>for</strong>med has a crucial impact on the subsequent processings.<br />
A few works only are reported in the literature regarding the numerous problems that experts (museum<br />
curators, archeologists, art historians, etc.) face about possibly virtual restoration/lifting, conservation and<br />
cataloging <strong>of</strong> ancient mosaics. Each artwork style and period requires the development <strong>of</strong> dedicated processing<br />
methods to take into account a priori in<strong>for</strong>mation <strong>of</strong> materials, shape, color, etc. An interesting point <strong>of</strong> view
<strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong> Applications 471<br />
(a) Configuration at initialization (b) Configuration after SA<br />
(c) Energy evolution <strong>for</strong> GD and SA<br />
FIGURE 16.16<br />
Cartography <strong>of</strong> tesserae orientation. Each tessera is characterized by its center <strong>of</strong> mass (circle) and its<br />
orientation (segment crossing the circle).
472 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
(a) Configuration after SA (b) Configuration after GD<br />
FIGURE 16.17<br />
Another example <strong>of</strong> tesserae orientation cartography with (a) simulated annealing optimization and (b)<br />
gradient descent optimization.<br />
FIGURE 16.18<br />
Result <strong>of</strong> guidelines detection <strong>for</strong> the “bird” mosaic in Figure 16.12(a).
<strong>Analysis</strong> <strong>of</strong> <strong>Ancient</strong> <strong>Mosaic</strong> <strong>Images</strong> <strong>for</strong> <strong>Dedicated</strong> Applications 473<br />
FIGURE 16.19<br />
Example <strong>of</strong> querying a distant mosaic catalog by keyword.<br />
on the problem is to consider, within the processings, that a mosaic is a collection <strong>of</strong> tiles and not only a series<br />
<strong>of</strong> image pixels. If this tessera-oriented strategy seems suited in numerous cases, improvement regarding the<br />
robustness <strong>of</strong> tesserae extraction are still required, especially when mosaic tiles show a large difference in<br />
size and a high level <strong>of</strong> degradation.<br />
The algorithms described within Sections 16.3 to 16.5 are only a few basic, but crucial, steps toward<br />
complete applications expected by experts such as (i) ancient mosaic restoration with virtual “inpainting”<br />
tools taking into account main guidelines and tesserae shapes, and (ii) multi-site and distant catalog browsing<br />
with “intelligent” retrieval capabilities <strong>of</strong> semantic objects. A simplehtml interface has been designed to<br />
illustrate the possible use <strong>of</strong> basic modules <strong>for</strong> browsing, in situ, a distant database <strong>of</strong> Roman-style mosaics. A<br />
simple query using textual keywords like “human”, “face” or “animal” allows to visualize the corresponding<br />
mosaics in a distant database. Figure 16.19 shows an example <strong>of</strong> this application with the word “bird” as<br />
query. Of course, one goal <strong>of</strong> the project is to replace keywords by self-content indexing using semantic<br />
objects extraction and characterization, based on segmentation, main guidelines estimation and connected<br />
components labeling algorithms [22]. The mosaic coding strategy exposed above can be used to construct a<br />
tile-oriented CBIR application. If the CBIR can work directly on the coded stream (without reconstruction),<br />
one can expect to reduce drastically the RAM and/or hard disk memory requirements.<br />
The problem <strong>of</strong> quality <strong>of</strong> processings <strong>for</strong> segmentation and lossy coding has not been addressed in this<br />
Chapter. Particular attention should be paid to this issue in order to quantify the effectiveness <strong>of</strong> the approach.<br />
To that goal, specific quality metrics <strong>for</strong> tesserae shape and color have to be defined with experts and evaluated<br />
against image resolution. Cumulative error impact should then be analyzed along the complete processing<br />
chain to evaluate the level <strong>of</strong> accuracy <strong>for</strong> the targeted application. Note that fully automatic tools are not<br />
always required and applications piloted by users can be <strong>of</strong> interest.<br />
An interesting open issue is the construction <strong>of</strong> an invariant representation <strong>of</strong> semantic mosaic objects<br />
which is robust to the tiling organisation <strong>of</strong> shapes. Such a module will help CBIR systems to retrieve mosaic
474 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
objects independently <strong>of</strong> their orientation, pose and size in the scenes. <strong>Dedicated</strong> registration algorithms<br />
to reconstruct a complete mosaic scene from a set <strong>of</strong> sub-images can also be <strong>of</strong> interest in case <strong>of</strong> in situ<br />
acquisition <strong>of</strong> a large mosaic <strong>for</strong> example.<br />
Finally, one very promising research direction is to develop new image processing methods to automatically<br />
learn ancient mosaicists’ way <strong>of</strong> working and style (e.g., tessera color and size, organisation, guidelines,<br />
etc.). These tools could first be used to virtually fill in missing patches or restore deteriorated patches in<br />
a mosaic scene, using tessera-dedicated inpainting methods. They could also be exploited <strong>for</strong> emulating a<br />
mosaic artwork style from a raster input image using computer-aided algorithms, cf. [6] <strong>for</strong> an overview <strong>of</strong><br />
digital mosaic creation.<br />
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Index<br />
γ-Correction, 310<br />
2D, 5, 47, 51, 58, 63, 65, 74, 75, 79, 80, 86, 87, 92,<br />
95, 117, 121, 128, 129, 151, 153, 193, 220,<br />
222–225, 229, 230, 232, 234–236, 238, 240,<br />
246–248, 250, 253, 256, 258, 259, 268, 271,<br />
275, 338, 480, 485, 506, 508<br />
Objects reassembly, 258<br />
Puzzles reassembly, 258<br />
3D, 2, 5, 20, 27, 40, 41, 45, 46, 52, 53, 58, 73, 74, 79,<br />
95, 106, 109, 116, 121, 123, 124, 128, 130,<br />
139, 142, 149, 151, 153, 164, 165, 169, 174,<br />
175, 181, 189, 193, 201, 216, 225, 238, 246,<br />
275, 289, 329, 336, 366, 368, 416, 432, 442,<br />
457, 480, 499, 506<br />
Acquisition, 106, 107<br />
Catalogs, 45<br />
Investigation and diagnostics, 59<br />
Modeling, 2, 5, 24, 40, 52, 53, 74, 80, 96, 123,<br />
126<br />
Objects reconstruction, 255<br />
Puzzle Solving, 234<br />
Scanner, 128, 129<br />
Scanning pipeline, 109, 111, 113<br />
Scanning technologies, 106<br />
Visualization, 124<br />
Watermarking, 496<br />
Web, 127<br />
Aberration<br />
Astigmatism, 389<br />
Coma, 389<br />
Off-axis, 388<br />
Spherical, 383<br />
Additional parameters, 82, 85<br />
Additive/Multiplicative Model, 295<br />
Alvarez extinction theory, 385<br />
Anamorphic, 395, 396, 419<br />
Antique book, 284, 285, 299, 304<br />
Arc de Triomphe, 181<br />
ARC3D, 107, 164–166, 169, 173, 175, 181<br />
Archaeology, 1–5, 27, 253<br />
Archeomatica, 5, 21, 22, 25<br />
Area closing, 461<br />
Artificial Lighting, 158, 195, 205<br />
Asclepius, 22<br />
Asymmetry, 230, 491<br />
Authentication, 482, 483, 485, 488, 504<br />
Automatic reproduction, 52<br />
Betacam Dropout, 322, 323<br />
Blender, 6, 21, 25, 26, 43, 123, 181<br />
Blind techniques, 489, 490, 508<br />
Blotch, 282, 283, 288, 294–296, 322, 324, 325, 327–<br />
332, 336, 338<br />
Water, 282–285, 287, 288, 293, 297<br />
Bobeche, see Candelier389<br />
Bosch, Hieronymus, 410<br />
BRDF, 42, 92, 113, 120, 121, 138–144, 152, 153, 156,<br />
159, 366–369<br />
Break point, 253, 255<br />
Bruegel, Pieter, 410<br />
BTF, 367<br />
Bundle adjustment, 79, 82, 85, 87<br />
Cabanel, Alexandre, 386<br />
Camera<br />
Calibration, 79, 80, 82, 83, 85–87, 140, 196, 198<br />
Lucida, 386<br />
Campin, Robert, 383, 392, 398, 410<br />
Capacity, 95, 217, 480, 483, 485–487, 489, 503, 505<br />
Caravaggio, 394, 410<br />
Carracci, Annibale, 392<br />
CBIR, 459, 473<br />
CCD, 81, 82, 106, 289, 338, 348, 352, 368<br />
Chandelier, 384<br />
Candle holder, 389<br />
Perspective, 384<br />
515
516 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
Chiaroscuro, 400<br />
Christus, Petrus, 410<br />
CIE, 348, 350<br />
Closing by reconstruction, 288<br />
CMOS, 81, 82<br />
Color, 289<br />
Apparent, 112, 116, 120<br />
Filtering, 303<br />
Mapping, 65, 110, 112, 117, 118, 121<br />
Resampling, 120<br />
Sampling, 107, 116, 119<br />
Computer graphics, 2, 4, 5, 28, 74, 92, 120, 138, 223,<br />
366, 394, 410, 414, 415, 431, 434, 438, 496<br />
Computer-generated animations, 41<br />
Contrast, 198, 202, 282, 289, 306, 329, 331, 333, 455,<br />
458, 461, 469, 488<br />
Copyright, v, 46, 53, 479, 485<br />
Cormon, Fernand, 386<br />
Crack, 60, 62, 191, 240, 282, 283, 285, 288<br />
Cranach, Lucas, 387<br />
Crocket, 384<br />
Cultural artifacts, 245, 275<br />
Dürer, Albrecht, 392<br />
Data preservation, 45<br />
Decentering distortion, 83<br />
Depth <strong>of</strong> field, 176, 388, 395, 401, 412<br />
Detection, 79, 113, 114, 122, 198, 200, 205, 207, 210,<br />
232, 285, 287–289, 291–293, 295, 302, 314,<br />
322, 324, 326, 327, 329, 333, 441, 461, 470,<br />
482, 489–491, 498, 502, 506<br />
Dinanderie, 384<br />
Dirt, 191–193, 232, 321–324, 327, 338, 340, 341, 388,<br />
468<br />
Disparity, 173<br />
Documents reassembly, 255<br />
Donatello, 111, 400<br />
DTM/DSM, 76, 87, 89<br />
Dunhuang, 175<br />
Eroded Rock, 192, 207, 208<br />
Exterior parameters, 82<br />
Eyck, Jan van, 383–385, 387, 389–392, 397–401, 410,<br />
412, 414, 416<br />
Fabriano, Gentile da, 387, 397<br />
Film tear, 322, 323<br />
Fingerprinting, 482–484<br />
Flash, 120, 322, 457<br />
Flicker, 322, 336, 341<br />
Foxing, 282–285, 287, 293–295, 299, 300, 302–305<br />
Fracture, 55, 234, 235, 238–240, 246, 248, 250, 251,<br />
253, 255–257, 283, 284, 298<br />
Fragment, 4, 10, 12, 13, 21, 22, 55, 57, 106, 108, 216,<br />
218–220, 224, 225, 227, 231–235, 237, 238,<br />
245, 247, 250, 251, 253, 255, 259, 260, 263,<br />
266, 285, 292, 296, 432<br />
Fromentin, Eugène, 386<br />
Furtenagel, Laux, 410<br />
Fusion, 77, 78, 89, 201, 291, 329<br />
Gabor Filter Bank, 289, 291<br />
Geographic Web browsers, 45<br />
Geometric triangulation, 106<br />
GIS, 74, 93<br />
Glass plate, 283, 292, 296<br />
Gradient Vector, 238, 294, 442<br />
Graph Cuts, 257, 331, 441, 442<br />
Grid construction, 386<br />
Haghia Triada, 6<br />
Handwriting Recognition, 205<br />
Hartrick, A. S., 386<br />
Hidden notations, 485<br />
Histogram, 206, 260, 285, 303, 307, 339, 459, 469,<br />
488<br />
Hockney, David, 380, 410<br />
Holbein, Hans, 395, 396<br />
Hole filling, 110, 115<br />
Hot spots, 127<br />
Hypermedia, 127<br />
Image<br />
Matching, 76, 87, 89<br />
Quality, 306, 313, 314<br />
Image-based rendering, 121, 152, 248<br />
Image-to-geometry registration, 92, 117, 119<br />
Ingres, Jean Auguste Dominiques, 386<br />
Inpainting, 116, 253, 294, 336, 459<br />
Interactive visualization, 43, 92, 112, 202, 369<br />
Interior parameters, 82<br />
Interpolation, 111, 125, 156, 294–296, 310, 322, 334,<br />
335, 338, 341, 366<br />
Invertibility, 481, 490
Copyright Protection <strong>of</strong> Digital <strong>Images</strong> <strong>of</strong> CH 517<br />
Iron, 283, 285<br />
Iterative Closest Point, 76, 149, 254, 262<br />
Kemp, Martin, 401<br />
Kinescope Moire, 322<br />
Laser scanner, 3, 5, 21, 24, 25, 45, 75, 200, 250, 368<br />
Laser scanner, 193, 221<br />
Least squares matching, 89<br />
Light field rendering, 121<br />
Lighting analysis, 394<br />
Local contrast, 307, 310<br />
Local luminance, 307, 310<br />
LOD, 77, 93<br />
Long term preservation, 130<br />
Lotto, Lorenzo, 387–389<br />
Mahl stick, 393<br />
Mantegna, 387<br />
Mapping photos on 3D models, 119<br />
Melozzo da Forlì, 387<br />
Memling, Hans, 395, 410, 413<br />
Mesh, 40, 65, 90, 110, 112, 116, 123, 124, 139, 150,<br />
165, 193, 200, 220, 457, 496, 501<br />
MeshLab, 60, 62, 64, 65, 116, 121, 123, 124, 164,<br />
165, 181, 201<br />
Metadata, 46, 127, 129, 141<br />
Metsys, Quentin, 410<br />
Michelangelo, 43, 65, 117, 400<br />
Microcrack, 288, 289<br />
Mirror<br />
Concave, 388, 398<br />
Convex, 384, 399<br />
Modelviewer, 165<br />
Mogao caves, 175<br />
Monet, Claude, 401<br />
Moroni, Giovanni Battista, 387<br />
<strong>Mosaic</strong>, 246, 247, 282, 292, 348, 431–435, 437–442,<br />
446–449, 453–462, 464–470, 472–474<br />
Acquisition, 457<br />
<strong>Ancient</strong>, 432–434, 438–445, 448, 454, 455, 457,<br />
460, 462, 464, 466, 468, 470, 473, 474<br />
Byzantine, 454<br />
Coding, 464, 465<br />
De<strong>for</strong>mation, 457<br />
Digital, 432, 434<br />
Greek, 454<br />
Guidelines, 468<br />
Inpainting, 459<br />
Islamic, 454<br />
Material, 455<br />
Medieval, 458<br />
Photo-mosaic, 434, 448<br />
Puzzle Image <strong>Mosaic</strong>, 434<br />
Resolution, 457<br />
Restoration, 458<br />
Roman, 454, 459<br />
Segmentation, 464<br />
Symmetry, 459<br />
Zillij, 454<br />
<strong>Mosaic</strong>ing, 248, 266, 268–272, 274, 275, 352, 368<br />
Spanning tree, 269<br />
Sub-graph spanning tree, 270<br />
Multi-image matching, 89<br />
Multimedia, 3, 5, 24, 41, 50, 58, 124, 127, 480, 485,<br />
496<br />
Multiresolution, 44, 65, 75, 77, 110, 112, 119, 124,<br />
138, 140, 151, 153, 502<br />
Narrow-band, 350, 353<br />
Neugebauer, 365<br />
Nieuwenhove, Maarten van, 410, 412–414<br />
Noise, 76, 85, 110, 125, 129, 155, 165, 176, 206, 223,<br />
230, 260, 307, 322, 326, 335, 340, 352, 355,<br />
360, 439, 458, 488, 497<br />
Non-blind techniques, 489, 490, 502<br />
Non-falsifiability, 383<br />
Non-Photorealistic Rendering, 431<br />
Non-pictorial matching, 253<br />
OCR, 205, 210, 299, 300, 304–306<br />
Opening by reconstruction, 288<br />
Optical style in painting, 382, 400<br />
Opus<br />
Musivum, 438, 439<br />
Palladium, 442<br />
Vermiculatum, 438, 439<br />
Ownership <strong>of</strong> the rights, 482<br />
Parmigianino, 410<br />
Parthenon, 3, 21, 41, 138, 157<br />
Pathological Motion, 327<br />
PCA, 143, 355, 357, 361, 362<br />
Perspective
518 Digital Imaging <strong>for</strong> Cultural Heritage Preservation<br />
<strong>Analysis</strong>, 395, 396, 412<br />
Error, 383<br />
Photogrammetry, 45, 52, 55, 74, 79, 95, 111, 140,<br />
142, 250, 384<br />
Photographic Prints, 282, 285, 293<br />
Pictorial matching, 253<br />
Pinprick hole evidence, 392<br />
Pisanello, 387<br />
Poisson Reconstruction, 181<br />
Polizzello Mountain, 6<br />
Porta, Giambattista della, 389, 398<br />
Portaels, Jean-François, 386<br />
Private techniques, 489<br />
Public techniques, 489<br />
Queuing system, 172<br />
Radial distortion, 83<br />
Range map, 108, 109, 111, 113–115, 117, 123–125,<br />
368<br />
Alignment, 109, 111, 113<br />
Automatic alignment, 113<br />
Global registration, 111<br />
Local registration, 111<br />
Merge, 110, 112<br />
Reconstruction, 112<br />
Reconstruction from, 110<br />
Raphael, 387<br />
Rapid prototyping, 52<br />
Rational filter, 288, 302, 303<br />
Re-ordering, 498<br />
Re-triangulation, 497<br />
Reconstruction pipeline, 169<br />
Red chrominance matrix, 285, 287<br />
Reducing compass, 385, 392<br />
Refinement, 326, 327, 385, 499<br />
Relative alignment, 238, 257, 263, 264<br />
Relative <strong>of</strong>fset evidence, 391<br />
Rembrandt, 400<br />
Remeshing, 497, 499, 501, 506, 507<br />
Rendering, 6, 40, 42, 47, 55, 77, 92, 111, 117, 124,<br />
125, 138, 140, 144, 147, 152, 156, 159, 193,<br />
200, 246, 365, 368, 394, 431, 439, 496<br />
Restoration, 3, 5, 12, 20, 22, 25, 40, 43, 46, 57–60,<br />
62, 63, 65, 74, 78, 93, 95, 117, 138, 218,<br />
246, 248, 274, 282, 284, 289, 292–296, 299,<br />
300, 302, 303, 306, 314, 321, 322, 325, 327,<br />
329, 331, 335, 338, 340, 348, 368, 369, 454,<br />
457, 459<br />
Reversibility, 491<br />
Reynolds, Joshua, 397<br />
Robustness, 116, 169, 223, 231, 237, 309, 350, 464,<br />
479, 485, 487, 502<br />
Rock Art, 193<br />
Rock art, 191, 192, 194, 198, 200, 202, 205, 208<br />
ROD, 324, 325<br />
Roman de la Rose, 398<br />
Roto-translation, 65, 114, 296<br />
Sampling, 64, 77, 90, 106, 108, 116, 123, 129, 143,<br />
156, 171, 349, 350, 354, 361, 436, 499<br />
Scaling, 65, 85, 141, 172, 198, 248, 266, 385, 457,<br />
488, 497<br />
Scratch, 333<br />
SDI, 324<br />
Segmentation, 127, 231, 235, 257, 288, 436, 448, 457,<br />
459, 460, 462, 464–466, 473<br />
Self-calibration, 173<br />
Sepia, 285, 288, 289<br />
Sfumato, 400<br />
Shadow, 87, 116, 139, 152, 181, 193, 250, 367, 393,<br />
394, 412, 457, 465<br />
Shake, 322, 336, 341<br />
Similarity, 48, 89, 117, 119, 200, 223, 250, 255, 261,<br />
432, 440, 459, 499<br />
Simplification, 77, 93, 110, 123, 124, 129, 164, 230,<br />
497, 506<br />
simplification, 499<br />
Smith Waterman algorithm, 261<br />
Spanning tree, 266, 269–272, 274<br />
Sparkle, 322–324, 433, 454<br />
Spectacle, 381, 382, 399, 400<br />
Spectral, 77, 80, 92, 140, 146, 156, 300, 348–350,<br />
353–366, 368, 369, 506<br />
Stelae, 183<br />
Structure from motion, 80, 82<br />
Structure-from-motion, 164<br />
Surface reflection properties, 112, 116, 120, 128<br />
Surveying, 74–76, 78, 80, 93, 200, 382<br />
Tessera, 454, 455, 457, 460–466, 468–471, 473, 474<br />
Altered, 456<br />
Disaggregated, 456<br />
Extraction, 460
Copyright Protection <strong>of</strong> Digital <strong>Images</strong> <strong>of</strong> CH 519<br />
Smalti, 454<br />
Wear, 456<br />
Texture, 63, 74, 75, 80, 112, 116, 119–121, 123, 139,<br />
150, 153–155, 175, 176, 183, 193, 195, 199,<br />
238, 250, 253, 283, 288, 300, 312, 322, 335,<br />
367, 397, 419, 446, 462, 496, 499, 505, 508<br />
Thresholding, 206, 287, 288, 292, 293, 303, 334<br />
Thysia, 25<br />
TOF, 106, 107, 110, 125<br />
Tonal adjustment, 303, 304<br />
Tour, Georges de la, 393<br />
Tracing theory<br />
Burden <strong>of</strong> pro<strong>of</strong>, 386<br />
Direct tracing, 381<br />
Documentary evidence, 382<br />
Ghent symposium, 381, 401<br />
Indirect influence, 381, 402<br />
Philosophical foundations, 382<br />
Revisions, 385<br />
Timing, 387<br />
Trade secrets, 387, 398<br />
Triangulation, 21, 24, 52, 53, 75, 85, 106, 119, 124,<br />
138, 167, 193, 208, 220<br />
UAV, 81, 82, 95<br />
Upload tool, 165<br />
Usability, 40, 44, 75, 76, 90, 126, 282<br />
UV, 58, 63, 77, 299<br />
Velàzquez, Diego, 392<br />
Vermeer, Jan, 382, 412<br />
Vinegar Syndrome, 322, 323<br />
Virtual<br />
Inspector, 43, 64, 65, 125, 126<br />
Re-assembly, 55<br />
Reality, 4, 27, 28, 45, 80, 209<br />
Reconstruction, 10, 12, 53, 55, 247, 459<br />
Repainting, 57<br />
Visual communication, 3, 41, 46<br />
Voronoi, 435, 436, 441<br />
Diagrams, 435, 439, 440<br />
Water-tight reconstruction, 116<br />
Watermarking, 479, 482, 485–490, 496, 502, 506<br />
Watershed, 461<br />
Web GIS, 45<br />
Web service, 165<br />
WebGL, 127<br />
Wide-band, 350–354, 359<br />
Yellowing, 282, 284, 299, 300, 303, 306