Lecture Notes in Computer Science 10269

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Friedemann MatternETH Zurich, Zurich, Switzerland

John C. MitchellStanford University, Stanford, CA, USA

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More information about this series at http://www.springer.com/series/7412

Puneet Sharma • Filippo Maria Bianchi (Eds.)

Image Analysis20th Scandinavian Conference, SCIA 2017Tromsø, Norway, June 12–14, 2017Proceedings, Part I

123

EditorsPuneet SharmaUniversity of TromsøTromsøNorway

Filippo Maria BianchiUniversity of TromsøTromsøNorway

ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Computer ScienceISBN 978-3-319-59125-4 ISBN 978-3-319-59126-1 (eBook)DOI 10.1007/978-3-319-59126-1

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Preface

This book constitutes the refereed proceedings of the 20th Scandinavian Conference onImage Analysis, SCIA 2017, held in Tromsø, Norway, in June 2017.

The 87 revised papers presented were carefully reviewed and selected from 133submissions. The 87 accepted articles are organized in two volumes, i.e., volumes 1and 2. Volume 1 comprises topical sections on the history of SCIA, motion analysisand 3D vision, pattern detection and recognition, machine learning, and image pro-cessing and applications. Volume 2 is structured in topical sections on remote sensing,medical and biomedical image analysis, feature extraction and segmentation, and face,gesture, and multispectral analysis.

June 2017 Puneet SharmaFilippo Maria Bianchi

Organizers

Sponsors

Organization

General Chair

Robert Jenssen University of Tromsø - The Arctic Universityof Norway

Program Chairs

Puneet Sharma University of Tromsø - The Arctic Universityof Norway

Filippo Maria Bianchi University of Tromsø - The Arctic Universityof Norway

Program Co-chairs

Arnt Børre Salberg Norwegian Computing Center, NorwayJon Yngve Hardeberg Norwegian University of Science and Technology,

NorwayTrym Haavardsholm Norwegian Defence Research Establishment, Norway

Program Committee

Adrien Bartoli ISIT – CENTI, FranceAnders Heyden Lund University, SwedenAnne H. Schistad Solberg University of Oslo, NorwayArnt-Børre Norsk Regnesentral, NorwayAtsuto Maki Kungliga Tekniska Högskolan, SwedenCristina Soguero Ruiz Rey Juan Carlos University, SpainDaniele Nardi Sapienza University, ItalyDomenico Daniele Bloisi Sapienza University, ItalyEnrico Maiorino Sapienza University, ItalyErkki Oja Aalto University, FinlandFredrik Kahl Lund University, SwedenGustau Camps-Valls University of Valencia, SpainHeikki Kälviäinen Lappeenranta University of Technology, FinlandHelene Schulerud Sintef, NorwayIngela Nyström Uppsala University, SwedenJanne Heikkilä University of Oulu, FinlandJens Thielemann SINTEF, NorwayJoni Kämäräinen Tampere University of Technology, FinlandKarl Øyvind Mikalsen University of Tromsø, NorwayKjersti Engan University of Stavanger, Norway

Lasse Riis Østergaard Aalborg University, DenmarkLorenzo Livi University of Exeter, UKMads Nielsen University of Copenhagen, DenmarkMarco Loog Delft University of Technology, The NetherlandsMichael Felsberg Linkoping University, SwedenMichael Kampffmeyer University of Tromsø, NorwayNorbert Krüger University of Southern Denmark, DenmarkRasmus Paulsen Technical University of DenmarkSigurd Løkse University of Tromsø, NorwaySimone Scardapane Sapienza University, ItalyThomas Moeslund Aalborg University, Denmark

VIII Organization

Contents – Part I

History of SCIA

Image Processing and Its Hardware Support Analysisvs Synthesis - Historical Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Ewert Bengtsson

Motion Analysis and 3D Vision

Averaging Three-Dimensional Time-Varying Sequences of Rotations:Application to Preprocessing of Motion Capture Data . . . . . . . . . . . . . . . . . 17

Tomasz Hachaj, Marek R. Ogiela, Marcin Piekarczyk,and Katarzyna Koptyra

Plane Refined Structure from Motion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Branislav Micusik and Horst Wildenauer

A Time-Efficient Optimisation Framework for Parametersof Optical Flow Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Michael Stoll, Sebastian Volz, Daniel Maurer, and Andrés Bruhn

Subpixel-Precise Tracking of Rigid Objects in Real-Time . . . . . . . . . . . . . . 54Tobias Böttger, Markus Ulrich, and Carsten Steger

Wearable Gaze Trackers: Mapping Visual Attention in 3D. . . . . . . . . . . . . . 66Rasmus R. Jensen, Jonathan D. Stets, Seidi Suurmets, Jesper Clement,and Henrik Aanæs

Image Processing of Leaf Movements in Mimosa pudica . . . . . . . . . . . . . . . 77Vegard Brattland, Ivar Austvoll, Peter Ruoff, and Tormod Drengstig

Evaluation of Visual Tracking Algorithms for Embedded Devices . . . . . . . . . 88Ville Lehtola, Heikki Huttunen, Francois Christophe,and Tommi Mikkonen

Multimodal Neural Networks: RGB-D for Semantic Segmentationand Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Lukas Schneider, Manuel Jasch, Björn Fröhlich, Thomas Weber,Uwe Franke, Marc Pollefeys, and Matthias Rätsch

Uncertainty Computation in Large 3D Reconstruction . . . . . . . . . . . . . . . . . 110Michal Polic and Tomas Pajdla

Robust and Practical Depth Map Fusion for Time-of-Flight Cameras . . . . . . . 122Markus Ylimäki, Juho Kannala, and Janne Heikkilä

An Error Analysis of Structured Light Scanning of Biological Tissue . . . . . . 135Sebastian Nesgaard Jensen, Jakob Wilm, and Henrik Aanæs

Structure from Motion by Artificial Neural Networks . . . . . . . . . . . . . . . . . 146Julius Schöning, Thea Behrens, Patrick Faion, Peyman Kheiri,Gunther Heidemann, and Ulf Krumnack

Pattern Detection and Recognition

Computer Aided Detection of Prostate Cancer on Biparametric MRIUsing a Quadratic Discriminant Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Carina Jensen, Anne Sofie Korsager, Lars Boesen,Lasse Riis Østergaard, and Jesper Carl

Pipette Hunter: Patch-Clamp Pipette Detection . . . . . . . . . . . . . . . . . . . . . . 172Krisztian Koos, József Molnár, and Peter Horvath

Non-reference Image Quality Assessment for Fingervein PresentationAttack Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

Amrit Pal Singh Bhogal, Dominik Söllinger, Pauline Trung,Jutta Hämmerle-Uhl, and Andreas Uhl

Framework for Machine Vision Based Traffic Sign Inventory. . . . . . . . . . . . 197Petri Hienonen, Lasse Lensu, Markus Melander, and Heikki Kälviäinen

Copy-Move Forgery Detection Using the Segment Gradient OrientationHistogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

Ali Retha Hasoon Khayeat, Paul L. Rosin, and Xianfang Sun

BriefMatch: Dense Binary Feature Matching for Real-Time OpticalFlow Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

Gabriel Eilertsen, Per-Erik Forssén, and Jonas Unger

Robust Data Whitening as an Iteratively Re-weighted LeastSquares Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

Arun Mukundan, Giorgos Tolias, and Ondřej Chum

DEBC Detection with Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248Ian E. Nordeng, Ahmad Hasan, Doug Olsen, and Jeremiah Neubert

Object Proposal Generation Applying the Distance Dependent ChineseRestaurant Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Mikko Lauri and Simone Frintrop

X Contents – Part I

Object Tracking via Pixel-Wise and Block-Wise Sparse Representation . . . . . 273Pouria Navaei, Mohammad Eslami, and Farah Torkamani-Azar

Supervised Approaches for Function Prediction of Proteins ContactNetworks from Topological Structure Information . . . . . . . . . . . . . . . . . . . . 285

Alessio Martino, Enrico Maiorino, Alessandro Giuliani,Mauro Giampieri, and Antonello Rizzi

Top-Down Deep Appearance Attention for Action Recognition. . . . . . . . . . . 297Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost van de Weijer,and Jorma Laaksonen

Machine Learning

Soft Margin Bayes-Point-Machine Classification via AdaptiveDirection Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313

Karsten Vogt and Jörn Ostermann

ConvNet Regression for Fingerprint Orientations. . . . . . . . . . . . . . . . . . . . . 325Patrick Schuch, Simon-Daniel Schulz, and Christoph Busch

Domain Transfer for Delving into Deep Networks Capacityto De-Abstract Art. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337

Corneliu Florea, Mihai Badea, Laura Florea, and Constantin Vertan

Foreign Object Detection in Multispectral X-ray Images of Food ItemsUsing Sparse Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350

Gudmundur Einarsson, Janus N. Jensen, Rasmus R. Paulsen,Hildur Einarsdottir, Bjarne K. Ersbøll, Anders B. Dahl,and Lars Bager Christensen

Sparse Approximation by Matching PursuitUsing Shift-Invariant Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362

Karl Skretting and Kjersti Engan

Diagnosis of Broiler Livers by Classifying Image Patches . . . . . . . . . . . . . . 374Anders Jørgensen, Jens Fagertun, and Thomas B. Moeslund

Historical Document Binarization Combining Semantic Labelingand Graph Cuts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

Kalyan Ram Ayyalasomayajula and Anders Brun

Convolutional Neural Networks for Segmentation and Object Detectionof Human Semen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

Malte S. Nissen, Oswin Krause, Kristian Almstrup, Søren Kjærulff,Torben T. Nielsen, and Mads Nielsen

Contents – Part I XI

Convolutional Neural Networks for False Positive Reductionof Automatically Detected Cilia in Low Magnification TEM Images . . . . . . . 407

Anindya Gupta, Amit Suveer, Joakim Lindblad, Anca Dragomir,Ida-Maria Sintorn, and Nataša Sladoje

Deep Kernelized Autoencoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi,Robert Jenssen, and Lorenzo Livi

Spectral Clustering Using PCKID – A Probabilistic Cluster Kernelfor Incomplete Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

Sigurd Løkse, Filippo M. Bianchi, Arnt-Børre Salberg,and Robert Jenssen

Automatic Emulation by Adaptive Relevance Vector Machines. . . . . . . . . . . 443Luca Martino, Jorge Vicent, and Gustau Camps-Valls

Image Processing and Applications

Deep Learning for Polar Bear Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . 457Scott Sorensen, Wayne Treible, Leighanne Hsu, Xiaolong Wang,Andrew R. Mahoney, Daniel P. Zitterbart, and Chandra Kambhamettu

Crowd Counting Based on MMCNN in Still Images . . . . . . . . . . . . . . . . . . 468Tao Wang, Guohui Li, Jun Lei, Shuohao Li, and Shukui Xu

Generation and Authoring of Augmented Reality Terrains ThroughReal-Time Analysis of Map Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480

Theodore Panagiotopoulos, Gerasimos Arvanitis,Konstantinos Moustakas, and Nikos Fakotakis

Solution of Pure Scattering Radiation Transport Equation (RTE)Using Finite Difference Method (FDM) . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

Hassan A. Khawaja

Optimised Anisotropic Poisson Denoising . . . . . . . . . . . . . . . . . . . . . . . . . 502Georg Radow, Michael Breuß, Laurent Hoeltgen, and Thomas Fischer

Augmented Reality Interfaces for Additive Manufacturing . . . . . . . . . . . . . . 515Eythor R. Eiriksson, David B. Pedersen, Jeppe R. Frisvad,Linda Skovmand, Valentin Heun, Pattie Maes, and Henrik Aanæs

General Cramér-von Mises, a Helpful Ally for Transparent ObjectInspection Using Deflection Maps? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526

Johannes Meyer, Thomas Längle, and Jürgen Beyerer

XII Contents – Part I

Dynamic Exploratory Search in Content-Based Image Retrieval . . . . . . . . . . 538Joel Pyykkö and Dorota Głowacka

Robust Anomaly Detection Using Reflectance Transformation Imagingfor Surface Quality Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550

Gilles Pitard, Gaëtan Le Goïc, Alamin Mansouri, Hugues Favrelière,Maurice Pillet, Sony George, and Jon Yngve Hardeberg

Block-Permutation-Based Encryption Scheme with EnhancedColor Scrambling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562

Shoko Imaizumi, Takeshi Ogasawara, and Hitoshi Kiya

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575

Contents – Part I XIII

Contents – Part II

Feature Extraction and Segmentation

Simplification of Polygonal Chains by Enforcing Few DistinctiveEdge Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Melanie Pohl, Jochen Meidow, and Dimitri Bulatov

Leaflet Free Edge Detection for the Automatic Analysis of ProstheticHeart Valve Opening and Closing Motion Patterns from High SpeedVideo Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Maryam Alizadeh, Melissa Cote, and Alexandra Branzan Albu

Max-Margin Learning of Deep Structured Modelsfor Semantic Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Måns Larsson, Jennifer Alvén, and Fredrik Kahl

Robust Abdominal Organ Segmentation Using RegionalConvolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Måns Larsson, Yuhang Zhang, and Fredrik Kahl

Detecting Chest Compression Depth Using a Smartphone Cameraand Motion Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Øyvind Meinich-Bache, Kjersti Engan, Trygve Eftestøl, and Ivar Austvoll

Feature Space Clustering for Trabecular Bone Segmentation. . . . . . . . . . . . . 65Benjamin Klintström, Eva Klintström, Örjan Smedby,and Rodrigo Moreno

Airway-Tree Segmentation in Subjects with Acute RespiratoryDistress Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Kristína Lidayová, Duván Alberto Gómez Betancur, Hans Frimmel,Marcela Hernández Hoyos, Maciej Orkisz, and Örjan Smedby

Context Aware Query Image Representation for Particular Object Retrieval . . . 88Zakaria Laskar and Juho Kannala

Granulometry-Based Trabecular Bone Segmentation . . . . . . . . . . . . . . . . . . 100Manish Chowdhury, Benjamin Klintström, Eva Klintström,Örjan Smedby, and Rodrigo Moreno

Automatic Segmentation of Abdominal Fat in MRI-Scans,Using Graph-Cuts and Image Derived Energies. . . . . . . . . . . . . . . . . . . . . . 109

Anders Nymark Christensen, Christian Thode Larsen,Camilla Maria Mandrup, Martin Bæk Petersen, Rasmus Larsen,Knut Conradsen, and Vedrana Andersen Dahl

Remote Sensing

Two-Source Surface Reconstruction Using Polarisation . . . . . . . . . . . . . . . . 123Gary A. Atkinson

Synthetic Aperture Radar (SAR) Monitoring of Avalanche Activity:An Automated Detection Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

H. Vickers, M. Eckerstorfer, E. Malnes, and A. Doulgeris

Canonical Analysis of Sentinel-1 Radar and Sentinel-2 Optical Data . . . . . . . 147Allan A. Nielsen and Rasmus Larsen

A Noncentral and Non-Gaussian Probability Model for SAR Data . . . . . . . . 159Anca Cristea, Anthony P. Doulgeris, and Torbjørn Eltoft

Unsupervised Multi-manifold Classification of Hyperspectral RemoteSensing Images with Contractive Autoencoder . . . . . . . . . . . . . . . . . . . . . . 169

Aidin Hassanzadeh, Arto Kaarna, and Tuomo Kauranne

A Clustering Approach to Heterogeneous Change Detection . . . . . . . . . . . . . 181Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser,Robert Jenssen, Filippo Maria Bianchi, Sebastiano Serpico,and Gregoire Mercier

Large-Scale Mapping of Small Roads in Lidar ImagesUsing Deep Convolutional Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . 193

Arnt-Børre Salberg, Øivind Due Trier, and Michael Kampffmeyer

Physics-Aware Gaussian Processes for Earth Observation. . . . . . . . . . . . . . . 205Gustau Camps-Valls, Daniel H. Svendsen, Luca Martino,Jordi Muñoz-Marí, Valero Laparra, Manuel Campos-Taberner,and David Luengo

Medical and Biomedical Image Analysis

Automatic Segmentation of Bone Tissue from Computed TomographyUsing a Volumetric Local Binary Patterns Based Method. . . . . . . . . . . . . . . 221

Jukka Kaipala, Miguel Bordallo López, Simo Saarakkala,and Jérôme Thevenot

XVI Contents – Part II

Local Adaptive Wiener Filtering for Class Averaging in SingleParticle Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

Ali Abdollahzadeh, Erman Acar, Sari Peltonen, and Ulla Ruotsalainen

Comparison of Concave Point Detection Methods for Overlapping ConvexObjects Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

Sahar Zafari, Tuomas Eerola, Jouni Sampo, Heikki Kälviäinen,and Heikki Haario

Decoding Gene Expression in 2D and 3D . . . . . . . . . . . . . . . . . . . . . . . . . 257Maxime Bombrun, Petter Ranefall, Joakim Lindblad, Amin Allalou,Gabriele Partel, Leslie Solorzano, Xiaoyan Qian, Mats Nilsson,and Carolina Wählby

Estimation of Heartbeat Peak Locations and Heartbeat Ratefrom Facial Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Mohammad A. Haque, Kamal Nasrollahi, and Thomas B. Moeslund

Segmentation of Multiple Structures in Chest RadiographsUsing Multi-task Fully Convolutional Networks . . . . . . . . . . . . . . . . . . . . . 282

Chunliang Wang

A Novel Method for Automatic Localization of Joint Area on KneePlain Radiographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290

Aleksei Tiulpin, Jerome Thevenot, Esa Rahtu, and Simo Saarakkala

Semi-automatic Method for Intervertebral Kinematics Measurementin the Cervical Spine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302

Anne Krogh Nøhr, Louise Pedersen Pilgaard, Bolette Dybkjær Hansen,Rasmus Nedergaard, Heidi Haavik, Rene Lindstroem,Maciej Plocharski, and Lasse Riis Østergaard

Memory Effects in Subjective Quality Assessment of X-Ray Images . . . . . . . 314Victor Landre, Marius Pedersen, and Dag Waaler

Classification of Fingerprints Captured Using OpticalCoherence Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326

Ctirad Sousedik, Ralph Breithaupt, and Patrick Bours

Interpolation from Grid Lines: Linear, Transfinite and Weighted Method . . . . 338Anne-Sofie Wessel Lindberg, Thomas Martini Jørgensen,and Vedrana Andersen Dahl

Automated Pain Assessment in Neonates . . . . . . . . . . . . . . . . . . . . . . . . . . 350Ghada Zamzmi, Chih-Yun Pai, Dmitry Goldgof, Rangachar Kasturi,Yu Sun, and Terri Ashmeade

Contents – Part II XVII

Enhancement of Cilia Sub-structures by Multiple Instance Registrationand Super-Resolution Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362

Amit Suveer, Nataša Sladoje, Joakim Lindblad, Anca Dragomir,and Ida-Maria Sintorn

Faces, Gestures and Multispectral Analysis

Residual vs. Inception vs. Classical Networks for Low-ResolutionFace Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377

Christian Herrmann, Dieter Willersinn, and Jürgen Beyerer

Visual Language Identification from Facial Landmarks . . . . . . . . . . . . . . . . 389Radim Špetlík, Jan Čech, Vojtěch Franc, and Jiří Matas

HDR Imaging Pipeline for Spectral Filter Array Cameras. . . . . . . . . . . . . . . 401Jean-Baptiste Thomas, Pierre-Jean Lapray, and Pierre Gouton

Thistle Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413Søren I. Olsen, Jon Nielsen, and Jesper Rasmussen

An Image-Based Method for Objectively Assessing Injection MouldedPlastic Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426

Morten Hannemose, Jannik Boll Nielsen, László Zsíros,and Henrik Aanæs

Creating Ultra Dense Point Correspondence Over the Entire Human Head . . . 438Rasmus R. Paulsen, Kasper Korsholm Marstal, Søren Laugesen,and Stine Harder

Collaborative Representation of Statistically Independent Filters’ Response:An Application to Face Recognition Under Illicit Drug Abuse Alterations . . . 448

Raghavendra Ramachandra, Kiran Raja, Sushma Venkatesh,and Christoph Busch

Multispectral Constancy Based on Spectral Adaptation Transform . . . . . . . . . 459Haris Ahmad Khan, Jean Baptiste Thomas, and Jon Yngve Hardeberg

State Estimation of the Performance of Gravity Tables Using MultispectralImage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

Michael A.E. Hansen, Ananda S. Kannan, Jacob Lund, Peter Thorn,Srdjan Sasic, and Jens M. Carstensen

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481

XVIII Contents – Part II