Image processing Books

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Book SynopsisThis volume helps to fill the gap between data analytics, image processing, and soft computing practices. Soft computing methods are used to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality research that presents the current trends, advanced methods, and hybridized techniques relating to data analytics and intelligent systems. The book also features case studies related to medical diagnosis with the use of image processing and soft computing algorithms in particular models. Providing extensive coverage of biometric systems, soft computing, image processing, artificial intelligence, and data analytics, the chapter authors discuss the latest research issues, present solutions to research problems, and look at comparative analysis with earlier results. Topics include some of the most important challenges and discoveries in intelligent systems today, such as computer vision concepts and image identification, data analysis and computational paradigms, deep learning techniques, face and speaker recognition systems, and more.Table of ContentsPart 1: Biometric Systems And Image Processing 1. Intelligent Techniques: An Overview 2. A Survey on Artificial Intelligence Techniques Used in Bio-Metric Systems 3. Speech-Based Biometric Using Odia Phonetics 4. Deep Learning Techniques to Classify and Analyze Medical Imaging Data 5. Face Recognition System: An Overview 6. An Overview on the Concept of Speaker Recognition 7. Analysis of a Unimodal and Multimodal Biometric System Part 2: Soft Computing And Data Analytics 8. A Heuristic Approach of Parameter Tuning in a Smote-Based Preprocessing Algorithm for Imbalanced Ordinal Classification 9. Aspects of Deep Learning: Hyper-Parameter Tuning, Regularization, and Normalization 10. Super-Resolution of Reconstruction of Infrared Images Adopting Counter Neural Networks 11. High-End Tools and Technologies for Managing Data in the Age of Big Data 12. An AI-Based Chatbot Using Deep Learning Part 3: Intelligent Systems And Hybrid Systems 13. A Real-Time Data Analytics-Based Crop Diseases Recognition System 14. Image Caption Generation with Beam Search

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Book SynopsisChange Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.Table of ContentsContents Preface xi Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1 Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU and Xiaohua TONG 1.1. Introduction 1 1.2. Unsupervised change detection in multispectral images 3 1.2.1.Relatedconcepts 3 1.2.2.Openissuesandchallenges 7 1.2.3. Spectral–spatial unsupervised CD techniques 7 1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9 1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9 1.3.2. Multiscale morphological compressed change vector analysis 11 1.3.3. Superpixel-level compressed change vector analysis 15 1.4.Datasetdescriptionandexperimentalsetup 18 1.4.1.Datasetdescription 18 1.4.2.Experimentalsetup 22 1.5.Resultsanddiscussion 24 1.5.1.ResultsontheXuzhoudataset 24 1.5.2. Results on the Indonesia tsunami dataset 24 xv 1.6.Conclusion 28 1.7.Acknowledgements 29 1.8.References 29 Chapter 2 Change Detection in Time Series of Polarimetric SAR Images 35 Knut CONRADSEN, Henning SKRIVER, MortonJ.CANTY andAllanA.NIELSEN 2.1. Introduction 35 2.1.1.Theproblem 36 2.1.2 Important concepts illustrated by means of the gamma distribution 39 2.2.Testtheoryandmatrixordering 45 2.2.1. Test for equality of two complex Wishart distributions 45 2.2.2. Test for equality of k-complex Wishart distributions 47 2.2.3. The block diagonal case 49 2.2.4.TheLoewnerorder 52 2.3.Thebasicchangedetectionalgorithm 53 2.4.Applications 55 2.4.1.Visualizingchanges 58 2.4.2.Fieldwisechangedetection 59 2.4.3. Directional changes using the Loewner ordering 62 2.4.4. Software availability 65 2.5.References 70 Chapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73 Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ, Arnaud BRELOY and Frédéric PASCAL 3.1. Introduction 73 3.2.Datasetdescription 76 3.3.StatisticalmodelingofSARimages 77 3.3.1.Thedata 77 3.3.2.Gaussianmodel 77 3.3.3.Non-Gaussianmodeling 83 3.4.Dissimilaritymeasures 84 3.4.1.Problemformulation 84 3.4.2. Hypothesis testing statistics 85 3.4.3.Information-theoreticmeasures 87 3.4.4.Riemanniangeometrydistances 89 3.4.5.Optimaltransport 90 3.4.6.Summary 91 3.4.7. Results of change detectors on the UAVSAR dataset 91 3.5. Change detection based on structured covariances 94 3.5.1. Low-rank Gaussian change detector 96 3.5.2. Low-rank compound Gaussian change detector 97 3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100 3.6.Conclusion 102 3.7.References 103 Chapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109 Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN and Lionel BOMBRUN 4.1. Introduction 109 4.2.Parametricmodelingofconvnetfeatures 110 4.3.Anomalydetectioninimagetimeseries 113 4.4.Functionalimagetimeseriesclustering 119 4.5.Conclusion 123 4.6.References 123 Chapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127 Fatima KARBOU, Guillaume JAMES, Philippe DURAND and Abdourrahmane M. ATTO 5.1. Introduction 127 5.2.Testareaanddata 129 5.3.WetsnowdetectionusingSentinel-1 129 5.4.Metricstodetectwetsnow 133 5.5.Discussion 138 5.6.Conclusion 143 5.7.Acknowledgements 143 5.8.References 143 Chapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145 Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC and Pedro MORETTIN 6.1. Introduction 145 6.2. Random field model of a cyclone texture 148 6.2.1.Cyclonetexturefeature 149 6.2.2. Wavelet-based power spectral densities and cyclone fields 150 6.2.3. Fractional spectral power decay model 153 6.3.Cyclonefieldeyedetectionandtracking 157 6.3.1.Cycloneeyedetection 157 6.3.2.Dynamicfractalfieldeyetracking 158 6.4. Cyclone field intensity evolution prediction 159 6.5.Discussion 161 6.6.Acknowledgements 163 6.7.References 163 Chapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167 Minh-Tan PHAM and Grégoire MERCIER 7.1. Introduction 167 7.2. Texture representation and characterization using local extrema 169 7.2.1.Motivationandapproach 169 7.2.2. Local extrema keypoints within SAR images 172 7.3.Unsupervisedchangedetection 175 7.3.1. Proposed framework 175 7.3.2. Weighted graph construction from keypoints 176 7.3.3.Changemeasure(CM)generation 178 7.4.Experimentalstudy 179 7.4.1. Data description and evaluation criteria 179 7.4.2.Changedetectionresults 181 7.4.3.Sensitivitytoparameters 185 7.4.4.ComparisonwiththeNLMmodel 188 7.4.5. Analysis of the algorithm complexity 191 7.5.Applicationtoglacierflowmeasurement 192 7.5.1. Proposed method 193 7.5.2.Results 194 7.6.Conclusion 196 7.7.References 197 Chapter 8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201 Andrea GARZELLI and Claudia ZOPPETTI 8.1. Introduction 201 8.2. Proposed method 203 8.2.1.Testsiteanddata 206 8.3.SARprocessing 209 8.4.Opticalprocessing 215 8.5.Combinationlayer 217 8.6.Results 219 8.7.Conclusion 220 8.8.References 221 Chapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223 Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE 9.1. Introduction 223 9.2. Overview of the change detection problem 225 9.2.1. Change detection methods for multispectral images 227 9.2.2. Challenges addressed in this chapter 230 9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231 9.3.1. Magnitude image statistical mixture model 231 9.3.2.Bayesiandecision 233 9.3.3. Numerical approach to parameter estimation 234 9.4. A compound multiclass statistical model of the difference image 239 9.4.1. Difference image statistical mixture model 240 9.4.2. Magnitude image statistical mixture model 245 9.4.3.Bayesiandecision 248 9.4.4. Numerical approach to parameter estimation 249 9.5.Experimentalresults 253 9.5.1.Datasetdescription 253 9.5.2.Experimentalsetup 256 9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256 9.5.4. Test 2: Multiclass Rician mixture model 260 9.6.Conclusion 266 9.7.References 267 List of Authors 275 Index 277 Summary of Volume 2 281

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Book SynopsisChange Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253

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Book SynopsisThis book provides a modern, algorithmic introduction to digital image p- cessing, designed to be used both by learners desiring a ?rm foundation on which to build and practitioners in search of critical analysis and modern - plementations of the most important techniques. This updated and enhanced paperbackedition ofourcomprehensivetextbookDigital Image Processing: An Algorithmic Approach Using Java packages the original material into a series of compactvolumes, therebysupporting a ?exiblesequenceofcoursesindigital image processing. Tailoring the contents to the scope of individual semester courses is also an attempt to provide a?ordable (and "backpack-compatible") textbooks without comprimising the quality and depth of content. Oneapproachtolearninganewlanguageistobecomeconversantinthecore vocabulary and to start using it right away. At ?rst, you may only know how to ask for directions, order co?ee, and so on, but once you become con?dent with the core, you will start engaging others in "conversations" and rapidly learn how to get things done. This step-by-step approach works equally well in many areas of science and engineering. In this ?rst volume, ostentatiously titled Fundamental Techniques,wehave attemptedtocompilethecore"vocabulary" ofdigitalimageprocessing,starting from the basic concepts and elementary properties of digital images through simple statistics and point operations, fundamental ? ltering techniques, loc- ization of edges and contours, and basic operations on color images. Mastering these most commonly used techniques and algorithms will enable you to start being productive right away.Trade ReviewFrom the reviews: "This slim volume is the first of a three-volume set. … the book’s overall coverage is sound--well written, with plenty of illustrative examples and elegant diagrams. … this book is a fine introduction to image processing, and some topics--color, in particular--are very well done indeed. … In summary, this is mostly a fine text. … with the addition of more exercises, this would be an excellent introduction to the field." (Alasdair McAndrew, ACM Computing Reviews, August, 2009)Table of ContentsDigital Images.- ImageJ.- Histograms.- Point Operations.- Filters.- Edges and Contours.- Morphological Filters.- Color Images.

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Book SynopsisComputer-aided automatic processing of images requires the control of a series of operations, which this book analyzes. Knowing the statistical properties of images, sampling them to reduce the observable world to a series of discrete values, restoring images in order to correct degradations – all these operations are explained here, together with the mathematical tools they require. Topics covered include fractal representation, mathematical morphology, wavelet representations and the detection and description of contours and shapes.Table of ContentsChapter 1. Statistical properties of images (Henri Maître). Chapter 2. Image sampling and fractal representation (Henri Maître). Chapter 3. Discrete representations (Isabelle Bloch). Chapter 4. Restoration of images (Henri Maître). Chapter 5. Mathematical morphology (Isabelle Bloch). Chapter 6. Markov fields (Florence Tupin and Marc Sigelle). Chapter 7. Wavelets and image processing (Béatrice Pesquet-Popescu and Jean-Christophe Pesquet). Chapter 8. Partial differential equations (Yann Gousseau). Chapter 9. Preprocessing (Henri Maître). Chapter 10. Detection of contours in images (Henri Maître). Chapter 11. Segmentation by regions (Henri Maître). Chapter 12. Textures (Henri Maître). Chapter 13. Description of contours and shapes (Henri Maître). List of Authors. Index.

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Book SynopsisDuring the last decade, image and signal compression for storage and transmission purpose has seen a great expansion. But what about medical data compression? Should a medical image or a physiological signal be processed and compressed like any other data? The progress made in imaging systems, storing systems and telemedicine makes compression in this field particularly interesting. However, this compression has to be adapted to the specificities of biomedical data which contain diagnosis information. As such, this book offers an overview of compression techniques applied to medical data, including: physiological signals, MRI, X-ray, ultrasound images, static and dynamic volumetric images. Researchers, clinicians, engineers and professionals in this area, along with postgraduate students in the signal and image processing field, will find this book to be of great interest.Table of ContentsPreface xiii Chapter 1. Relevance of Biomedical Data Compression 1 Jean-Yves TANGUY, Pierre JALLET, Christel LE BOZEC and Guy FRIJA 1.1. Introduction 1 1.2. The management of digital data using PACS 2 1.2.1. Usefulness of PACS 2 1.2.2. The limitations of installing a PACS 3 1.3. The increasing quantities of digital data 4 1.3.1. An example from radiology 4 1.3.2. An example from anatomic pathology 6 1.3.3. An example from cardiology with ECG 7 1.3.4. Increases in the number of explorative examinations 8 1.4. Legal and practical matters 8 1.5. The role of data compression. 9 1.6. Diagnostic quality 10 1.6.1. Evaluation 10 1.6.2. Reticence 11 1.7. Conclusion 12 1.8. Bibliography 12 Chapter 2. State of the Art of Compression Methods 15 Atilla BASKURT 2.1. Introduction 15 2.2. Outline of a generic compression technique 16 2.2.1. Reducing redundancy 17 2.2.2. Quantizing the decorrelated information 18 2.2.3. Coding the quantized values 18 2.2.4. Compression ratio, quality evaluation 20 2.3. Compression of still images 21 2.3.1. JPEG standard 22 2.3.1.1. Why use DCT? 22 2.3.1.2. Quantization 24 2.3.1.3. Coding 24 2.3.1.4. Compression of still color images with JPEG 25 2.3.1.5. JPEG standard: conclusion 26 2.3.2. JPEG 2000 standard 27 2.3.2.1. Wavelet transform 27 2.3.2.2. Decomposition of images with the wavelet transform 27 2.3.2.3. Quantization and coding of subbands 29 2.3.2.4. Wavelet-based compression methods, serving as references 30 2.3.2.5. JPEG 2000 standard 31 2.4. The compression of image sequences 33 2.4.1. DCT-based video compression scheme 34 2.4.2. A history of and comparison between video standards 36 2.4.3. Recent developments in video compression 38 2.5. Compressing 1D signals 38 2.6. The compression of 3D objects 39 2.7. Conclusion and future developments 39 2.8. Bibliography 40 Chapter 3. Specificities of Physiological Signals and Medical Images 43 Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Jean-Yves TANGUY, Elsa ANGELINI, Christel LE BOZEC and Jean-Jacques LE JEUNE 3.1. Introduction 43 3.2. Characteristics of physiological signals 44 3.2.1. Main physiological signals 44 3.2.1.1. Electroencephalogram (EEG) 44 3.2.1.2. Evoked potential (EP) 45 3.2.1.3. Electromyogram (EMG) 45 3.2.1.4. Electrocardiogram (ECG) 46 3.2.2. Physiological signal acquisition 46 3.2.3. Properties of physiological signals 46 3.2.3.1. Properties of EEG signals 46 3.2.3.2. Properties of ECG signals 48 3.3. Specificities of medical images 50 3.3.1. The different features of medical imaging formation processes 50 3.3.1.1. Radiology 51 3.3.1.2. Magnetic resonance imaging (MRI) 54 3.3.1.3. Ultrasound 58 3.3.1.4. Nuclear medicine 62 3.3.1.5. Anatomopathological imaging 66 3.3.1.6. Conclusion 68 3.3.2. Properties of medical images 69 3.3.2.1. The size of images 70 3.3.2.2. Spatial and temporal resolution 71 3.3.2.3. Noise in medical images 72 3.4. Conclusion 73 3.5. Bibliography 74 Chapter 4. Standards in Medical Image Compression 77 Bernard GIBAUD and Joël CHABRIAIS 4.1. Introduction 77 4.2. Standards for communicating medical data 79 4.2.1. Who creates the standards, and how? 79 4.2.2. Standards in the healthcare sector 80 4.2.2.1. Technical committee 251 of CEN 80 4.2.2.2. Technical committee 215 of the ISO 80 4.2.2.3. DICOM Committee 80 4.2.2.4.Health Level Seven (HL7) 85 4.2.2.5. Synergy between the standards bodies 86 4.3. Existing standards for image compression 87 4.3.1. Image compression 87 4.3.2. Image compression in the DICOM standard 89 4.3.2.1. The coding of compressed images in DICOM 89 4.3.2.2. The types of compression available 92 4.3.2.3. Modes of access to compressed data 95 4.4. Conclusion 99 4.5. Bibliography 99 Chapter 5. Quality Assessment of Lossy Compressed Medical Images 101 Christine CAVARO-MÉNARD, Patrick LE CALLET, Dominique BARBA and Jean-Yves TANGUY 5.1. Introduction 101 5.2. Degradations generated by compression norms and their consequences in medical imaging 102 5.2.1. The block effect 102 5.2.2. Fading contrast in high spatial frequencies 103 5.3. Subjective quality assessment 105 5.3.1. Protocol evaluation 105 5.3.2. Analyzing the diagnosis reliability 106 5.3.2.1. ROC analysis 108 5.3.2.2. Analyses that are not based on the ROC method 111 5.3.3. Analyzing the quality of diagnostic criteria 111 5.3.4. Conclusion 114 5.4. Objective quality assessment 114 5.4.1. Simple signal-based metrics 115 5.4.2. Metrics based on texture analysis 115 5.4.3. Metrics based on a model version of the HVS 117 5.4.3.1. Luminance adaptation 117 5.4.3.2. Contrast sensivity 118 5.4.3.3. Spatio-frequency decomposition 118 5.4.3.4. Masking effect 119 5.4.3.5. Visual distortion measures 120 5.4.4. Analysis of the modification of quantitative clinical parameters 123 5.5. Conclusion 125 5.6. Bibliography 125 Chapter 6. Compression of Physiological Signals 129 Amine NAÏT-ALI 6.1. Introduction 129 6.2. Standards for coding physiological signals 130 6.2.1. CEN/ENV 1064 Norm 130 6.2.2. ASTM 1467 Norm 130 6.2.3. EDF norm 130 6.2.4. Other norms 131 6.3. EEG compression 131 6.3.1. Time-domain EEG compression 131 6.3.2. Frequency-domain EEG compression 132 6.3.3. Time-frequency EEG compression 132 6.3.4. Spatio-temporal compression of the EEG 132 6.3.5. Compression of the EEG by parameter extraction 132 6.4. ECG compression 133 6.4.1. State of the art 133 6.4.2. Evaluation of the performances of ECG compression methods 134 6.4.3. ECG pre-processing 135 6.4.4. ECG compression for real-time transmission 136 6.4.4.1. Time domain ECG compression 136 6.4.4.2. Compression of the ECG in the frequency domain 141 6.4.5. ECG compression for storage 144 6.4.5.1. Synchronization and polynomial modeling 145 6.4.5.2. Synchronization and interleaving 149 6.4.5.3. Compression of the ECG signal using the JPEG 2000 standard 150 6.5. Conclusion 150 6.6. Bibliography 151 Chapter 7. Compression of 2D Biomedical Images 155 Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Olivier DEFORGES and Marie BABEL 7.1. Introduction 155 7.2. Reversible compression of medical images 156 7.2.1. Lossless compression by standard methods 156 7.2.2. Specific methods of lossless compression 157 7.2.3. Compression based on the region of interest 158 7.2.4. Conclusion 160 7.3. Lossy compression of medical images 160 7.3.1. Quantization of medical images 160 7.3.1.1. Principles of vector quantization 161 7.3.1.2. A few illustrations 161 7.3.1.3. Balanced tree-structured vector quantization 163 7.3.1.4. Pruned tree-structured vector quantization 163 7.3.1.5. Other vector quantization methods applied to medical images 163 7.3.2. DCT-based compression of medical images 164 7.3.3. JPEG 2000 lossy compression of medical images 167 7.3.3.1. Optimizing the JPEG 2000 parameters for the compression of medical images 167 7.3.4. Fractal compression 170 7.3.5. Some specific compression methods 171 7.3.5.1. Compression of mammography images 171 7.3.5.2. Compression of ultrasound images 172 7.4. Progressive compression of medical images 173 7.4.1. State-of-the-art progressive medical image compression techniques 173 7.4.2. LAR progressive compression of medical images 174 7.4.2.1. Characteristics of the LAR encoding method 174 7.4.2.2. Progressive LAR encoding 176 7.4.2.3. Hierarchical region encoding 178 7.5. Conclusion 181 7.6. Bibliography 182 Chapter 8. Compression of Dynamic and Volumetric Medical Sequences 187 Azza OULED ZAID, Christian OLIVIER and Amine NAÏT-ALI 8.1. Introduction 187 8.2. Reversible compression of (2D+t) and 3D medical data sets 190 8.3. Irreversible compression of (2D+t) medical sequences 192 8.3.1. Intra-frame lossy coding 192 8.3.2. Inter-frame lossy coding 194 8.3.2.1. Conventional video coding techniques 194 8.3.2.2. Modified video coders 195 8.3.2.3. 2D+t wavelet-based coding systems limits 195 8.4. Irreversible compression of volumetric medical data sets 196 8.4.1. Wavelet-based intra coding 196 8.4.2. Extension of 2D transform-based coders to 3D data 197 8.4.2.1. 3D DCT coding 197 8.4.2.2. 3D wavelet-based coding based on scalar or vector quantization 198 8.4.2.3. Embedded 3D wavelet-based coding 199 8.4.2.4. Object-based 3D embedded coding 204 8.4.2.5. Performance assessment of 3D embedded coders 205 8.5. Conclusion 207 8.6. Bibliography 208 Chapter 9. Compression of Static and Dynamic 3D Surface Meshes 211 Khaled MAMOU, Françoise PRÊTEUX, Rémy PROST and Sébastien VALETTE 9.1. Introduction 211 9.2. Definitions and properties of triangular meshes 213 9.3. Compression of static meshes 216 9.3.1. Single resolution mesh compression 217 9.3.1.1. Connectivity coding 217 9.3.1.2. Geometry coding 218 9.3.2. Multi-resolution compression 219 9.3.2.1. Mesh simplification methods 219 9.3.2.2. Spectral methods 219 9.3.2.3. Wavelet-based approaches 220 9.4. Compression of dynamic meshes 229 9.4.1. State of the art 230 9.4.1.1. Prediction-based techniques 230 9.4.1.2. Wavelet-based techniques 231 9.4.1.3. Clustering-based techniques 233 9.4.1.4. PCA-based techniques 234 9.4.1.5. Discussion 234 9.4.2. Application to dynamic 3D pulmonary data in computed tomography 236 9.4.2.1. Data 236 9.4.2.2. Proposed approach 237 9.4.2.3. Results 238 9.5. Conclusion 239 9.6. Appendices 240 9.6.1. Appendix A: mesh via the MC algorithm 240 9.7. Bibliography 241 Chapter 10. Hybrid Coding: Encryption-Watermarking-Compression for Medical Information Security 247 William PUECH and Gouenou COATRIEUX 10.1. Introduction 247 10.2. Protection of medical imagery and data 248 10.2.1. Legislation and patient rights 248 10.2.2. A wide range of protection measures 249 10.3. Basics of encryption algorithms 251 10.3.1. Encryption algorithm classification 251 10.3.2. The DES encryption algorithm 252 10.3.3. The AES encryption algorithm 253 10.3.4. Asymmetric block system: RSA 254 10.3.5. Algorithms for stream ciphering 255 10.4. Medical image encryption 257 10.4.1. Image block encryption 258 10.4.2. Coding images by asynchronous stream cipher 258 10.4.3. Applying encryption to medical images 259 10.4.4. Selective encryption of medical images 261 10.5. Medical image watermarking and encryption 265 10.5.1. Image watermarking and health uses 265 10.5.2. Watermarking techniques and medical imagery 266 10.5.2.1. Characteristics. 266 10.5.2.2. The methods 267 10.5.3. Confidentiality and integrity of medical images by data encryption and data hiding 269 10.6. Conclusion. 272 10.7. Bibliography 273 Chapter 11. Transmission of Compressed Medical Data on Fixed and Mobile Networks 277 Christian OLIVIER, Benoît PARREIN and Rodolphe VAUZELLE 11.1. Introduction 277 11.2. Brief overview of the existing applications 278 11.3. The fixed and mobile networks 279 11.3.1. The network principles 279 11.3.1.1. Presentation, definitions and characteristics 279 11.3.1.2. The different structures and protocols 281 11.3.1.3. Improving the Quality of Service 281 11.3.2. Wireless communication systems 282 11.3.2.1. Presentation of these systems 282 11.3.2.2. Wireless specificities 284 11.4. Transmission of medical images 287 11.4.1. Contexts 287 11.4.1.1. Transmission inside a hospital 287 11.4.1.2. Transmission outside hospital on fixed networks 287 11.4.1.3. Transmission outside hospital on mobile networks 288 11.4.2. Encountered problems 288 11.4.2.1. Inside fixed networks 288 11.4.2.2. Inside mobile networks 289 11.4.3. Presentation of some solutions and directions 293 11.4.3.1. Use of error correcting codes 294 11.4.3.2. Unequal protection using the Mojette transform 297 11.5. Conclusion 299 11.6. Bibliography 300 Conclusion 303 List of Authors 305 Index 309

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Book SynopsisMany scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems. The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three chapters bring the theoretical notions that make it possible to cast inverse problems within a mathematical framework. The next three chapters address the fundamental inverse problem of deconvolution in a comprehensive manner. Chapters 7 and 8 deal with advanced statistical questions linked to image estimation. In the last five chapters, the main tools introduced in the previous chapters are put into a practical context in important applicative areas, such as astronomy or medical imaging.Table of ContentsIntroduction 15 Jérôme IDIER PART I. FUNDAMENTAL PROBLEMS AND TOOLS 23 Chapter 1. Inverse Problems, Ill-posed Problems 25 Guy DEMOMENT, Jérôme IDIER 1.1. Introduction 25 1.2. Basic example 26 1.3. Ill-posed problem 30 1.3.1. Case of discrete data 31 1.3.2. Continuous case 32 1.4. Generalized inversion 34 1.4.1. Pseudo-solutions 35 1.4.2. Generalized solutions 35 1.4.3. Example 35 1.5. Discretization and conditioning 36 1.6. Conclusion 38 1.7. Bibliography 39 Chapter 2. Main Approaches to the Regularization of Ill-posed Problems 41 Guy DEMOMENT, Jérôme IDIER 2.1. Regularization 41 2.1.1. Dimensionality control 42 2.1.2. Minimization of a composite criterion 44 2.2. Criterion descent methods 48 2.2.1.Criterion minimization for inversion 48 2.2.2. The quadratic case 49 2.2.3. The convex case 51 2.2.4. General case 52 2.3. Choice of regularization coefficient 53 2.3.1. Residual error energy control 53 2.3.2. “L-curve” method 53 2.3.3. Cross-validation 54 2.4. Bibliography 56 Chapter 3. Inversion within the Probabilistic Framework 59 Guy DEMOMENT, Yves GOUSSARD 3.1. Inversion and inference 59 3.2. Statistical inference 60 3.2.1. Noise law and direct distribution for data 61 3.2.2. Maximum likelihood estimation 63 3.3. Bayesian approach to inversion 64 3.4. Links with deterministic methods 66 3.5. Choice of hyperparameters 67 3.6. A priori model68 3.7. Choice of criteria 70 3.8. The linear, Gaussian case 71 3.8.1. Statistical properties of the solution 71 3.8.2. Calculation of marginal likelihood 73 3.8.3. Wiener filtering 74 3.9. Bibliography 76 PART II. DECONVOLUTION 79 Chapter 4. Inverse Filtering and Other Linear Methods 81 Guy LE BESNERAIS, Jean-François GIOVANNELLI, Guy DEMOMENT 4.1. Introduction 81 4.2. Continuous-time deconvolution 82 4.2.1. Inverse filtering 82 4.2.2. Wiener filtering 84 4.3. Discretization of the problem 85 4.3.1. Choice of a quadrature method 85 4.3.2. Structure of observation matrix H 87 4.3.3. Usual boundary conditions 89 4.3.4. Problem conditioning 89 4.3.5.Generalized inversion 91 4.4. Batch deconvolution 92 4.4.1. Preliminary choices 92 4.4.2. Matrix form of the estimate 93 4.4.3. Hunt’s method (periodic boundary hypothesis) 94 4.4.4. Exact inversion methods in the stationary case 96 4.4.5. Case of non-stationary signals 98 4.4.6. Results and discussion on examples 98 4.5. Recursive deconvolution 102 4.5.1. Kalman filtering 102 4.5.2. Degenerate state model and recursive least squares 104 4.5.3. Autoregressive state model 105 4.5.4. Fast Kalman filtering 108 4.5.5. Asymptotic techniques in the stationary case 110 4.5.6. ARMA model and non-standard Kalman filtering 111 4.5.7. Case of non-stationary signals 111 4.5.8. On-lineprocessing: 2Dcase 112 4.6. Conclusion 112 4.7. Bibliography 113 Chapter 5. Deconvolution of Spike Trains 117 Frédéric CHAMPAGNAT, Yves GOUSSARD, Stéphane GAUTIER, Jérôme IDIER 5.1. Introduction 117 5.2. Penalization of reflectivities, L2LP/L2Hy deconvolutions 119 5.2.1. Quadratic regularization 121 5.2.2. Non-quadratic regularization 122 5.2.3. L2LPorL2Hy deconvolution 123 5.3. Bernoulli-Gaussian deconvolution 124 5.3.1. Compound BG model 124 5.3.2. Various strategies for estimation 124 5.3.3. General expression for marginal likelihood 125 5.3.4. An iterative method for BG deconvolution 126 5.3.5. Other methods 128 5.4. Examples of processing and discussion 130 5.4.1. Nature of the solutions 130 5.4.2. Setting the parameters 132 5.4.3. Numerical complexity 133 5.5. Extensions 133 5.5.1. Generalization of structures of R and H 134 5.5.2. Estimation of the impulse response . . . 134 5.6. Conclusion 136 5.7. Bibliography 137 Chapter 6. Deconvolution of Images 141 Jérôme IDIER, Laure BLANC-FÉRAUD 6.1. Introduction 141 6.2. Regularization in the Tikhonov sense 142 6.2.1. Principle 142 6.2.2. Connection with image processing by linear PDE 144 6.2.3. Limits of Tikhonov’s approach 145 6.3. Detection-estimation 148 6.3.1. Principle 148 6.3.2. Disadvantages 149 6.4. Non-quadratic approach 150 6.4.1. Detection-estimation and non-convex penalization 154 6.4.2. Anisotropic diffusion by PDE 155 6.5. Half-quadratic augmented criteria 156 6.5.1. Duality between non-quadratic criteria and HQ criteria 157 6.5.2. Minimization of HQ criteria 158 6.6. Application in image deconvolution 159 6.6.1. Calculation of the solution 159 6.6.2. Example 161 6.7. Conclusion 164 6.8. Bibliography 165 PART III. ADVANCED PROBLEMS AND TOOLS 169 Chapter 7. Gibbs-Markov Image Models 171 Jérôme IDIER 7.1. Introduction 171 7.2. Bayesian statistical framework 172 7.3. Gibbs-Markov fields 173 7.3.1. Gibbs fields 174 7.3.2. Gibbs-Markov equivalence 177 7.3.3. Posterior law of a GMRF 180 7.3.4. Gibbs-Markov models for images 181 7.4. Statistical tools, stochastic sampling 185 7.4.1. Statistical tools 185 7.4.2. Stochastic sampling 188 7.5. Conclusion 194 7.6. Bibliography 195 Chapter 8. Unsupervised Problems 197 Xavier DESCOMBES, Yves GOUSSARD 8.1. Introduction and statement of problem 197 8.2. Directly observed field 199 8.2.1. Likelihood properties 199 8.2.2. Optimization 200 8.2.3. Approximations 202 8.3. Indirectly observed field 205 8.3.1. Statement of problem 205 8.3.2. EM algorithm 206 8.3.3. Application to estimation of the parameters of a GMRF 207 8.3.4. EM algorithm and gradient 208 8.3.5. Linear GMRF relative to hyperparameters 210 8.3.6. Extensions and approximations 212 8.4. Conclusion 215 8.5. Bibliography 216 PART IV. SOME APPLICATIONS 219 Chapter 9. Deconvolution Applied to Ultrasonic Non-destructive Evaluation 221 Stéphane GAUTIER, Frédéric CHAMPAGNAT, Jérôme IDIER 9.1. Introduction 221 9.2. Example of evaluation and difficulties of interpretation 222 9.2.1. Description of the part to be inspected 222 9.2.2. Evaluation principle 222 9.2.3. Evaluation results and interpretation 223 9.2.4. Help with interpretation by restoration of discontinuities 224 9.3. Definition of direct convolution model 225 9.4. Blind deconvolution 226 9.4.1. Overview of approaches for blind deconvolution 226 9.4.2. DL2Hy/DBGd econvolution 230 9.4.3. Blind DL2Hy/DBG deconvolution 232 9.5. Processing real data 232 9.5.1. Processing by blind deconvolution 233 9.5.2. Deconvolution with a measured wave 234 9.5.3. Comparison between DL2Hy and DBG 237 9.5.4. Summary 240 9.6. Conclusion 240 9.7. Bibliography 241 Chapter 10. Inversion in Optical Imaging through Atmospheric Turbulence 243 Laurent MUGNIER, Guy LE BESNERAIS, Serge MEIMON 10.1. Optical imaging through turbulence 243 10.1.1. Introduction 243 10.1.2. Image formation 244 10.1.4. Imaging techniques 249 10.2. Inversion approach and regularization criteria used 253 10.3. Measurement of aberrations 254 10.3.1. Introduction 254 10.3.2. Hartmann-Shack sensor 255 10.3.3. Phase retrieval and phase diversity 257 10.4. Myopic restoration in imaging 258 10.4.1. Motivation and noise statistic 258 10.4.2. Data processing in deconvolution from wavefront sensing 259 10.4.3. Restoration of images corrected by adaptive optics 263 10.4.4. Conclusion 267 10.5. Image reconstruction in optical interferometry (OI) 268 10.5.1. Observation model 268 10.5.2. Traditional Bayesian approach 271 10.5.3. Myopic modeling 272 10.5.4. Results 274 10.6. Bibliography 277 Chapter 11. Spectral Characterization in Ultrasonic Doppler Velocimetry 285 Jean-François GIOVANNELLI, Alain HERMENT 11.1. Velocity measurement in medical imaging 285 11.1.1. Principle of velocity measurement in ultrasound imaging 286 11.1.2. Information carried by Doppler signals 286 11.1.3.Some characteristics and limitations 288 11.1.4. Data and problems treated 288 11.2. Adaptive spectral analysis 290 11.2.1. Least squares and traditional extensions 290 11.2.2. Long AR models – spectral smoothness – spatial continuity 291 11.2.3. Kalman smoothing 293 11.2.4. Estimation of hyperparameters 294 11.2.5. Processing results and comparisons 296 11.3. Tracking spectral moments 297 11.3.1. Proposed method 298 11.3.2. Likelihood of the hyperparameters 302 11.3.3. Processing results and comparisons 304 11.4. Conclusion 306 11.5. Bibliography 307 Chapter 12. Tomographic Reconstruction from Few Projections 311 Ali MOHAMMAD-DJAFARI, Jean-Marc DINTEN 12.1. Introduction 311 12.2. Projection generation model 312 12.3. 2D analytical methods 313 12.4. 3D analytical methods 317 12.5. Limitations of analytical methods 317 12.6. Discrete approach to reconstruction 319 12.7. Choice of criterion and reconstruction methods 321 12.8. Reconstruction algorithms 323 12.8.1. Optimization algorithms for convex criteria 323 12.8.2. Optimization or integration algorithms 327 12.9. Specific models for binary objects 328 12.10. Illustrations 328 12.10.1.2D reconstruction 328 12.10.2.3Dreconstruction 329 12.11. Conclusions 331 12.12. Bibliography 332 Chapter 13. Diffraction Tomography 335 Hervé CARFANTAN, Ali MOHAMMAD-DJAFARI 13.1. Introduction 335 13.2. Modeling the problem 336 13.2.1. Examples of diffraction tomography applications 336 13.2.2. Modeling the direct problem 338 13.3. Discretization of the direct problem 340 13.3.1. Choice of algebraic framework 340 13.3.2. Method of moments 341 13.3.3. Discretization by the method of moments 342 13.4. Construction of criteria for solving the inverse problem 343 13.4.1. First formulation: estimation of x 344 13.4.2. Second formulation: simultaneous estimation of x and φ 345 13.4.3. Properties of the criteria 347 13.5. Solving the inverse problem 347 13.5.1. Successive linearizations 348 13.5.2. Joint minimization 350 13.5.3. Minimizing MAP criterion 351 13.6. Conclusion 353 13.7. Bibliography 354 Chapter 14. Imaging from Low-intensity Data 357 Ken SAUER, Jean-Baptiste THIBAULT 14.1. Introduction 357 14.2. Statistical properties of common low-intensity image data 359 14.2.1. Likelihood functions and limiting behavior 359 14.2.2. Purely Poisson measurements 360 14.2.3. Inclusion of background counting noise 362 14.2.4. Compound noise models with Poisson information 362 14.3. Quantum-limited measurements in inverse problems 363 14.3.1. Maximum likelihood properties 363 14.3.2. Bayesian estimation 366 14.4. Implementation and calculation of Bayesian estimates 368 14.4.1. Implementation for pure Poisson model 368 14.4.2. Bayesian implementation for a compound data model 370 14.5. Conclusion 372 14.6. Bibliography 372 List of Authors 375 Index 377

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Book SynopsisThe concept of an inverse problem is a familiar one to most scientists and engineers, particularly in the field of signal and image processing, imaging systems (medical, geophysical, industrial non-destructive testing, etc.), and computer vision. In imaging systems, the aim is not just to estimate unobserved images but also their geometric characteristics from observed quantities that are linked to these unobserved quantities by a known physical or mathematical relationship. In this manner techniques such as image enhancement or addition of hidden detail can be delivered. This book focuses on imaging and vision problems that can be clearly described in terms of an inverse problem where an estimate for the image and its geometrical attributes (contours and regions) is sought. The book uses a consistent methodology to examine inverse problems such as: noise removal; restoration by deconvolution; 2D or 3D reconstruction in X-ray, tomography or microwave imaging; reconstruction of the surface of a 3D object using X-ray tomography or making use of its shading; reconstruction of the surface of a 3D landscape based on several satellite photos; super-resolution; motion estimation in a sequence of images; separation of several images mixed using instruments with different sensitivities or transfer functions; and much more.Trade Review"Apart from the high price I can recommend this book if you are interested in imaging or artificial vision." (I Programmer, 3 February 2011)Table of ContentsPreface 13 Chapter 1. Introduction to Inverse Problems in Imaging and Vision 15 Ali MOHAMMAD-DJAFARI 1.1. Inverse problems 16 1.2. Specific vision problems 21 1.3. Models for time-dependent quantities 26 1.4. Inverse problems with multiple inputs and multiple outputs (MIMO) 27 1.5. Non-linear inverse problems 30 1.6. 3D reconstructions 33 1.7. Inverse problems with multimodal observations 33 1.8. Classification of inversion methods: analytical or algebraic 34 1.9. Standard deterministic methods 40 1.10. Probabilistic methods 44 1.11. Problems specific to vision 50 1.12. Introduction to the various chapters of the book 52 1.13. Bibliography 55 Chapter 2. Noise Removal and Contour Detection 59 Pierre CHARBONNIER and Christophe COLLET 2.1. Introduction 61 2.2. Statistical segmentation of noisy images 72 2.3. Multi-band multi-scale Markovian regularization 79 2.4. Bibliography 88 Chapter 3. Blind Image Deconvolution 97 Laure BLANC-FÉRAUD, Laurent MUGNIER and André JALOBEANU 3.1. Introduction 97 3.2. The blind deconvolution problem 98 3.3. Joint estimation of the PSF and the object 103 3.4. Marginalized estimation of the impulse response 107 3.5. Various other approaches 112 3.6. Multi-image methods and phase diversity 114 3.7. Conclusion 115 3.8. Bibliography 116 Chapter 4. Triplet Markov Chains and Image Segmentation 123 Wojciech PIECZYNSKI 4.1. Introduction 124 4.2. Pairwise Markov chains (PMCs) 127 4.3. Copulas in PMCs 130 4.4. Parameter estimation 132 4.5. Triplet Markov chains (TMCs) 136 4.6. TMCs and non-stationarity 139 4.7. Hidden Semi-Markov chains (HSMCs) and TMCs 140 4.8. Auxiliary multivariate chains 144 4.9. Conclusions and outlook 148 4.10. Bibliography 149 Chapter 5. Detection and Recognition of a Collection of Objects in a Scene 155 Xavier DESCOMBES, Ian JERMYN and Josiane ZERUBIA 5.1. Introduction 155 5.2. Stochastic approaches 156 5.3. Variational approaches 167 5.4. Bibliography 184 Chapter 6. Apparent Motion Estimation and Visual Tracking 191 Etienne MÉMIN and Patrick PÉREZ 6.1. Introduction: from motion estimation to visual tracking 191 6.2. Instantaneous estimation of apparent motion 193 6.3. Visual tracking 219 6.4. Conclusions 240 6.5. Bibliography 241 Chapter 7. Super-resolution 251 Ali MOHAMMAD-DJAFARI and Fabrice HUMBLOT 7.1. Introduction 251 7.2. Modeling the direct problem 252 7.3. Classical SR methods 257 7.4. SR inversion methods 261 7.5. Methods based on a Bayesian approach 265 7.6. Simulation results 271 7.7. Conclusion 272 7.8. Bibliography 274 Chapter 8. Surface Reconstruction from Tomography Data 277 Charles SOUSSEN and Ali MOHAMMAD-DJAFARI 8.1. Introduction 277 8.2. Reconstruction of localized objects 280 8.3. Use of deformable contours for 3D reconstruction 284 8.4. Appropriate surface models and algorithmic considerations 293 8.5. Reconstruction of a polyhedric active contour 298 8.6. Conclusion 303 8.7. Bibliography 305 Chapter 9. Gauss-Markov-Potts Prior for Bayesian Inversion in Microwave Imaging 309 Olivier FÉRON, Bernard DUCHÊNE and Ali MOHAMMAD-DJAFARI 9.1. Introduction 310 9.2. Experimental configuration and modeling of the direct problem 311 9.3. Inversion in the linear case 315 9.4. Inversion in the non-linear case 325 9.5. Conclusion 335 9.6. Bibliography 336 Chapter 10. Shape from Shading 339 Jean-Denis DUROU 10.1. Introduction 339 10.2. Modeling of shape from shading 340 10.3. Resolution of shape from shading 353 10.4. Conclusion 371 10.5. Bibliography 372 Chapter 11. Image Separation 377 Hichem SNOUSSI and Ali MOHAMMAD-DJAFARI 11.1. General introduction 377 11.2. Blind image separation 378 11.3. Bayesian formulation 384 11.4. Stochastic algorithms 390 11.5. Simulation results 398 11.6. Conclusion 401 11.7. Appendix 1: a posteriori distributions 407 11.8. Bibliography 409 Chapter 12. Stereo Reconstruction in Satellite and Aerial Imaging 411 Julie DELON and Andrés ALMANSA 12.1. Introduction 411 12.2. Principles of satellite stereovision 412 12.3. Matching 415 12.4. Regularization 421 12.5. Numerical considerations 425 12.6. Conclusion 432 12.7. Bibliography 434 Chapter 13. Fusion and Multi-modality 437 Christophe COLLET, Farid FLITTI, Stéphanie BRICQ and André JALOBEANU 13.1. Fusion of optical multi-detector images without loss of information 437 13.2. Fusion of multi-spectral images using hidden Markov trees 438 13.3. Segmentation of multimodal cerebral MRI using an a priori probabilistic map 448 13.4. Bibliography 458 List of Authors 461 Index 463

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Book SynopsisThe authors describe a technique that can visualize the atomic structure of molecules, it is necessary, in terms of the image processing, to consider the reconstruction of sparse images. Many works have leveraged the assumption of sparsity in order to achieve an improved performance that would not otherwise be possible. For nano MRI, the assumption of sparsity is given by default since, at the atomic scale, molecules aresparse structures. This work reviews the latest results on molecular imaging for nano MRI. Sparse image reconstruction methods can be categorized as either non-Bayesian or Bayesian. A comparison of the performance and complexity of several such algorithms is given.Table of ContentsIntroduction ix Chapter 1. Nano MRI 1 Chapter 2. Sparse Image Reconstruction 7 Chapter 3. Iterative Thresholding Methods 15 Chapter 4. Hyperparameter Selection Using the SURE Criterion 43 Chapter 5. Monte Carlo Approach: Gibbs Sampling 53 Chapter 6. Simulation Study 65 Bibliography 73 Index 77

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Book SynopsisConventional topographic databases, obtained by capture on aerial or spatial images provide a simplified 3D modeling of our urban environment, answering the needs of numerous applications (development, risk prevention, mobility management, etc.). However, when we have to represent and analyze more complex sites (monuments, civil engineering works, archeological sites, etc.), these models no longer suffice and other acquisition and processing means have to be implemented. This book focuses on the study of adapted lifting means for “notable buildings”. The methods tackled in this book cover lasergrammetry and the current techniques of dense correlation based on images using conventional photogrammetry.Table of Contents1. Specific Requirements for the 3D Digitization of Outstanding Sites. 2. 3D Digitization Using Images. 3. 3D Digitization by Laser Scanner. 4. Complementarity of Techniques. 5. Point Cloud Processing. 6. Management and Use of Surveys.

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Book SynopsisMathematical Imaging is currently a rapidly growing field in applied mathematics, with an increasing need for theoretical mathematics. This book, the second of two volumes, emphasizes the role of mathematics as a rigorous basis for imaging sciences. It provides a comprehensive and convenient overview of the key mathematical concepts, notions, tools and frameworks involved in the various fields of gray-tone and binary image processing and analysis, by proposing a large, but coherent, set of symbols and notations, a complete list of subjects and a detailed bibliography. It establishes a bridge between the pure and applied mathematical disciplines, and the processing and analysis of gray-tone and binary images. It is accessible to readers who have neither extensive mathematical training, nor peer knowledge in Image Processing and Analysis. It is a self-contained book focusing on the mathematical notions, concepts, operations, structures, and frameworks that are beyond or involved in Image Processing and Analysis. The notations are simplified as far as possible in order to be more explicative and consistent throughout the book and the mathematical aspects are systematically discussed in the image processing and analysis context, through practical examples or concrete illustrations. Conversely, the discussed applicative issues allow the role of mathematics to be highlighted. Written for a broad audience – students, mathematicians, image processing and analysis specialists, as well as other scientists and practitioners – the author hopes that readers will find their own way of using the book, thus providing a mathematical companion that can help mathematicians become more familiar with image processing and analysis, and likewise, image processing and image analysis scientists, researchers and engineers gain a deeper understanding of mathematical notions and concepts.Table of ContentsPreface xvii Introduction xxv Part 5 Twelve Main Geometrical Frameworks for Binary Images 1 Chapter 21 The Set-Theoretic Framework 3 Chapter 22 The Topological Framework 9 Chapter 23 The Euclidean Geometric Framework 23 Chapter 24 The Convex Geometric Framework 37 Chapter 25 the Morphological Geometric Framework 47 Chapter 26 The Geometric and Topological Framework 57 Chapter 27 The Measure-Theoretic Geometric Framework 71 Chapter 28 The Integral Geometric Framework 89 Chapter 29 The Differential Geometric Framework 111 Chapter 30 The Variational Geometric Framework 129 Chapter 31 The Stochastic Geometric Framework 135 Chapter 32 The Stereological Framework 159 Part 6 Four Specific Geometrical Framework for Binary Images 177 Chapter 33 The Granulometric Geometric Framework 179 Chapter 34 The Morphometric Geometric Framework 189 Chapter 35 The Fractal Geometric Framework 211 Chapter 36 The Textural Geometric Framework 229 Part 7 Four 'Hybrid' Framework for Gray-Tone and Binary Images 241 Chapter 37 The Interpolative Framework 243 Chapter 38 The Bounded-Variation Framework 253 Chapter 39 The Level Set Framework 269 Chapter 40 The Distance-Map Framework 281 Concluding Discussion and Perspectives 295 Appendices 301 Tables of Notations and Symbols 303 Table of Acronyms 341 Table of Latin Phrases 347 Bibliography 349 Index of Authors 435 Index of Subjects 445

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Book SynopsisVolume 3 of the second edition of the fully revised and updated Digital Signal and Image Processing using MATLAB, after first two volumes on the "Fundamentals" and "Advances and Applications: The Deterministic Case", focuses on the stochastic case. It will be of particular benefit to readers who already possess a good knowledge of MATLAB, a command of the fundamental elements of digital signal processing and who are familiar with both the fundamentals of continuous-spectrum spectral analysis and who have a certain mathematical knowledge concerning Hilbert spaces. This volume is focused on applications, but it also provides a good presentation of the principles. A number of elements closer in nature to statistics than to signal processing itself are widely discussed. This choice comes from a current tendency of signal processing to use techniques from this field. More than 200 programs and functions are provided in the MATLAB language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.Table of ContentsForeword ix Notations and Abbreviations xiii 1 Mathematical Concepts 1 1.1 Basic concepts on probability 1 1.2 Conditional expectation 9 1.3 Projection theorem 10 1.4 Gaussianity 13 1.5 Random variable transformation 18 1.6 Fundamental statistical theorems 21 1.7 Other important probability distributions 23 2 Statistical Inferences 25 2.1 Statistical model 25 2.2 Hypothesis tests 27 2.3 Statistical estimation 41 3 Monte-Carlo Simulation 85 3.1 Fundamental theorems 85 3.2 Stating the problem 86 3.3 Generating random variables 88 3.4 Variance reduction 99 4 Second Order Stationary Process 107 4.1 Statistics for empirical correlation 107 4.2 Linear prediction of WSS processes 111 4.3 Non-parametric spectral estimation of WSS processes 124 5 Inferences on HMM 139 5.1 Hidden Markov Models (HMM) 130 5.2 Inferences on HMM 142 5.3 Gaussian linear case: the Kalman filter 143 5.4 Discrete finite Markov case 152 6 Selected Topics 163 6.1 High resolution methods 163 6.2 Digital Communications 186 6.3 Linear equalization and the Viterbi algorithm 211 6.4 Compression 220 7 Hints and Solutions 235 H1 Mathematical concepts 235 H2 Statistical inferences 237 H3 Monte-Carlo simulation 269 H4 Second order stationary process 283 H5 Inferences on HMM 283 H6 Selected Topics 300 8 Appendices 317 A1 Miscellaneous functions 317 A2 Statistical functions 318 Bibliography 329 Index 333

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Book SynopsisThis book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others. The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning，dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts. The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision.Table of ContentsIntroduction and Overview.- Matlab Functions of Image and Video.- Image and Video Segmentation.- Feature Extraction and Representation.- Common Evaluation Criterion.- Image Correction.- Image Inpainting.- Fusions.- Image Stitching.- Image Watermarking.

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Book SynopsisComputer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.Table of Contents

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Book SynopsisThis is the first book that focuses entirely on the fundamental questions in visualization. Unlike other existing books in the field, it contains discussions that go far beyond individual visual representations and individual visualization algorithms. It offers a collection of investigative discourses that probe these questions from different perspectives, including concepts that help frame these questions and their potential answers, mathematical methods that underpin the scientific reasoning of these questions, empirical methods that facilitate the validation and falsification of potential answers, and case studies that stimulate hypotheses about potential answers while providing practical evidence for such hypotheses. Readers are not instructed to follow a specific theory, but their attention is brought to a broad range of schools of thoughts and different ways of investigating fundamental questions. As such, the book represents the by now most significant collective effort for gathering a large collection of discourses on the foundation of data visualization. Data visualization is a relatively young scientific discipline. Over the last three decades, a large collection of computer-supported visualization techniques have been developed, and the merits and benefits of using these techniques have been evidenced by numerous applications in practice. These technical advancements have given rise to the scientific curiosity about some fundamental questions such as why and how visualization works, when it is useful or effective and when it is not, what are the primary factors affecting its usefulness and effectiveness, and so on. This book signifies timely and exciting opportunities to answer such fundamental questions by building on the wealth of knowledge and experience accumulated in developing and deploying visualization technology in practice.Table of ContentsPart I: Theoretical Underpinnings of Data Visualization.- The Fabric of Visualization.- Visual Abstraction.- Measures in Visualization Space.- Knowledge-Assisted Visualization and Guidance.- Mathematical Foundations in Visualizations.- Transformations, Mappings and Data Summaries.- Part II: Empirical Studies in Visualization.- A Survey of Variables Used in Empirical Studies for Visualization.- Empirical Evaluations with Domain Experts.- Evaluation of Visualization Systems with Long-term Case Studies.- Vis4Vis: Visualization for (Empirical) Visualization Research.- 'Isms' in Visualization.- Open Challenges in Empirical Visualization Research.- Part III: Collaboration with Domain Experts.- Case Studies for Working with Domain Experts.- Collaboration Between Industry and University.- Collaborating Successfully with Domain Experts.- Part IV: Developing Visualizations for Broad Audiences.- Reflections on Visualization for Broad Audiences.- Reaching Broad Audiences from a Research Institute Setting.- Reaching Broad Audiences from a Large Agency Setting.- Reaching Broad Audiences from a Science Center or Museum Setting.- Reaching Broad Audiences in an Educational Setting.- Challenges and Open Issues in Visualization for Broad Audiences

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Book SynopsisThis book constitutes the refereed proceedings of the 3rd International Conference on Technology, Innovation, Entrepreneurship and Education, TIE 2019, held in Braga, Portugal, in October 2019. The 11 full and 2 short papers focus on emerging technologies for education, entertainment, well-being, creativity, arts and business development. In addition, it aims at promoting new venture creation opportunities that emerge from these innovations, as well as innovation methods that target these core subjects.Table of ContentsInnovating and Exploring Children´s Learning.- Reading to Level Up: Gamifying Reading Fluency.- Rethinking the Design of Hotspots in Children’s Digital Picturebooks: Insights from an Exploratory Study.- Children’s tinkering activity with Collapse Informatics: the Internalization of Environmental Consciousness.- ”Play and learn”: exploring CodeCubes Innovating Media Usage.- Question & Answering interface to improve the students’ experience in an e-learning course with a virtual tutor.- Exploring the Use of Augmented Reality Concepts to Enhance the TV Viewer Experience.- Design Experiments in Nonrepresentational VR and Symmetric Texture Generation in Real-Time Innovation for Special Needs.- Didactic toy for children with special needs.- Digitally-mediated Learning Environments and Information Literacy for Active Ageing: A Pilot Study.- European video game development and disability: Reflections on data, rights, decisions and assistance Innovating Methods.- From community datamining to enterprising villagers.- The transformational effect of a designerly approach within a research project.- Visual Quotes and Physical Activity Tracking: Can Aesthetic Pleasure Motivate Our Short-term Exercise Motivation?.- Raising the Odds of Success for Innovative Product by Experimentation and Utilizing Input of Future User.

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Book SynopsisThis book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. Table of Contents1. Unsupervised Visual Learning: from Pixels to Seeing.- 2. Unsupervised Learning of Graph and Hypergraph Matching.- 3. Unsupervised Learning of Graph and Hypergraph Clustering.- 4. Feature Selection meets Unsupervised Learning.- 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features.- 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time.- 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks.- 8. Unsupervised Learning Towards the Future.

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Book SynopsisThis textbook is designed for postgraduate studies in the field of 3D Computer Vision. It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops. Overall, the book covers three main areas: ● 3D imaging, including passive 3D imaging, active triangulation 3D imaging, active time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data representation and visualisation; ● 3D shape analysis, including local descriptors, registration, matching, 3D morphable models, and deep learning on 3D datasets; and ● 3D applications, including 3D face recognition, cultural heritage and 3D phenotyping of plants. 3D computer vision is a rapidly advancing area in computer science. There are many real-world applications that demand high-performance 3D imaging and analysis and, as a result, many new techniques and commercial products have been developed. However, many challenges remain on how to analyse the captured data in a way that is sufficiently fast, robust and accurate for the application. Such challenges include metrology, semantic segmentation, classification and recognition. Thus, 3D imaging, analysis and their applications remain a highly-active research field that will continue to attract intensive attention from the research community with the ultimate goal of fully automating the 3D data capture, analysis and inference pipeline. Table of ContentsIntroduction.- Part I 3D Shape Acquisition, Representation and Visualization.- Passive 3D Imaging.- Active-triangulation 3D Imaging Systems for Industrial Inspection.- Active Time-of-Flight 3D Imaging Systems for Medium-range Applications.- Consumer-grade RGB-D cameras.- 3D Data Representation, Storage and Processing.- Part II: 3D Shape Analysis and Inference.- 3D Local Descriptors -- from Hand-crafted to Learned.- 3D Shape Registration.- 3D Shape Matching for Retrieval and Recognition.- 3D Morphable Models: the Face, Ear and Head.- Deep Learning on 3D Data.- Part III: 3D Imaging Applications.- 3D Face Recognition.- 3D Digitization of Cultural Heritage.- 3D Phenotyping of Plants.- Index

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Book SynopsisThis two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020.The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management.Due to the corona pandemic, ICIAR 2020 was held virtually only.Table of ContentsMachine Learning.- Medical Image and Analysis.- Analysis of Histopathology Images.- Diagnosis and Screening of Ophthalmic Diseases.- Grand Challenge on Automatic Lung Cancer Patient Management.

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Book SynopsisThis book is a significant novelty in the scientific and editorial landscape. Morphology is both an ancient and a new discipline that rests on Goethe's heritage and re-forms it in the present through the concepts of form and image. The latter are to be understood as structural elements of a new cultural grammar able to make the late modern world intelligible. In particular, compared to the original Goethean project, but also to C.P. Snow's idea of unifying the “two cultures”, the fields of morphological culture that are the object of this glossary have profoundly changed. The ever-increasing importance of the image as a polysemic form has made the two concepts absolutely transitive, so to speak. This is concomitant with the emergence of a culture that revolves around the image, attracting the verbal logos into its orbit. Incidentally, even the hermeneutic relationship between past and present relies more and more on the image, causing deep changes in cultural environments. Form and image are not just bridging concepts, as in the field of ancient morphology, but real transitive concepts that define the state of a culture. From the Internet to smartphones, television, advertising, etc., we are witnessing – as Horst Bredekamp observes – an immense mass of images that fill our time and affect the most diverse areas of our culture. The ancient connection between science and art recalled by Goethe emerges with unusual evidence thanks to intersecting patterns and expressive forms that are sometimes shared by different forms of knowledge. Creating a glossary and a culture of these intersections is the task of morphology, which thus enters into the boundaries between aesthetics, art, design, advertising, and sciences (from mathematics to computer science, to physics, and to biology), in order to provide the founding elements of a grammar and a syntax of the image. The latter, in its formal quality, both expressive and symbolic, is a fundamental element in the unification of the various kinds of knowledge, which in turn come to be configured, in this regard, also as styles of vision. The glossary is subdivided into contiguous sections, within a complex framework of cross-references. In addition to the two curators, the book features the collaboration of a team of scholars from the individual disciplines appearing in the glossary. Table of ContentsAesthetics.- Analogy.- Artefact.- Artifex.- Artistic Morphology.- Atmosphere.- Attractors/Basin of Attraction.- Biopolitics.- Body.- Character/State.- Chreod.- Classics.- Code (Biological).- Code (Juridical).- Colour.- Complexity.- Contour/Outline/Silhouette.- Dance.- Degeneration.- Demography.- Development/Evolution.- Device.- Diagrams.- Diaphane.- Drawing.- Dynamic System.- Eidetics.- Emergence.- Enactivism.- Epidemiology.- Epigenesis / Preformation(ism).- Epigenetic Landscape.- Epigenetics.- Ethics of image.- Evidence/Intuibility.- Extension.- Figuration/Figure/Form.- Folktale, Morphology.- Food.- Form Constancy.- Formation.- Formula

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Book SynopsisPREVIOUS EDITIONThis textbook introduces the “Fundamentals of Multimedia”, addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.Table of ContentsPart I: Introduction and Multimedia Data Representations.- Introduction to Multimedia.- A Taste of Multimedia.- Graphics and Image Data Representations.- Color in Image and Video.- Fundamental Concepts in Video.- Basics of Digital Audio.- Part II: Multimedia Data Compression.- Lossless Compression Algorithms.- Lossy Compression Algorithms.- Image Compression Standards.- Basic Video Compression Techniques.- MPEG Video Coding: MPEG-1, 2, 4 and 7.- Modern Video Coding Standards: H.264, H.265, and H.266.- Basic Audio Compression Techniques.- MPEG Audio Compression.- Part III: Multimedia Communications and Networking.- Network Services and Protocols for Multimedia Communications.- Internet Multimedia Content Distribution.- Multimedia over Wireless and Mobile Networks.- Cloud Computing for Multimedia Services.- Part IV: Human-Centric Interactive Multimedia.- Online Social Media Sharing.- Augmented Reality and Virtual Reality.- Content-Based Retrieval in Digital Libraries.- Cloud Computing for Multimedia Services.

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Book SynopsisThis textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field — What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. “An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, StanfordTable of ContentsIntroduction.- Part I: Foundations- Representing Position and Orientation.- Part II: Computer Vision.- Light and Color.- Images and Image Processing.- Image Feature Extraction.- Part III: The Geometry of Vision.- Image Formation.- Using Multiple Images.- Index.

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Book SynopsisThis open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures. Table of ContentsMachine Learning and Deep learning approaches in human brain mapping.- A high-resolution model of the human entorhinal cortex in the ‘BigBrain’– use case for machine learning and 3D analyses.- Deep learning-supported cytoarchitectonic mapping of the human lateral geniculate body in the BigBrain.- Brain modelling and simulation.- Computational modelling of cerebellar magnetic stimulation: the effect of washout?.- Usage and scaling of an open-source spiking multi-area model of the monkey cortex.- Exascale compute and data infrastructures for neuroscience and applications.- Modular supercomputing for neuroscience.- Fenix: Distributed e-Infrastructure Services for EBRAINS.- Independent component analysis for noise and artifact removal in three-dimensional Polarized Light Imaging.- Exascale artificial and natural neural architectures.- Brain-inspired algorithms for processing of visual data.- An hybrid attention-based system for the prediction of facial attributes.- The statistical physics of learning revisited: Typical learning curves in model scenarios.- Emotion mining: from unimodal to multimodal approaches.-

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Book SynopsisThis four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.The papers are organized into the following topical sections: extracting document semantics, text and symbol recognition, document analysis systems, office automation, signature verification, document forensics and provenance analysis, pen-based document analysis, human document interaction, document synthesis, and graphs recognition.Table of ContentsExtracting Document Semantics.- MiikeMineStamps: A Long-Tailed Dataset of Japanese Stamps via Active Learning.- Deep Learning for Document Layout Generation: A First Reproducible Quantitative Evaluation and a Baseline Model.- Text and Symbol Recognition.- MRD: A Memory Relation Decoder for Online Handwritten Mathematical Expression Recognition.-Full Page Handwriting Recognition via Image to Sequence Extraction.- SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition.- IHR-NomDB: The Old Degraded Vietnamese Handwritten Script Archive Database.- Sequence Learning Model for Syllables Recognition Arranged in Two Dimensions.- Transformer for Handwritten Text Recognition using Bidirectional Post-Decoding.- Zero-Shot Chinese Text Recognition via Matching Class Embedding.- Text-conditioned Character Segmentation for CTC-based Text Recognition.-Towards Fast, Accurate and Compact Online Handwritten Chinese Text Recognition.- HCADecoder: A Hybrid CTC-Attention Decoder for Chinese Text Recognition.-Meta-learning of Pooling Layers for Character Recognition.- Document Analysis Systems.- Text-line-up: Don’t Worry about the Caret.- Multimodal Attention-based Learning for Imbalanced Corporate Documents Classification.- Light-weight Document Image Cleanup using Perceptual Loss.- Office Automation.- A New Semi-Automatic Annotation Model via Semantic Boundary Estimation for Scene Text Detection.- Searching from the Prediction of Visual and Language Model for Handwritten Chinese Text Recognition.- Towards an IMU-based Pen Online Handwriting Recognizer.- Signature Verification.- 2D vs 3D online writer identification: a comparative study.- A Handwritten Signature Segmentation Approach for Multi-resolution and Complex Documents Acquired by Multiple Sources.- Attention based Multiple Siamese Network for Offline Signature Verification.- Attention to Warp: Deep Metric Learning for Multivariate Time Series.- Document Forensics and Provenance Analysis.- Customizable Camera Verification for Media Forensic.- Density Parameters of Handwriting in Schizophrenia and Affective Disorders Assessed Using the Raygraf Computer Software.- Pen-based Document Analysis.- Language-Independent Bimodal System for Early Parkinson’s Disease Detection.-TRACE: A Differentiable Approach to Line-level Stroke Recovery for Offline Handwritten Text.- Segmentation and graph matching for online analysis of student arithmetic operations.- Applying End-to-end Trainable Approach on Stroke Extraction in Handwritten Math Expressions Images.- A Novel Sigma-Lognormal Parameter Extractor for Online Signatures.- Human Document Interaction.- Near-perfect Relation Extraction from Family Books.- Estimating Human Legibility in Historic Manuscript Images - A Baseline.- A Modular and Automated Annotation Platform for Handwritings: Evaluation on Under-resourced Languages.- Reducing the Human Effort in Text Line Segmentation for Historical Documents.- DSCNN: Dimension Separable Convolutional Neural Networks for character recognition based on inertial sensor signal.- Document Synthesis.- DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis.- Font Style that Fits an Image -- Font Generation Based on Image Context.- Bayesian Hyperparameter optimization of Deep Neural Network algorithms based on Ant Colony optimization.- End-to-End Approach for Recognition of Historical Digit Strings.- Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs.- Synthesizing Training Data for Handwritten Music Recognition.- Towards Book Cover Design via Layout Graphs.- Graphics Recognition.- Complete Optical Music Recognition via Agnostic Transcription and Machine Translation.- Improving Machine Understanding of Human Intent in Charts.- DeMatch: Towards Understanding the Panel of Chart Documents.- Sequential Next-Symbol Prediction for Optical Music Recognition.- Which Parts Determine the Impression of the Font?.- Impressions2Font: Generating Fonts by Specifying Impressions.

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Book SynopsisThis four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.The papers are organized into the following topical sections: scene text detection and recognition, document classification, gold-standard benchmarks and data sets, historical document analysis, and handwriting recognition. In addition, the volume contains results of 13 scientific competitions held during ICDAR 2021.Table of ContentsScene Text Detection and Recognition.- HRRegionNet: Chinese Character Segmentation in Historical Documents with Regional Awareness.- Fast Text v. Non-text Classification of Images.- Mask Scene Text Recognizer.- Rotated Box Is Back: An Accurate Box Proposal Network for Scene Text Detection.- Heterogeneous Network Based Semi-supervised Learning For Scene Text Recognition.- Scene Text Detection with Scribble Line.- EEM: An End-to-end Evaluation Metric for Scene Text Detection and Recognition.- SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models.- Fast Recognition for Multidirectional and Multi-Type License Plates with 2D Spatial Attention.- A Multi-level Progressive Rectification Mechanism for Irregular Scene Text Recognition.- Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition.- FEDS - Filtered Edit Distance Surrogate.- Bidirectional Regression for Arbitrary-Shaped Text Detection.- Document Classification.- VML-HP: Hebrew paleography dataset.- Open Set Authorship Attribution toward Demystifying Victorian Periodicals.- A More Effective Sentence-Wise Text Segmentation Approach using BERT.- Data Augmentation for Writer Identification Using a Cognitive Inspired Model.- Key-guided Identity Document Classification Method by Graph Attention Network.- Document Image Quality Assessment via Explicit Blur and Text Size Estimation.- Analyzing the potential of Zero-Shot Recognition for Document Image Classification.- Gender Detection Based on Spatial Pyramid Matching.- EDNets: Deep Feature Learning for Document Image Classification based on Multi-view Encoder-Decoder Neural Networks.- Fast End-to-end Deep Learning Identity Document Detection, Classification and Cropping.- Gold-Standard Benchmarks and Data Sets.- Image Collation: Matching illustrations in manuscripts.- Revisiting the Coco Panoptic Metric to Enable Visual and Qualitative Analysis of Historical Map Instance Segmentation.- A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes.- GNHK: A Dataset for English Handwriting in the Wild.- Personalizing Handwriting Recognition Systems with Limited User-Specific Samples.- An Efficient Local Word Augment Approach for Mongolian Handwritten Script Recognition.- IIIT-INDIC-HW-WORDS: A Dataset for Indic Handwritten Text Recognition.- Historical Document Analysis.- AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions.- TS-Net: OCR Trained to Switch Between Text Transcription Styles.- Handwriting Recognition with Novelty.- Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction.- Data Augmentation Based on CycleGAN for Improving Woodblock-printing Mongolian Words Recognition.- SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization.- Handwriting Recognition.- Recognizing Handwritten Chinese Texts with Insertion and Swapping Using A Structural Attention Network.- Strikethrough Removal From Handwritten Words Using CycleGANs.- Iterative Weighted Transductive Learning for Handwriting Recognition.- Competition Reports.- ICDAR 2021 Competition on Scientific Literature Parsing.- ICDAR 2021 Competition on Historical Document Classification.- ICDAR 2021 Competition on Document Visual Question Answering.- ICDAR 2021 Competition on Scene Video Text Spotting.- ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment.- ICDAR 2021 Competition on Components Segmentation Task of Document Photos.- ICDAR 2021 Competition on Historical Map Segmentation.- ICDAR 2021 Competition on Time-Quality Document Image Binarization.- ICDAR 2021 Competition on On-Line Signature Verification.- ICDAR 2021 Competition on Script Identification in the Wild.- ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.- ICDAR 2021 Competition on Multimodal Emotion Recognition on Comics Scenes.- ICDAR 2021 Competition on Mathematical Formula Detection.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Computer Vision Systems, ICVS 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 20 papers presented were carefully reviewed and selected from 29 submissions. cover a broad spectrum of issues falling under the wider scope of computer vision in real-world applications, including among others, vision systems for robotics, autonomous vehicles, agriculture and medicine. In this volume, the papers are organized into the sections: attention systems; classification and detection; semantic interpretation; video and motion analysis; computer vision systems in agriculture.Table of ContentsAttention Systems.- Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields.- MARL: Multimodal Attentional Representation Learning for Disease Prediction.- Object Localization with Attribute Preference based on Top-Down Attention.- See the silence: improving visual-only voice activity detection by optical flow and RGB fusion.- Classification and Detection.- Score to Learn: a Comparative Analysis of Scoring Functions for Active Learning in Robotics.- Enhancing the performance of image classification through features automatically learned from depth-maps.- Object Detection on TPU Accelerated Embedded Devices.- Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification.- Semantic Interpretation.- Measuring the Sim2Real gap in 3D Object classification for different 3D data representation.- Spatially-Constrained Semantic Segmentation with Topological Μaps and Visual Εmbeddings.- Knowledge-enabled generation of semantically annotated image sequences of manipulation activities from VR demonstrations.- Make It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts.- Video and Motion Analysis.- Video Popularity Prediction through Fusing Early Viewership with Video Content.- Action Prediction during Human-Object Interaction based on DTW and Early Fusion of Human and Object Representations.- GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids.- An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset.- Computer Vision Systems in Agriculture.- Robust Counting of Soft Fruit through Occlusions with Re-identification.- Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture.- Learning Image-based Contaminant Detection in Wool Fleece from Noisy Annotations.- Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network’s Feature Pyramid Levels.

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Book SynopsisThis open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.Table of ContentsPart I - Introduction: 1. Digital Face Manipulation: An Introduction.- 2. Face Manipulation in Biometric Systems.- 3. Face Manipulation in Media Forensics.- Part II - Face Manipulation Detection Methods: 4. DeepFakes Manipulation.- 5. DeepFakes Detection.- 6. Attacking Face Recognition Systems with DeepFakes.- 7. Vulnerability of Face Recognition Systems to Morphing Attacks.- 8. Face Morphing Attack Detection.- 9. Face Synthesis Detection.- 10. Expression Swap Detection.- 11. Audio- and Text-to-Video Detection.- 12. Detection of Facial Retouching.- 13. Face De-Identification Detection.- Part III - Further Topics: 14. All-in-One Face Manipulation Detection: Generalization Analysis.- 15. Reversion of Face Manipulation.- 16. 3D Face Manipulation Detection.- 17. Improving Face Recognition with Face Image Manipulation.- 18. Impact of Post-Processing on Face Manipulation Detection.- 19. Societal and Legal Aspects of Face Manipulation.- 20. Face Manipulation for Privacy Protection.- 21. Privacy-preserving Face Manipulation Detection.- 22. Face Manipulation in Operational Systems.- Part IV - Open Issues, Trends, and Challenges: 23. All: Future trends in face Manipulation and Fake Detection.

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Book SynopsisThis book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems. Table of ContentsDGM4MICCAI 2021 - Image-to-Image Translation, Synthesis.- Frequency-Supervised MRI-to-CT Image Synthesis.- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain.- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images.- Bridging the gap between paired and unpaired medical image translation.- Conditional generation of medical images via disentangled adversarial inference. -CT-SGAN: Computed Tomography Synthesis GAN.- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference.- CaCL: class-aware codebook learning for weakly supervised segmentation on diffuse image patterns.- BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification.- Evaluating GANs in medical imaging.- DGM4MICCAI 2021 - AdaptOR challenge.- Improved Heatmap-based Landmark Detection.- Cross-domain Landmarks Detection in Mitral Regurgitation.- DALI 2021.- Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph.- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency.- One-shot Learning for Landmarks Detection.- Compound Figure Separation of Biomedical Images with Side Loss.- Data Augmentation with Variational Autoencoders and Manifold Sampling.- Medical image segmentation with imperfect 3D bounding boxes.- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images.- How Few Annotations are Needed for Segmentation using a Multi-planar U-Net?.- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation.- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning.- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions.- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation.- Label Noise in Segmentation Networks : Mitigation Must Deal with Bias.- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization.- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation.

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Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.*The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application. * The conference was originally planned for December 2021, but was postponed to 2022.

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Book SynopsisThis book, geared towards both students and professionals, examines the synthesis of artificial intelligence (AI) and psychology in detecting mis-/disinformation in digital media content, and suggests practical means to intervene and curtail this current global ‘infodemic’. This interdisciplinary book explores technological, psychological, philosophical, and linguistic insights into the nature of truth and deception, trust and credibility, cognitive biases and logical fallacies and how, through AI and human intervention, content users can be alerted to the presence of deception. The author investigates how AI can mimic the procedures and know-hows of humans, showing how AI can help spot fakes and how AI tools can work to debunk rumors and fact-check. The book describes how AI detection systems work and how they fit with broader societal and individual concerns. Each chapter focuses attention on key concepts and their inter-connection. The first part of the book seeks theoretical footing to understand our interactions with new information and reviews relevant empirical findings in behavioral sciences. The second part is about applied knowledge. The author looks at several known practices that guard us against deception, and provides several real-world examples of manipulative persuasive techniques in advertising, political propaganda, and public relations. She provides links to the downloadable executable files to three AI applications (clickbait, satire, and falsehood detectors) via LiT.RL GitHub, an open access repository. The book is useful to students and professionals studying AI and media studies as well as library and information professionals. Examines how artificial intelligence (AI) and psychology can aid in detecting mis-/disinformation and the language of deceit in digital media content; Suggests practical computational means to intervene and curtail the global ‘infodemic’ of fake news; Presents how AI can sift, sort, and shuffle digital content, to reduce the amount of content needed to be reviewed by humans. Table of ContentsIntroduction.- Infodemic in the Digital Media Content.- Part I. Human Nature of Deception and Perceptions of Truth.- Psychology of Mis-/Disinformation and Language of Deceit.- Library and Information Science on Credibility and Trust in Computing.- Philosophies of Truth.- Part II. Applied Practices and Artificial Intelligence (AI).- Investigation in Law Enforcement, Journalism, and Sciences.- Manipulation in Marketing, Advertising, and Public Relations.- Artificially Intelligent Solutions: Detection, Debunking, Fact-Checking.- Lessons for Infodemic Control and Future of Digital Content Verification.- Conclusion.

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Book SynopsisThis volume constitutes selected papers presented at the 28th International Conference on Systems, Signals and Image Processing, IWSSIP 2021, held in Bratislava, Slovakia, in June 2021. Due to the COVID-19 pandemic the conference was held online. The presented 14 full and 5 short papers were thorougly reviewed and selected from the 76 submissions. The papers focus on various aspects of advanced signal processing in different scientific areas, including filter design, Fourier and other transforms, feature extraction, machine learning and system adaptation to user-oriented products like 5G networks, IoT, virtual teleport or tele-surgery operations.Table of ContentsSegmentation and Quantification of Bi-Ventricles and Myocardium Using 3D SERes-U-Net.- Fingerprint Classification based on the Henry System via ResNet.- Segmentation of significant regions in retinal images: perspective of U-Net network through a comparative approach.- Presenting a system to aid on the scintigraphy bone metastasis analysis using DICOM files.- Viewpoint selection for fibrous structures in a pre-operative context: application to cranial nerves surrounding skull base tumors.- Gait Recognition with DensePose Energy Images.- Adaptive IIR Filtering for System Identification applying the method by Nelder and Mead.- Event-based Looming Objects Detection.- Moment Transform-Based Compressive Sensing in Image Processing.-Classification of Toxic Ornamental Plants for Domestic Animals using CNN.- Deep learning-based detection of seedling development from indoor to outdoor.- Banana Ripening Classification using Computer Vision: Preliminary Results.- Energy Reconstruction Techniques in TileCal under High Pile-up Conditions Fast Algorithm for Dyslexia Detection.- Automatic recognition of Native advertisements for the Slovak language Document Filter for Writer Identification.- An Approach for BCI using Motor Imagery Based on Wavelet Transform and Convolutional Neural Network.- Advanced Scene Sensing for Virtual Teleconference Supervised Mixture Analysis and Source Detection from Multimodal Measurements.

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Book SynopsisThis book gathers outstanding research papers presented at the International Conference on Intelligent Vision and Computing (ICIVC 2021), held online during October 03–04, 2021. ICIVC 2021 is organised by Sur University, Oman. The book presents novel contributions in intelligent vision and computing and serves as reference material for beginners and advanced research. The topics covered are intelligent systems, intelligent data analytics and computing, intelligent vision and applications collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal natural language processing.Table of ContentsHandwritten Bengali Digit Classification using Deep Learning.- IOT Based COVID Patient Health Monitoring System In Quarantine.- Self-attention Convolution for Sparse to Dense Depth Completion.- Using Algorithm in Parametric Design as an Approach to Inspire Nature in Architectural Design.- Docker Container Orchestration Management in Cloud Computing.- Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index.

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Book SynopsisFace detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. We then present two multiple instance learning schemes for face detection, multiple instance learning boosting (MILBoost) and winner-take-all multiple category boosting (WTA-McBoost). MILBoost addresses the uncertainty in accurately pinpointing the location of the object being detected, while WTA-McBoost addresses the uncertainty in determining the most appropriate subcategory label for multiview object detection. Both schemes can resolve the ambiguity of the labeling process and reduce outliers during training, which leads to improved detector performances. In many applications, a detector trained with generic data sets may not perform optimally in a new environment. We propose detection adaption, which is a promising solution for this problem. We present an adaptation scheme based on the Taylor expansion of the boosting learning objective function, and we propose to store the second order statistics of the generic training data for future adaptation. We show that with a small amount of labeled data in the new environment, the detector's performance can be greatly improved. We also present two interesting applications where boosting learning was applied successfully. The first application is face verification for filtering and ranking image/video search results on celebrities. We present boosted multi-task learning (MTL), yet another boosting learning algorithm that extends MILBoost with a graphical model. Since the available number of training images for each celebrity may be limited, learning individual classifiers for each person may cause overfitting. MTL jointly learns classifiers for multiple people by sharing a few boosting classifiers in order to avoid overfitting. The second application addresses the need of speaker detection in conference rooms. The goal is to find who is speaking, given a microphone array and a panoramic video of the room. We show that by combining audio and visual features in a boosting framework, we can determine the speaker's position very accurately. Finally, we offer our thoughts on future directions for face detection. Table of Contents: A Brief Survey of the Face Detection Literature / Cascade-based Real-Time Face Detection / Multiple Instance Learning for Face Detection / Detector Adaptation / Other Applications / Conclusions and Future WorkTable of ContentsA Brief Survey of the Face Detection Literature.- Cascade-based Real-Time Face Detection.- Multiple Instance Learning for Face Detection.- Detector Adaptation.- Other Applications.- Conclusions and Future Work.

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Book SynopsisBeing able to recover the shape of 3D deformable surfaces from a single video stream would make it possible to field reconstruction systems that run on widely available hardware without requiring specialized devices. However, because many different 3D shapes can have virtually the same projection, such monocular shape recovery is inherently ambiguous. In this survey, we will review the two main classes of techniques that have proved most effective so far: The template-based methods that rely on establishing correspondences with a reference image in which the shape is already known, and non-rigid structure-from-motion techniques that exploit points tracked across the sequences to reconstruct a completely unknown shape. In both cases, we will formalize the approach, discuss its inherent ambiguities, and present the practical solutions that have been proposed to resolve them. To conclude, we will suggest directions for future research. Table of Contents: Introduction / Early Approaches to Non-Rigid Reconstruction / Formalizing Template-Based Reconstruction / Performing Template-Based Reconstruction / Formalizing Non-Rigid Structure from Motion / Performing Non-Rigid Structure from Motion / Future DirectionsTable of ContentsIntroduction.- Early Approaches to Non-Rigid Reconstruction.- Formalizing Template-Based Reconstruction.- Performing Template-Based Reconstruction.- Formalizing Non-Rigid Structure from Motion.- Performing Non-Rigid Structure from Motion.- Future Directions.

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Book SynopsisAs networks of video cameras are installed in many applications like security and surveillance, environmental monitoring, disaster response, and assisted living facilities, among others, image understanding in camera networks is becoming an important area of research and technology development. There are many challenges that need to be addressed in the process. Some of them are listed below: - Traditional computer vision challenges in tracking and recognition, robustness to pose, illumination, occlusion, clutter, recognition of objects, and activities; - Aggregating local information for wide area scene understanding, like obtaining stable, long-term tracks of objects; - Positioning of the cameras and dynamic control of pan-tilt-zoom (PTZ) cameras for optimal sensing; - Distributed processing and scene analysis algorithms; - Resource constraints imposed by different applications like security and surveillance, environmental monitoring, disaster response, assisted living facilities, etc. In this book, we focus on the basic research problems in camera networks, review the current state-of-the-art and present a detailed description of some of the recently developed methodologies. The major underlying theme in all the work presented is to take a network-centric view whereby the overall decisions are made at the network level. This is sometimes achieved by accumulating all the data at a central server, while at other times by exchanging decisions made by individual cameras based on their locally sensed data. Chapter One starts with an overview of the problems in camera networks and the major research directions. Some of the currently available experimental testbeds are also discussed here. One of the fundamental tasks in the analysis of dynamic scenes is to track objects. Since camera networks cover a large area, the systems need to be able to track over such wide areas where there could be both overlapping and non-overlapping fields of view of the cameras, as addressed in Chapter Two: Distributed processing is another challenge in camera networks and recent methods have shown how to do tracking, pose estimation and calibration in a distributed environment. Consensus algorithms that enable these tasks are described in Chapter Three. Chapter Four summarizes a few approaches on object and activity recognition in both distributed and centralized camera network environments. All these methods have focused primarily on the analysis side given that images are being obtained by the cameras. Efficient utilization of such networks often calls for active sensing, whereby the acquisition and analysis phases are closely linked. We discuss this issue in detail in Chapter Five and show how collaborative and opportunistic sensing in a camera network can be achieved. Finally, Chapter Six concludes the book by highlighting the major directions for future research. Table of Contents: An Introduction to Camera Networks / Wide-Area Tracking / Distributed Processing in Camera Networks / Object and Activity Recognition / Active Sensing / Future Research DirectionsTable of ContentsAn Introduction to Camera Networks.- Wide-Area Tracking.- Distributed Processing in Camera Networks.- Object and Activity Recognition.- Active Sensing.- Future Research Directions.

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Book SynopsisIn its early years, the field of computer vision was largely motivated by researchers seeking computational models of biological vision and solutions to practical problems in manufacturing, defense, and medicine. For the past two decades or so, there has been an increasing interest in computer vision as an input modality in the context of human-computer interaction. Such vision-based interaction can endow interactive systems with visual capabilities similar to those important to human-human interaction, in order to perceive non-verbal cues and incorporate this information in applications such as interactive gaming, visualization, art installations, intelligent agent interaction, and various kinds of command and control tasks. Enabling this kind of rich, visual and multimodal interaction requires interactive-time solutions to problems such as detecting and recognizing faces and facial expressions, determining a person's direction of gaze and focus of attention, tracking movement of the body, and recognizing various kinds of gestures. In building technologies for vision-based interaction, there are choices to be made as to the range of possible sensors employed (e.g., single camera, stereo rig, depth camera), the precision and granularity of the desired outputs, the mobility of the solution, usability issues, etc. Practical considerations dictate that there is not a one-size-fits-all solution to the variety of interaction scenarios; however, there are principles and methodological approaches common to a wide range of problems in the domain. While new sensors such as the Microsoft Kinect are having a major influence on the research and practice of vision-based interaction in various settings, they are just a starting point for continued progress in the area. In this book, we discuss the landscape of history, opportunities, and challenges in this area of vision-based interaction; we review the state-of-the-art and seminal works in detecting and recognizing the human body and its components; we explore both static and dynamic approaches to "looking at people" vision problems; and we place the computer vision work in the context of other modalities and multimodal applications. Readers should gain a thorough understanding of current and future possibilities of computer vision technologies in the context of human-computer interaction.Table of ContentsPreface.- Acknowledgments.- Figure Credits.- Introduction.- Awareness: Detection and Recognition.- Control: Visual Lexicon Design for Interaction.- Multimodal Integration.- Applications of Vision-Based Interaction.- Summary and Future Directions.- Bibliography.- Authors' Biographies.

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