Computer vision Books

Book SynopsisComputer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features.Table of ContentsChapter 1. Image Capture and RepresentationChapter 2. Image Pre-ProcessingChapter 3. Global and Regional FeaturesChapter 4. Local Feature Design Concepts, Classification, and LearningChapter 5. Taxonomy Of Feature Description AttributesChapter 6. Interest Point Detector and Feature Descriptor SurveyChapter 7. Ground Truth Data, Data, Metrics, and AnalysisChapter 8. Vision Pipelines and OptimizationsAppendix A. Synthetic Feature AnalysisAppendix B. Survey of Ground Truth DatasetsAppendix C. Imaging and Computer Vision ResourcesAppendix D. Extended SDM Metrics

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Book SynopsisWhile the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, "Introduction to Biometrics" is the first textbook to introduce the fundamentals of Biometrics to undergraduate/graduate students.Table of ContentsIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.

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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 SynopsisFace analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve.This book describes the progress towards this goal, from a core building block – landmark detection – to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.Table of ContentsPreface xiRomain BELMONTE and Benjamin ALLAERT Part 1. Facial Landmark Detection 1 Introduction to Part 1 3Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA Chapter 1. Facial Landmark Detection 13Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 1.1. Facial landmark detection in still images 14 1.1.1.Generativeapproaches 14 1.1.2.Discriminative approaches 18 1.1.3.Deep learningapproaches 24 1.1.4.Handlingchallenges 34 1.1.5.Summary 40 1.2.Extendingfacial landmarkdetectionto videos 41 1.2.1.Trackingby detection 41 1.2.2.Box, landmarkand pose tracking 43 1.2.3.Adaptive approaches 45 1.2.4. Joint approaches 46 1.2.5. Temporal constrained approaches 47 1.2.6.Summary 49 1.3.Discussion 50 1.4.References 52 Chapter 2. Effectiveness of Facial Landmark Detection 67Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 2.1.Overview 68 2.2.Datasets and evaluationmetrics 69 2.2.1. Image and videodatasets 69 2.2.2. Face preprocessing and data augmentation 73 2.2.3.Evaluationmetrics 75 2.2.4.Summary 77 2.3. Image andvideobenchmarks 77 2.3.1. Compiled results on 300W 77 2.3.2. Compiled results on 300VW 79 2.4.Cross-dataset benchmark 80 2.4.1.Evaluationprotocol 80 2.4.2.Comparisonof selected approaches 82 2.5.Discussion 86 2.6.References 88 Chapter 3. Facial Landmark Detection with Spatio-temporal Modeling 93Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 3.1.Overview 94 3.2.Spatio-temporalmodelingreview 95 3.2.1.Hand-craftedapproaches 95 3.2.2.Deep learningapproaches 97 3.2.3.Summary 103 3.3.Architecturedesign 104 3.3.1. Coordinate regression networks 104 3.3.2.Heatmapregressionnetworks 106 3.4.Experiments 107 3.4.1.Datasets andevaluationprotocols 107 3.4.2. Implementationdetails 108 3.4.3.EvaluationonSNaP-2DFe 109 3.4.4. Evaluation on 300VW 111 3.4.5.Comparisonwith existingmodels 112 3.4.6. Qualitative results 112 3.4.7.Propertiesof the networks 114 3.5.Design investigations 114 3.5.1.Encoder-decoder 115 3.5.2. Complementarity between spatial and temporal information 117 3.5.3. Complementarity between local and global motion 119 3.6.Discussion 122 3.7.References 123 Conclusion to Part 1 133Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA Part 2. Facial Expression Analysis 147 Introduction to Part 2 149Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA Chapter 4. Extraction of Facial Features 157Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 4.1. Introduction 157 4.2.Face detection 158 4.2.1.Point-of-interestdetectionalgorithms 160 4.2.2.Face alignment approaches 162 4.2.3.Synthesis 166 4.3.Face normalization 166 4.3.1.Dealingwith headpose variations 167 4.3.2.Dealingwith facial occlusions 170 4.3.3.Synthesis 172 4.4.Extractionof visual features 172 4.4.1.Facial appearancefeatures 172 4.4.2.Facial geometric features 174 4.4.3. Facial dynamics features 175 4.4.4.Facial segmentationmodels 177 4.4.5.Synthesis 179 4.5. Learning methods 179 4.5.1.Classification versus regression 180 4.5.2.Fusionmodel 182 4.5.3.Synthesis 184 4.6.Conclusion 185 4.7.References 186 Chapter 5. Facial Expression Modeling 191Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 5.1. Introduction 191 5.2.Modelingof the affective state 192 5.2.1.Categoricalmodeling 192 5.2.2.Dimensionalmodeling 194 5.2.3.Synthesis 196 5.3. The challenges of facial expression recognition 197 5.3.1. The variation of the intensity of the expressions 197 5.3.2.Variationof facialmovement 199 5.3.3.Synthesis 200 5.4.The learningdatabases 201 5.4.1. Improvementof learningdata 201 5.4.2. Comparison of learning databases 203 5.4.3.Synthesis 205 5.5. Invariance to facial expression intensities 206 5.5.1.Macro-expression 206 5.5.2.Micro-expression 208 5.5.3.Synthesis 209 5.6. Invarianceto facialmovements 211 5.6.1. Pose variations (PV) and large displacements (LD) 211 5.6.2.Synthesis 214 5.7.Conclusion 215 5.8.References 216 Chapter 6. Facial Motion Characteristics 223Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 6.1. Introduction 223 6.2.Characteristics of the facialmovement 225 6.2.1. Local constraint of magnitude and direction 226 6.2.2. Local constraint of the motion distribution 228 6.2.3.Motionpropagationconstraint 230 6.3.LMP 232 6.3.1. Local consistency of the movement 233 6.3.2.Consistencyof local distribution 236 6.3.3. Coherence in the propagationof themovement 238 6.4.Conclusion 241 6.5.References 242 Chapter 7. Micro- and Macro-Expression Analysis 243Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 7.1. Introduction 243 7.2. Definition of a facial segmentation model 244 7.3.Feature vector construction 247 7.3.1.Motionfeaturesvector 247 7.3.2.Geometric featuresvector 248 7.3.3.Features fusion 249 7.4. Recognition process 250 7.5. Evaluation on micro- and macro-expressions 251 7.5.1.Learningdatabases 252 7.5.2. Micro-expression recognition 253 7.5.3. Macro-expressions recognition 255 7.5.4. Synthesis of experiments on micro- and macro-expressions 258 7.6. Same expression with different intensities 260 7.6.1.Data preparation 260 7.6.2.Fractional time analysis 263 7.6.3.Analysis on a different time frame 264 7.6.4. Synthesis of experiments on activation segments 265 7.7.Conclusion 265 7.8.References 266 Chapter 8. Towards Adaptation to Head Pose Variations 271Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 8.1. Introduction 271 8.2.Learningdatabase challenges 273 8.3. Innovative acquisition system (SNaP-2DFe) 274 8.4. Evaluation of face normalization methods 276 8.4.1. Does the normalization preserve the facial geometry? 277 8.4.2. Does normalization preserve facial expressions? 280 8.5.Conclusion 283 8.6.References 284 Conclusion to Part 2 287Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA List of Authors 293 Index 295

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Book SynopsisAs the second volume of the "Digital Oil & Gas Pipeline: Research and Practice" series of monographs, this book introduces the implementation strategies, examples and technical roadmaps of two important aspects of the Digital Oil & Gas Pipeline construction: pipeline real-time data integration and pipeline network virtual reality system. Two example of pipeline real-time data integration are elaborated: integration of pipeline WebGIS (Geographic Information System) and pipeline SCADA (Supervisory Control and Data Acquisition) via OPC (OLE for Process Control) technology, integration of pipeline network virtual reality system and pipeline SCADA via OPC, JNI (Java Native Interface) and SAI (Scene Access Interface). The pipeline network virtual reality system aims for the pipeline virtual expression, interaction, and 3D visual management. It can be used for pipeline route visual design and plan, immersive pipeline industry training, remote visual supervision and control, etc. The implementation details of the pipeline network virtual reality system, including 3D pipeline and terrain modeling with X3D (Extensible 3D) technology, improving large-scene display performance and speed in the network environment using LOD (Level of Detail) technology, interaction of virtual pipeline scenes, and pipeline 3D visual monitoring, are also introduced. The knowledge and experience delivered by this book will provide useful reference for the readers from the industries of oil & gas pipeline, GIS, Virtual Reality, industrial control, etc.Table of Contents

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Book SynopsisThis comprehensive reference provides easy access to relevant information on all aspects of Computer Vision. An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume.Each entry includes synonyms, a definition and discussion of the topic, and a robust bibliography. Extensive cross-references to other entries support efficient, user-friendly searches for immediate access to relevant information. Entries were peer-reviewed by a distinguished international advisory board, both scientifically and geographically diverse, ensuring balanced coverage. Over 3700 bibliographic references for further reading enable deeper exploration into any of the topics covered.The content of Computer Vision: A Reference Guide is expository and tutorial, making the book a practical resource for students who are considering entering the field, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.Table of ContentsOver 240 entries organized A to Z.

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Book SynopsisThis book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.Table of ContentsPart I. A Panorama of Computational Intelligence in Super-Resolution Imaging.- Chapter 1. Introduction to Computational Intelligence and Super-Resolution.- Chapter 2. Review on Fuzzy Logic Systems with Super-Resolved Imaging and Metaheuristics for Medical Applications.- Chapter 3. Super-Resolution with Deep Learning Techniques-A Review.- Chapter 4. A Comprehensive Review of CAD Systems in Ultrasound and Elastography for Breast Cancer Diagnosis.- Part II. State-of-the-Art Computational Intelligence in Super-Resolution Imaging.- Chapter 5. Pictorial Image Synthesis from Text and Its Super-Resolution using Generative Adversarial Networks.- Chapter 6. Analysis of Lossy and Lossless Compression Algorithms for Computed Tomography Medical Images Based on Bat and Simulated Annealing Optimization Techniques.- Chapter 7. Super resolution-based Human-Computer Interaction System for Speech and Hearing Impaired using Real-Time Hand Gesture Recognition System.- Chapter 8. Lossy Compression of Noisy Images Using Autoencoders for Computer Vision Applications.- Chapter 9. Recognition of Handwritten Nandinagari Palm Leaf Manuscript Tex.- Chapter 10. Deep Image Prior and Structural Variation Based Super-Resolution Network for Fluorescein Fundus Angiography Images.- Chapter 11. Lightweight Spatial Geometric Models Assisting Shape Description and Retrieval and Relative Global Optimum Based Measure for Fusion.- Chapter 12. Dual-Tree Complex Wavelet Transform and Deep CNN-based Super-Resolution for Video Inpainting with Application to Object Removal and Error Concealment.- Chapter 13. Super-Resolution Imaging and Intelligent solution for Classification, Monitoring and Diagnosis of Alzheimer's Disease.- Chapter 14. Image Enhancement using Non-Local Prior and Gradient Residual Minimization for Improved Visualization of Deep Underwater Image.- Chapter 15. Relative Global Optimum Based Measure for Fusion Technique in Shearlet Transform Domain for Prognosis of Alzheimer Disease.

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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 book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.Table of ContentsIntroduction.- Fundamentals. - Bayesian models for Dynamic Scene Analysis.- Multi-layer label fusion models.- Multitemporal data analysis with Marked Point Processes. - Multi-level object population analysis with an EMPP model.- Concluding Remarks.- References.- Index.

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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 shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.Table of ContentsChapter1. Introduction.- Chapter2. Existing Research in Video Surveillance System .- Chapter3. Background Modeling.- Chapter4. Object Tracking.- Chapter5. Summary of Book.

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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 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 SynopsisThis book constitutes refereed proceedings of the Second International Conference on Smart Technologies, Systems and Applications, held in Quito, Ecuador, in December 2021. Due to the COVID-19 pandemic the conference was held in a hybrid format. The 29 full papers along with 1 short paper presented were carefully reviewed and selected from 104 submissions. The papers of this volume are organized in topical sections on smart technologies; smart systems; smart trends and applications.Table of ContentsSmart Technologies.- Smart Systems.- Smart Trends and Applications.

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Book SynopsisComputer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.Table of ContentsPreface.- Acknowledgments.- Introduction.- Features and Classifiers.- Neural Networks Basics.- Convolutional Neural Network.- CNN Learning.- Examples of CNN Architectures.- Applications of CNNs in Computer Vision.- Deep Learning Tools and Libraries.- Conclusion.- Bibliography.- Authors' Biographies.

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Book SynopsisThis book constitutes the refereed proceedings of the 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022, held in Qingdao, China, in July 2022. The 37 full papers and 6 short papers presented were carefully reviewed and selected from 57 submissions. They are organized in topical sections on Machine Learning, Data Mining, Multiagent Systems, Social Computing, Blockchain Technology, Game Theory and Emotion, Pattern Recognition, Image Processing and Applications.Table of ContentsMachine Learning.- An AdaBoost Based- Deep Stochastic Configuration Network.- Comparative Study of Chaos-embedded Particle Swarm Optimization.- A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification.- Inductive Light Graph Convolution Network for Text Classification based on Word-Label Graph.- Sparse Subspace Clustering Based on Adaptive Parameter Training.- A Hybrid Multi-objective Optimization Algorithm with Improved Neighborhood Rough Sets for Feature Selection.- Augmenting Convolution Neural Networks By Utilizing Attention Mechanism for Knowledge Tracing.- Data Mining.- Interactive Mining of User-Preferred Co-Location Patterns Based on SVM.- Classification between Rumors and Explanations of Rumors based on Common and Difference Subsequences of Sentences.- Double-Channel Multi-layer Information Fusion for Text Matching.- Augmenting Context Representation with Triggers Knowledge for Relation Extraction.- Does Large Pretrained Dataset always help? On the Effect of Dataset Size on Big Transfer Model.- Using Multi-level Attention based on Concept Embedding Enrichen Short Text to Classification.- Multiagent Systems.- Pre-loaded Deep-Q Learning.- Resource Scheduling for Human-Machine Collaboration in Multiagent Systems.- Social Computing.- Automatic Generation and Analysis of Role Relation Network from Emergency Plans.- Information Tracking Extraction for Emergency Scenario Response.- Neighborhood Network for Aspect-based Sentiment Analysis.- A Hybrid Parallel Algorithm with Multiple Improved Strategies.- Blockchain Technology.- Research on Blockchain Privacy Protection Mechanism in Financial Transaction Services based on Zero-knowledge Proof and Federal Learning.- A Distributed Supply Chain Architecture Based on Blockchain Technology.- Game Theory and Emotion.- A Game-Theoretic Analysis of Impulse Purchase.- A Self-supervised Strategy for the Robustness of VQA Models.- Employing Contrastive Strategies for Multi-label Textual Emotion Recognition.- Pattern Recognition.- Fault Localization Based on Deep Neural Network and Execution Slicing.- Defect Detection and Classification of Strip Steel based on Improved VIT Model.- ROSES: A novel semi-supervised feature selector.- Improving speech emotion recognition by fusing pre-trained and acoustic features using Transformer and BiLSTM.- A Pear Leaf Diseases Image Recognition Model Based on Capsule Network.- Software Defect Prediction Method Based on Cost-Sensitive Random Forest.- Fault Diagnosis of Sewage Treatment Equipment Based on Feature Selection.- Attention Adaptive Chinese Named Entity Recognition Based on Vocabulary Enhancement.- Image Processing.- A HEp-2 Cell Image Classification Model Based on Deep Residual Shrinkage Network Combined with Dilated Convolution.- A Method on Online Learning Video Recommendation Method Based on Knowledge Graph.- Data Transformation for Super-Resolution on Ocean Remote Sensing Images.- A Novel RGBD Image Superpixel Segmentation Intergrated Depth Map Quality.- Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial Networks.- Multi-instance Learning for Semantic Image Analysis.- High-resolution Remote Sensing Image Semantic Segmentation Method Based on Improved Encoder-Decoder Convolutional Neural Network.- Applications.- A Method for AGV Double-cycling Scheduling at Automated Container Terminals.- Predicting Student Performance In Online Learning Using A Highly Efficient Gradient Boosting Decision Tree.- Adapting on Road Traffic-oriented Controlled Optimization of Phases to Heterogeneous Intersections.- A Method of Garbage Quantity Prediction based on Population Change.

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Book SynopsisThe proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc. Table of ContentsBrave New Ideas.- Biomedical and Assistive Technology.- Multimedia.- Deep Learning.- Image Processing for Cultural Heritage.- Robot Vision.

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Book SynopsisThis book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations. Image processing technology has progressed significantly in recent years, and it has been commercialized worldwide to provide superior performance with enhanced computer/machine vision, video processing, and pattern recognition capabilities. Meanwhile, machine learning systems like CNN and CapsNet get popular to provide better model hierarchical relationships and attempts to more closely mimic biological neural organization. As machine learning systems prosper, image processing and machine learning techniques will be tightly intertwined and continuously promote each other in real-world settings. Adopting this trend, however, the image processing researchers are faced with few image reconstruction, analysis, and segmentation challenges. On the application side, the orientation of the image features and noise removal has become a huge burden.

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Book SynopsisThe volume LNAI 13546 constitutes the refereed proceedings of the 23rd Annual Conference Towards Autonomous Robotic Systems, TAROS 2022, held in Culham, UK, in September 2022. The 14 full papers and 10 short papers were carefully reviewed and selected from 38 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: Robotic Grippers and Manipulation; Soft Robotics, Sensing and Mobile Robots; Robotic Learning, Mapping and Planning; Robotic Systems and Applications.Table of ContentsA distributed approach to haptic simulation.- A Novel Two-Hand-Inspired Hybrid Robotic End-Effector Fabricated Using 3D Printing.- Investigating the relationship between posture and safety in teleoperational tasks: A pilot study in improved operational safety through enhanced human-machine interaction.- Design and Analysis of an End Effector Using the Fin Ray Structure for Integrated Limb Mechanisms.- Trigger-Assisted Ambidextrous Control Framework for Teleoperation of Two Legged Manipulators.- Teleoperating a Legged Manipulator through Whole-Body Control.- In-silico Design and Computational Modelling of Electroactive Polymer based Soft Robotics.- Exploration of Underwater Storage Facilities with Swarm of Micro-Surface Robots.- Characterization of an Inflatable Soft Actuator and Tissue Interaction for In Vitro Mechanical Stimulation of Tissue.- EMap: Real-time terrain estimation.- Design and Preliminary In-Classroom Evaluation\\of a Low-Cost Educational Mobile Robot.- Internal State-based Risk Assessment for Robots in Hazardous Environment.- Investigating Scene Visibility Estimation within ORB-SLAM3.- Tactile and Proprioceptive Online Learning in Robotic Contour Following.- Learning cooperative behaviours in adversarial multi-agent systems.- Task Independent Safety Assessment for Reinforcement Learning.- Sensing Anomalies as Potential Hazards: Datasets and Benchmarks.- Integration and robustness analysis of the Buzz swarm programming language with the Pi-puck robot platform.- Implementing and assessing a remote teleoperation setup with a Digital Twin using cloud networking.- Agent-Based Simulation of Multi-Robot Soil Compaction Mapping.- A-EMS: An Adaptive Emergency Management System for Autonomous Agents in Unforeseen Situations.- Towards Scalable Multi-Robot Systems by Partitioning the Task Domain.- Effectiveness of brush operational parameters for robotic debris removal.- Automatic, Vision-Based Tool Changing Solution for Dexterous Teleoperation Robots in a Nuclear Glovebox

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Book SynopsisThis book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. Table of ContentsDetecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification.- Benchmarking Transformers for Medical Image Classification.- Supervised domain adaptation using gradients transfer for improved medical image analysis.- Stain-AgLr: Stain Agnostic Learning for Computational Histopathology using Domain Consistency and Stain Regeneration Loss.- MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation.- Unsupervised site adaptation by intra-site variability alignment.- Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining.- POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.- Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging.- Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images.- Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts.- Adaptive Optimization with Fewer Epochs Improves Across-Scanner Generalization of U-Net based Medical Image Segmentation.- CateNorm: Categorical Normalization for Robust Medical Image Segmentation.

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Book SynopsisThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.Table of ContentsExpanding Language-Image Pretrained Models for General Video Recognition.- Hunting Group Clues with Transformers for Social Group Activity Recognition.- Contrastive Positive Mining for Unsupervised 3D Action Representation Learning.- Target-Absent Human Attention.- Uncertainty-Based Spatial-Temporal Attention for Online Action Detection.- Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows.- Rethinking Zero-Shot Action Recognition: Learning from Latent Atomic Actions.- Mining Cross-Person Cues for Body-Part Interactiveness Learning in HOI Detection.- Collaborating Domain-Shared and Target-Specific Feature Clustering for Cross-Domain 3D Action Recognition.- Is Appearance Free Action Recognition Possible?.- Learning Spatial-Preserved Skeleton Representations for Few-ShotAction Recognition.- Dual-Evidential Learning for Weakly-Supervised Temporal ActionLocalization.- Global-Local Motion Transformer for Unsupervised Skeleton-BasedAction Learning.- AdaFocusV3: On Unified Spatial-Temporal Dynamic Video Recognition.- Panoramic Human Activity Recognition.- Delving into Details: Synopsis-to-Detail Networks for Video Recognition.- A Generalized & Robust Framework for Timestamp Supervision in Temporal Action Segmentation.- Few-Shot Action Recognition with Hierarchical Matching and Contrastive Learning.- PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens.- Scale-Aware Spatio-Temporal Relation Learning for Video Anomaly Detection.- Compound Prototype Matching for Few-Shot Action Recognition.- Continual 3D Convolutional Neural Networks for Real-Time Processing of Videos.- Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition.- Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection.- Action Quality Assessment with Temporal Parsing Transformer.- Entry-Flipped Transformer for Inference and Prediction of Participant Behavior.- Pairwise Contrastive Learning Network for Action Quality Assessment.- Geometric Features Informed Multi-Person Human-Object Interaction Recognition in Videos.- ActionFormer: Localizing Moments of Actions with Transformers.- SocialVAE: Human Trajectory Prediction Using Timewise Latents.- Shape Matters: Deformable Patch Attack.- Frequency Domain Model Augmentation for Adversarial Attack.- Prior-Guided Adversarial Initialization for Fast Adversarial Training.- Enhanced Accuracy and Robustness via Multi-Teacher Adversarial Distillation.- LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity.- A Large-Scale Multiple-Objective Method for Black-Box Attack against Object Detection.- GradAuto: Energy-Oriented Attack on Dynamic Neural Networks.- A Spectral View of Randomized Smoothing under Common Corruptions: Benchmarking and Improving Certified Robustness.- Improving Adversarial Robustness of 3D Point Cloud Classification Models.- Learning Extremely Lightweight and Robust Model with Differentiable Constraints on Sparsity and Condition Number.- RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN.- Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks.

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Book SynopsisThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.Table of ContentsAU-Aware 3D Face Reconstruction through Personalized AU-Specific Blendshape Learning.- B´ezierPalm: A Free Lunch for Palmprint Recognition.- Adaptive Transformers for Robust Few-Shot Cross-Domain Face Anti-Spoofing.- Face2Faceρ: Real-Time High-Resolution One-Shot Face Reenactment.- Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation.- BoundaryFace: A Mining Framework with Noise Label Self-Correction for Face Recognition.- Pre-training Strategies and Datasets for Facial Representation Learning.- Look Both Ways: Self-Supervising Driver Gaze Estimation and Road Scene Saliency.- MFIM: Megapixel Facial Identity Manipulation.- 3D Face Reconstruction with Dense Landmarks.- Emotion-Aware Multi-View Contrastive Learning for Facial Emotion Recognition.- Order Learning Using Partially Ordered Data via Chainization.- Unsupervised High-Fidelity Facial Texture Generation and Reconstruction.- Multi-Domain Learning for Updating Face Anti-Spoofing Models.- Towards Metrical Reconstruction of Human Faces.- Discover and Mitigate Unknown Biases with Debiasing Alternate Networks.- Unsupervised and Semi-Supervised Bias Benchmarking in Face Recognition.- Towards Efficient Adversarial Training on Vision Transformers.- MIME: Minority Inclusion for Majority Group Enhancement of AI Performance.- Studying Bias in GANs through the Lens of Race.- Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness.- Learning to Censor by Noisy Sampling.- An Invisible Black-Box Backdoor Attack through Frequency Domain.- FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification.- Attaining Class-Level Forgetting in Pretrained Model Using Few Samples.- Anti-Neuron Watermarking: Protecting Personal Data against Unauthorized Neural Networks.- An Impartial Take to the CNN vs Transformer Robustness Contest.- Recover Fair Deep Classification Models via Altering Pre-trained Structure.- Decouple-and-Sample: Protecting Sensitive Information in TaskAgnostic Data Release.- Privacy-Preserving Action Recognition via Motion Difference Quantization.- Latent Space Smoothing for Individually Fair Representations.- Parameterized Temperature Scaling for Boosting the Expressive Powerin Post-Hoc Uncertainty Calibration.- FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations.- Distilling the Undistillable: Learning from a Nasty Teacher.- SOS! Self-Supervised Learning over Sets of Handled Objects in Egocentric Action Recognition.- Egocentric Activity Recognition and Localization on a 3D Map.- Generative Adversarial Network for Future Hand Segmentation from Egocentric Video.- My View Is the Best View: Procedure Learning from Egocentric Videos.- GIMO: Gaze-Informed Human Motion Prediction in Context.- Image-Based CLIP-Guided Essence Transfer.- Detecting and Recovering Sequential DeepFake Manipulation.- Self-Supervised Sparse Representation for Video Anomaly Detection.

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Book SynopsisThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.Table of ContentsEfficient One-Stage Video Object Detection by Exploiting Temporal Consistency.- Leveraging Action Affinity and Continuity for Semi-Supervised Temporal Action Segmentation.- Spotting Temporally Precise, Fine-Grained Events in Video.- Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation.- Efficient Video Transformers with Spatial-Temporal Token Selection.- Long Movie Clip Classification with State-Space Video Models.- Prompting Visual-Language Models for Efficient Video Understanding.- Asymmetric Relation Consistency Reasoning for Video Relation Grounding.- Self-Supervised Social Relation Representation for Human Group Detection.- K-Centered Patch Sampling for Efficient Video Recognition.- A Deep Moving-Camera Background Model.- GraphVid: It Only Takes a Few Nodes to Understand a Video.- Delta Distillation for Efficient Video Processing.- MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning.- COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality.- E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context.- TDViT: Temporal Dilated Video Transformer for Dense Video Tasks.- Semi-Supervised Learning of Optical Flow by Flow Supervisor.- Flow Graph to Video Grounding for Weakly-Supervised Multi-step Localization.- Deep 360° Optical Flow Estimation Based on Multi-Projection Fusion.- MaCLR: Motion-Aware Contrastive Learning of Representations for Videos.- Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection.- Frozen CLIP Models Are Efficient Video Learners.- PIP: Physical Interaction Prediction via Mental Simulation with Span Selection.- Panoramic Vision Transformer for Saliency Detection in 360° Videos.- Bayesian Tracking of Video Graphs Using Joint Kalman Smoothing and Registration.- Motion Sensitive Contrastive Learning for Self-Supervised Video Representation.- Dynamic Temporal Filtering In Video Models.- Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification.- Temporal Lift Pooling for Continuous Sign Language Recognition.- MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes.- SiRi: A Simple Selective Retraining Mechanism for Transformer-Based Visual Grounding.- Cross-Modal Prototype Driven Network for Radiology Report Generation.- TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts.- SeqTR: A Simple Yet Universal Network for Visual Grounding.- VTC: Improving Video-Text Retrieval with User Comments.- FashionViL: Fashion-Focused Vision-and-Language Representation Learning.- Weakly Supervised Grounding for VQA in Vision-Language Transformers.- Automatic Dense Annotation of Large-Vocabulary Sign Language Videos.- MILES: Visual BERT Pre-training with Injected Language Semantics for Video-Text Retrieval.- GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval.- A Simple and Robust Correlation Filtering Method for Text-Based Person Search.

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Book SynopsisThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.Table of ContentsAccelerating Score-Based Generative Models with Preconditioned Diffusion Sampling.- Learning to Generate Realistic LiDAR Point Clouds.- RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds.- Diverse Image Inpainting with Normalizing Flow.- Improved Masked Image Generation with Token-Critic.- TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation.- Exploring Gradient-Based Multi-directional Controls in GANs.- Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition.- Neural Scene Decoration from a Single Photograph.- Outpainting by Queries.- Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes.- ChunkyGAN: Real Image Inversion via Segments.- GAN Cocktail: Mixing GANs without Dataset Access.- Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering.- Controllable Shadow Generation Using Pixel Height Maps.- Learning Where to Look – Generative NAS Is Surprisingly Efficient.- Subspace Diffusion Generative Models.- DuelGAN: A Duel between Two Discriminators Stabilizes the GAN Training.- MINER: Multiscale Implicit Neural Representation.- An Embedded Feature Whitening Approach to Deep Neural Network Optimization.- Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization.- Self-Supervised Learning of Visual Graph Matching.- Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models.- QISTA-ImageNet: A Deep Compressive Image Sensing Framework Solving ℓq-Norm Optimization Problem.- R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning.- Domain Generalization by Mutual-Information Regularization with Pre-trained Models.- Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning.- Neural-Sim: Learning to Generate Training Data with NeRF.- Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning.- Learned Variational Video Color Propagation.- Continual Variational Autoencoder Learning via Online Cooperative Memorization.- Learning to Learn with Smooth Regularization.- Incremental Task Learning with Incremental Rank Updates.- Batch-Efficient EigenDecomposition for Small and Medium Matrices.- Ensemble Learning Priors Driven Deep Unfolding for Scalable Video Snapshot Compressive Imaging.- Approximate Discrete Optimal Transport Plan with Auxiliary Measure Method.- A Comparative Study of Graph Matching Algorithms in Computer Vision.- Improving Generalization in Federated Learning by Seeking Flat Minima.- Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search: Tight or Not.- Transfer without Forgetting.- AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation.- Tackling Long-Tailed Category Distribution under Domain Shifts.- Doubly-Fused ViT: Fuse Information from Vision Transformer Doubly with Local Representation.

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Book SynopsisThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.Table of ContentsAdaptive Image Transformations for Transfer-Based Adversarial Attack.- Generative Multiplane Images: Making a 2D GAN 3D-Aware.- AdvDO: Realistic Adversarial Attacks for Trajectory Prediction.- Adversarial Contrastive Learning via Asymmetric InfoNCE.- One Size Does NOT Fit All: Data-Adaptive Adversarial Training.- UniCR: Universally Approximated Certified Robustness via Randomized Smoothing.- Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips.- Robust Network Architecture Search via Feature Distortion Restraining.- SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination.- Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack.- Data-Free Backdoor Removal Based on Channel Lipschitzness.- Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack.- Learning Energy-Based Models with Adversarial Training.- Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation.- Revisiting Outer Optimization in Adversarial Training.- Zero-Shot Attribute Attacks on Fine-Grained Recognition Models.- Towards Effective and Robust Neural Trojan Defenses via Input Filtering.- Scaling Adversarial Training to Large Perturbation Bounds.- Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack.- Generative Domain Adaptation for Face Anti-Spoofing.- MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition.- GaitEdge: Beyond Plain End-to-End Gait Recognition for Better Practicality.- UIA-ViT: Unsupervised Inconsistency-Aware Method Based on Vision Transformer for Face Forgery Detection.- Effective Presentation Attack Detection Driven by Face Related Task.- PPT: Token-Pruned Pose Transformer for Monocular and Multi-View Human Pose Estimation.- AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing.- P-STMO: Pre-trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation.- D&D: Learning Human Dynamics from Dynamic Camera.- Explicit Occlusion Reasoning for Multi-Person 3D Human Pose Estimation.- COUCH: Towards Controllable Human-Chair Interactions.- Identity-Aware Hand Mesh Estimation and Personalization from RGB Images.- C3P: Cross-Domain Pose Prior Propagation for Weakly Supervised 3D Human Pose Estimation.- Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields.- CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation.- DeciWatch: A Simple Baseline for 10Ö Efficient 2D and 3D Pose Estimation.- SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos.- PoseTrans: A Simple yet Effective Pose Transformation Augmentation for Human Pose Estimation.- Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement.- Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction.- Structural Triangulation: A Closed-Form Solution to Constrained 3D Human Pose Estimation.- Audio-Driven Stylized Gesture Generation with Flow-Based Model.- Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation

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Book SynopsisThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.Table of ContentsA Simple Approach and Benchmark for 21,000-Category Object Detection.- Knowledge Condensation Distillation.- Reducing Information Loss for Spiking Neural Networks.- Masked Generative Distillation.- Fine-Grained Data Distribution Alignment for Post-Training Quantization.- Learning with Recoverable Forgetting.- Efficient One Pass Self-Distillation with Zipf’s Label Smoothing.- Prune Your Model before Distill It.- Deep Partial Updating: Towards Communication Efficient Updating for On-Device Inference.- Patch Similarity Aware Data-Free Quantization for Vision Transformers.- L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training.- Streaming Multiscale Deep Equilibrium Models.- Symmetry Regularization and Saturating Nonlinearity for Robust Quantization.- SP-Net: Slowly Progressing Dynamic Inference Networks.- Equivariance and Invariance Inductive Bias for Learning from Insufficient Data.- Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance.- Event Neural Networks.- EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers.- PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators.- Disentangled Differentiable Network Pruning.- IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors.- Learning to Weight Samples for Dynamic Early-Exiting Networks.- AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets.- Adaptive Token Sampling for Efficient Vision Transformers.- Weight Fixing Networks.- Self-Slimmed Vision Transformer.- Switchable Online Knowledge Distillation.- ℓ∞-Robustness and Beyond: Unleashing Efficient Adversarial Training.- Multi-Granularity Pruning for Model Acceleration on Mobile Devices.- Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification.- Helpful or Harmful: Inter-Task Association in Continual Learning.- Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies.- SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks.- Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning.- Network Binarization via Contrastive Learning.- Lipschitz Continuity Retained Binary Neural Network.- SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning.- Soft Masking for Cost-Constrained Channel Pruning.- Non-uniform Step Size Quantization for Accurate Post-Training Quantization.- SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning.- Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously.- Towards Ultra Low Latency Spiking Neural Networks for Vision and Sequential Tasks Using Temporal Pruning.- Towards Accurate Network Quantization with Equivalent Smooth Regularizer.

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Book SynopsisThis book constitutes the proceedings of the 16th Chinese Conference on Biometric Recognition, CCBR 2022, which took place in Beijing, China, in November 2022.The 70 papers presented in this volume were carefully reviewed and selected from 115 submissions. The papers cover a wide range of topics such as Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Gesture and Action Recognition; Affective Computing and Human-Computer Interface; Speaker and Speech Recognition; Gait, Iris and Other Biometrics; Multi-modal Biometric Recognition and Fusion; Quality Evaluation and Enhancement of Biometric Signals; Animal Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.Table of ContentsFingerprint, Palmprint and Vein Recognition.- A Finger BiModal Fusion Algorithm based on Improved DenseNet.- A lightweight segmentation network based on extraction.- A novel multi-layered minutiae extractor based on OCT fingerprints.- An overview and forecast of biometric recognition technology used in forensic science.- Combining Band-Limited OTSDF Filter and Directional Representation for Palmprint Recognition.- Cross-Dataset Image Matching Network for Heterogeneous Palmprint Recognition.- DUAL MODE NEAR-INFRARED SCANNER FOR IMAGING DORSAL HAND VEINS.- Multi-Stream Convolutional Neural Networks Fusion for Palmprint Recognition.- Multi-view Finger Vein Recognition using Attention-based MVCNN.- SELECTIVE DETAIL ENHANCEMENT ALGORITHM FOR FINGER VEIN IMAGES.- SP-FVR: SuperPoint-based Finger Vein Recognition.- TransFinger: Transformer based Finger Tri-modal Biometrics.- Face Detection, Recognition and Tracking.- A Survey of Domain Generalization-based Face Anti-spoofing.- An Empirical Comparative Analysis of Africans with Asians using DCNN Facial Biometric Models.- Disentanglement of Deep Features for Adversarial Face Detection.- Estimation of Gaze-Following Based on Transformer and the Guiding Offset.- Learning Optimal Transport Mapping of Joint Distribution for Cross-Scenario Face Anti-Spooffing.- MLFW: A Database for Face Recognition on Masked Faces.- Multi-scale object detection algorithm based on adaptive feature fusion.- Sparsity-Regularized Geometric Mean Metric Learning for Kinship Verification.- YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing.- Gesture and Action Recognition.- Adaptive Joint Interdependency Learning for 2D Occluded Hand Pose Estimation.- Contrastive and Consistent Learning for Unsupervised Human Parsing.- Dynamic Hand Gesture Authentication Based on Improved Two-stream CNN.- Efficient Video Understanding-based Random Hand Gesture Authentication.- Multidimension Joint Networks for Action Recognition.- Multi-Level Temporal-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition.- Research on Gesture Recognition of Surface EMG Based on Machine Learning.- Affective Computing and Human-Computer Interface.- Adaptive Enhanced Micro-expression Spotting Network based on Multi-stage Features Extraction.- Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition.- Hemispheric Asymmetry Measurement Network for Emotion Classification.- Human Action Recognition Algorithm of Non-Local Two-Stream Convolution Network Based on Image Depth Flow.- Synthetic Feature Generative Adversarial Network for Motor Imagery Classification: Create Feature from Sampled Data.- Speaker and Speech Recognition.- An End-to-end Conformer-based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation.- ATRemix: An Auto-Tune Remix Dataset for Singer Recognition.- Low-resource speech keyword search based on residual neural network.- Online Neural Speaker Diarization with Core Samples.- Pose-unconstrainted 3D Lip Behaviometrics via Unsupervised Symmetry Correction.- Virtual Fully-Connected Layer for a Large-Scale Speaker Verification Dataset.- Gait, Iris and Other Biometrics.- A Simple Convolutional Neural Network for Small Sample Multi-lingual Offline Handwritten Signature Recognition.- Attention Skip Connection Dense Network for Accurate Iris Segmentation.- Gait Recognition with Various Data Modalities: A Review.- INCREMENTAL EEG BIOMETRIC RECOGNITION BASED ON EEG RELATION NETWORK.- Salient Foreground-Aware Network for Person Search.- Shoe print retrieval algorithm based on improved ecientnetV2.- Multi-modal Biometric Recognition and Fusion.- A novel dual-modal biometric recognition method based on weighted joint group sparse representation classification.- FINGER TRIMODAL FEATURES CODING FUSION METHOD.- Fusion of Gait and Face for Human Identification at the Feature Level.- Gait Recognition in Sensing Insoles: a study based on a Hybrid CNN-Attention-LSTM Network.- Identity Authentication Using a Multimodal Sensing Insole a Feasibility Study.- MDF-Net: Multimodal Deep Fusion for Large-scale Product Recognition.- Survey on Deep Learning based Fusion Recognition of Multimodal Biometrics.- Synthesizing Talking Face Videos with a Spatial Attention Mechanism.- Quality Evaluation and Enhancement of Biometric Signals.- Blind Perceptual Quality Assessment for Single Image Motion Deblurring.- Low-illumination Palmprint Image Enhancement Method Based On U-Net Neural Network.- Texture-guided multiscale feature learning network for palmprint image quality assessment.- Animal Biometrics.- An Adaptive Weight Joint Loss Optimization For Dog Face Recognition.- Improved YOLOv5 for Dense Wildlife Object Detection.- Self-Attention based Cross-level Fusion Network for Camou aged Object Detection.- Trustyworth, Privacy and Persondal Data Security.- Face Forgery Detection by Multi-dimensional Image Decomposition.- IrisGuard: Image Forgery Detection for Iris Anti-spooffing.- Multi-branch network with circle loss using voice conversion and channel robust data augmentation for synthetic speech detection.- Spoof Speech Detection Based on Raw Cross-dimension Interaction Attention Network.- Medical and Other Applications.- A Deformable Convolution Encoder with Multi-Scale Attention Fusion Mechanism for Classification of Brain Tumor MRI Images.- GI Tract Lesion Classification Using Multi-task Capsule Networks with Hierarchical Convolutional Layers.- Grading Diagnosis of Sacroiliitis in CT Scans Based on Radiomics and Deep Learning.- Noninvasive blood pressure waveform measurement method based on CNN-LSTM.- Recurrence Quantification Analysis of Cardiovascular System During Cardiopulmonary Resuscitation.- UAV AERIAL PHOTOGRAPHY TRAFFIC OBJECT DETECTION BASED ON LIGHTWEIGHT DESIGN AND FEATURE FUSION.- UMixer: A novel U-shaped convolutional mixer for multi-scale feature fusion in Medical Image Segmentation.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThis three-volume set, LNCS 13421, 13422 and 13423, constitutes the thoroughly refereed proceedings of the 6th International Joint Conference, APWeb-WAIM 2022, held in Nanjing, China, in August 2022.The 75 full papers presented together with 45 short papers, and 5 demonstration papers were carefully reviewed and selected from 297 submissions. The papers are organized around the following topics: Big Data Analytic and Management, Advanced database and web applications, Cloud Computing and Crowdsourcing, Data Mining, Graph Data and Social Networks, Information Extraction and Retrieval, Knowledge Graph, Machine Learning, Query processing and optimization, Recommender Systems, Security, privacy, and trust and Blockchain data management and applications, and Spatial and multi-media data.Table of ContentsResearch tracks. Big Data Analytic and Management.- Advanced database and web applications.- Cloud Computing and Crowdsourcing.- Data Mining.- Graph Data and Social Networks.- Information Extraction and Retrieval.- Knowledge Graph.- Machine Learning.- Query processing and optimization.- Recommender Systems.- Security, privacy, and trust& Blockchain data management and applications.- Spatial and multi-media data.- Demo papers.

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Book SynopsisThe 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.

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Book SynopsisThe 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. Table of ContentsRecognition: feature detection, indexing, matching, and shape representation.- datasets and performance analysis.

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Book SynopsisThis book constitutes the Third Diabetic Foot Ulcers Grand Challenge, DFUC 2022, which was held on September 2022, in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 in Singapore. The 8 full papers presented together with 5 challenge papers and 3 post-challenge papers included in this book were carefully reviewed and selected from 19 submissions.The DFU challenges aim to motivate the health care domain to share datasets, participate in ground truth annotation, and enable data-innovation in computer algorithm development. In the longer term, it will lead to improved patient care.Table of ContentsQuantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification.- DFUC2022 Challenge Papers.- HarDNet-DFUS: Enhancing Backbone and Decoder of HarDNet-MSEGfor Diabetic Foot Ulcer Image Segmentation.- OCRNet For Diabetic Foot Ulcer Segmentation Combined with Edge Loss 30.- On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness.- Capture the Devil in the Details via Partition-then-Ensemble on Higher Resolution Images.- Unconditionally Generated and Pseudo-Labeled Synthetic Images for Diabetic Foot Ulcer Segmentation Dataset Extension.-Post Challenge Paper.- Diabetic Foot Ulcer Segmentation Using Convolutional and Transformer-based Refined Mixup Augmentation for Diabetic Foot Ulcer Segmentation.- Organization IX DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with Vision Transformer Based Detection.- Summary Paper.- Diabetic Foot Ulcer Grand Challenge 2022 Summary.

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Book SynopsisThis two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023.The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.Table of ContentsDatasets and Evaluation.- Action and Behaviour Recognition.- Image and Video Processing, Analysis, and Understanding.- Detection, Recognition, Classification, and Localization in 2D and/or 3D.- Machine Learning and Deep Learning.

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Book SynopsisThis two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023.The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.Table of ContentsSegmentation, Grouping, and Shape.- Vision for Robotics and Autonomous Vehicles.- Biometrics, Faces, Body Gestures and Pose.- 3D Vision from Multiview and other Sensors.- Vision Applications and Systems.

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Book SynopsisThis book constitutes the First Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022.The 15 papers presented in this volume were carefully reviewed and selected form numerous submissions. The aim of the challenge is not only benchmarking various LA scar segmentation algorithms, but also covering the topic of general cardiac image segmentation, quantification, joint optimization, and model generalization, and raising discussions for further technical development and clinical deployment.Table of ContentsLASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification.- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network.- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation.- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners.- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net.- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation .- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing.- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI .- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images.- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium.- Deep U-Net architecture with curriculum learning for left atrial segmentation.- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion.- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation.- Automated segmentation of the left atrium and scar using deep convolutional neural networks.- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge.

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Book SynopsisThis book constitutes the refereed proceedings of the 12th International Conference on Functional Imaging and Modeling of the Heart, held in Lyon, France, in June 2023.The 72 full papers were carefully reviewed and selected from 80 submissions. The focus of the papers is on following topics: increased imaging resolutions, data explosion, sophistication of computational models and advent of AI frameworks, while new imaging modalities have emerged (e.g. combined PET-MRI, Spectral CT).Table of ContentsCardiac multiscale structure.- Cardiac electrophysiology modeling.- Image and shape analysis.- Cardiovascular hemodynamics and CFD.- Cardiac biomechanics.- Clinical applications.

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Book SynopsisThis 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26th International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computer vision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.Table of ContentsTowards a Complete Analysis of People: From Face and Body to Clothes (T-CAP).- 12th International Workshop on Human Behavior Understanding (HBU).- Theories, Applications, and Cross Modality for Self-Supervised Learning Models (SSL).- Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS 2022).- Fairness in Biometric Systems (FAIRBIO).- 2nd International Workshop on Artificial Intelligence for Healthcare Applications (AIHA 2022).- Multimodal Data for Mental Disorder Recognition (MDMR).- MANPU 2022 : The 5th International Workshop on coMics ANalysis, Processing and Understanding.- Image Analysis for Forest Environmental Monitoring (FOREST).- MultiMedia FORensics in the WILD (MMFORWILD 2022).- Image Mining: Theory and Applications (IMTA-VIII).- International Workshop on Pattern Recognition in Healthcare Analytics (PRHA 2022).- International Workshop on Industrial Machine Learning (IML).- 3rd International Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2022).- 2nd Workshop on Explainable and Ethical AI (XAIE 2022).- 12th Workshop on Pattern Recognition in Remote Sensing (PRRS).- Understanding and Mitigating Demographic Bias in Biometric Systems (UMDBB).- Artificial Intelligence for Multimedia Forensics and Disinformation Detection (AI4MFDD).- 3rd Workshop on Applied Multimodal Affect Recognition (AMAR).

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Book SynopsisThis 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26th International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computer vision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.Table of ContentsTowards a Complete Analysis of People: From Face and Body to Clothes (T-CAP).- 12th International Workshop on Human Behavior Understanding (HBU).- Theories, Applications, and Cross Modality for Self-Supervised Learning Models (SSL).- Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS 2022).- Fairness in Biometric Systems (FAIRBIO).- 2nd International Workshop on Artificial Intelligence for Healthcare Applications (AIHA 2022).- Multimodal Data for Mental Disorder Recognition (MDMR).- MANPU 2022 : The 5th International Workshop on coMics ANalysis, Processing and Understanding.- Image Analysis for Forest Environmental Monitoring (FOREST).- MultiMedia FORensics in the WILD (MMFORWILD 2022).- Image Mining: Theory and Applications (IMTA-VIII).- International Workshop on Pattern Recognition in Healthcare Analytics (PRHA 2022).- International Workshop on Industrial Machine Learning (IML).- 3rd International Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2022).- 2nd Workshop on Explainable and Ethical AI (XAIE 2022).- 12th Workshop on Pattern Recognition in Remote Sensing (PRRS).- Understanding and Mitigating Demographic Bias in Biometric Systems (UMDBB).- Artificial Intelligence for Multimedia Forensics and Disinformation Detection (AI4MFDD).- 3rd Workshop on Applied Multimodal Affect Recognition (AMAR).

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Book SynopsisThis 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26th International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computer vision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.Table of ContentsTowards a Complete Analysis of People: From Face and Body to Clothes (T-CAP).- 12th International Workshop on Human Behavior Understanding (HBU).- Theories, Applications, and Cross Modality for Self-Supervised Learning Models (SSL).- Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS 2022).- Fairness in Biometric Systems (FAIRBIO).- 2nd International Workshop on Artificial Intelligence for Healthcare Applications (AIHA 2022).- Multimodal Data for Mental Disorder Recognition (MDMR).- MANPU 2022 : The 5th International Workshop on coMics ANalysis, Processing and Understanding.- Image Analysis for Forest Environmental Monitoring (FOREST).- MultiMedia FORensics in the WILD (MMFORWILD 2022).- Image Mining: Theory and Applications (IMTA-VIII).- International Workshop on Pattern Recognition in Healthcare Analytics (PRHA 2022).- International Workshop on Industrial Machine Learning (IML).- 3rd International Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2022).- 2nd Workshop on Explainable and Ethical AI (XAIE 2022).- 12th Workshop on Pattern Recognition in Remote Sensing (PRRS).- Understanding and Mitigating Demographic Bias in Biometric Systems (UMDBB).- Artificial Intelligence for Multimedia Forensics and Disinformation Detection (AI4MFDD).- 3rd Workshop on Applied Multimodal Affect Recognition (AMAR).

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Book SynopsisThis book constitutes the refereed proceedings of the 24th Annual Conference Towards Autonomous Robotic Systems, TAROS 2023, held in Cambridge, UK, during September 13–15, 2023.The 40 full papers presented in this book were carefully reviewed and selected from 70 submissions.They cover a wide range of different topics such as: agri-food robotics; autonomy; collaborative and service robotics; locomotion and manipulation; machine vision; multi-robot systems; soft robotics; tactile sensing; and teleoperation.Table of Contents​Agri-food Robotics.- Plant phenotyping using DLT method: Towards retrieving the delicate features in a dynamic environment.- Rapid Development and Performance Evaluation of a Potato Planting Robot.- An Automated Precision Spraying Evaluation System.- Smart Parking System Using Heuristic Optimization For Autonomous Transportation Robots In Agriculture.- Closed-Loop Robotic Cooking of Soups with Multi-modal Taste Feedback.- Folding Morphing-wheg Duct-entry Robot for Nuclear Characterisation.- Autonomy.- Occupancy Map Abstraction for Higher Level Mission Planning of Autonomous Robotic Exploration in Hazardous Nuclear Environments.- Spiral Sweeping Protocols for Detection of Smart Evaders.- Action Recognition for Improving Pedestrian Intent Prediction.- Evaluation of SLAM algorithms for Search and Rescue applications.- Developing an Integrated Runtime Verification for Safety and Security of Industrial Robot Inspection System.- Collaborative and Service Robotics.- Sonification of Ionising Radiation Data for Robot Operators.- Automating a Telepresence Robot for Human Detection, Tracking, and Following.- Towards Multimodal Sensing and Interaction for Assistive Autonomous Robots.- Locomotion and Manipulation.- CPG-based locomotion control of a quadruped robot with an active spine.- Low-resolution sensing for sim-to-real complex terrain robots.- Towards wait-and-catch routine of a dynamic swinging object using a prototype robotic arm manipulator.- Simultaneous Base and Arm Trajectories for Multi-Target Mobile Agri-Robot.- Design and kinematic analysis of a 3D-printed 3DOF robotic manipulandum.- Sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-place.- Machine Vision.- Fast 3D Semantic Segmentation Using a Self Attention Network and Random Sampling.- An assessment of self-supervised learning for data efficient potato instance segmentation.- Automated 3D Mapping, localization and pavement inspection with low cost RGB-D cameras and IMUs.- Optimized Custom Dataset for Efficient Detection of Underwater Trash.- A Geometric Algebra Solution to the 3D Registration Problem.- Active Anomaly Detection for Autonomous Robots: a Benchmark.- Multi-robot Systems.- Hardware Validation of Adaptive Fault Diagnosis in Swarm Robots.- Mobile Robots For Collaborative Manipulation Over Uneven Ground Using Decentralised Impedance Control.- Multi-agent Collaborative Target Search Based on Curiosity Intrinsic Motivation.- Simulation of Collective Bernoulli-Ball System for Characterizing Dynamic Self-stability.- Soft Robotics.- Casting vs injection moulding: a comparison study for in-lab low-cost soft robot fabrication.- Estimation of Soft Body Deformation by Using Light.- Reduced-Order Modeling of a Soft Anthropomorphic Finger for Piano Keystrokes.- Tactile Sensing.- Multi-directional Force and Tactile Sensor Sleeves for Micro Catheters and Cannulas.- Towards smooth human-robot handover with a vision-based tactile sensor.- Feeling Good: Validation of Bilateral Tactile Telemanipulation for a Dexterous Robot.- Teleoperation.- Comparative study of hand-tracking and traditional control interfaces for remote palpation.- 5G-based Low-Latency Teleoperation: Two-way Timeout Approach.- Generative Model-based Simulation of Driver Behavior when Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains.- Implementation of a Stereo Vision System for a Mixed Reality Robot Teleoperation Simulator.

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Book SynopsisThis book constitutes the refereed proceedings of the 10th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2023, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023.The 16 papers presented at OMIA 2023 were carefully reviewed and selected from 27 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.

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Book SynopsisThis volume LNCS 14184 and 14185 constitutes the refereed proceedings of the 20th International Conference, CAIP 2023, in Limassol, Cyprus, in September 2023. The 54 full papers presented were carefully reviewed and selected from 67 submissions. They were organized in the following section as follows:Part I-:PAR Contest 2023; Deep Learning; Machine Learning for Image and Pattern Analysis; and Object Recognition and Segmentation.Part II : Biometrics- Human Pose Estimation- Action Recognition; Biomedical Image and Pattern Analysis; and General Vision- AI Applications.Table of Contents​PAR Contest 2023.- Contest 2023: Pedestrian Attributes Recognition with Multi-Task Learning.- Evaluation of a Visual Question Answering Architecture for Pedestrian Attribute Recognition.- Deep Learning.- True Rank Guided Efficient Neural Architecture Search for End to End Low-Complexity Network Discovery.- Explainability-enhanced neural network for thoracic diagnosis improvement.- A comparison of neural network-based super-resolution models on 3D rendered images.- Safe Robot Navigation in Indoor Healthcare Workspaces.- EMBiL: An English-Manipuri Bi-lingual benchmark for scene text detection and language identification.- Low-dimensionality information extraction model for semi-structured documents.- Machine Learning for Image and Pattern Analysis.- Downsampling GAN for Small Object Data Augmentation.- Model regularisation for skin lesion symmetry classification: SymDerm v2.0.- Robust Adversarial Defense: Use of Auto-Inpainting.- Generalized Median Computation for Consensus Learning: A Brief Survey.- Efficient Representation Learning for Inner Speech Domain Generalization.- Using Diffusion Models for Dataset Generation: Prompt Engineering vs. Fine-tuning.- Towards Robust Colour Texture Classification with Limited Training Data.- Explaining StyleGAN Synthesized Swimmer Images in Low-Dimensional Space.- Interpolation Kernel Machines: Reducing Multiclass to Binary.- Knowledge Guided Deep Learning for General-Purpose Computer Vision Applications.- Teaching Computer Programming with Mathematics for Generating Digital Videos and Machine Learning Optimization.- Performance characterization of 2D CNN features for partial video copy detection.- Semi-Automated Patch-based Segmentation of Different Size Groups of Brain Metastases in MRI Images.- Texture analysis contribution to evaluating the common carotid artery’s stroke risk using structural equation modeling.- Object Recognition and Segmentation.- Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition.- PSM-PS: Part-based Signal Modulation for Person Search.- Fast Video Instance Segmentation via Recurrent Encoder-based Transformers.- Fast Context Adaptation for Video Object Segmentation.- ALPR - A Method for Identifying License Plates using Sequential Information.- Non-separable Moments in 3D.

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