Pattern recognition Books

Book SynopsisStereoscopic processes are increasingly used in virtual reality and entertainment. This technology is interesting because it allows for a quick immersion of the user, especially in terms of depth perception and relief clues. However, these processes tend to cause stress on the visual system if used over a prolonged period of time, leading some to question the cause of side effects that these systems generate in their users, such as eye fatigue. This book explores the mechanisms of depth perception with and without stereoscopy and discusses the indices which are involved in the depth perception. The author describes the techniques used to capture and retransmit stereoscopic images. The causes of eyestrain related to these images are then presented along with their consequences in the long and short term. The study of the causes of eyestrain forms the basis for an improvement in these processes in the hopes of developing mechanisms for easier virtual viewing.Table of ContentsAcknowledgments ix Introduction xi Chapter 1. Principles of Depth and Shape Perception 1 1.1. Function of the eye 1 1.2. Depth perception without stereoscopy 2 1.2.1. Monocular cues 2 1.2.2. Proprioceptive cues 7 1.3. Depth perception through stereoscopic vision 9 1.4. Perception of inclinations and curves 10 1.4.1. Perception of inclination and obliqueness 10 1.4.2. Perception of curves 14 1.5. Artificial stereoscopic vision 22 Chapter 2. Technological Elements 25 2.1. Taking a picture 25 2.2. Reproduction 26 2.2.1. Colorimetric differentiation 27 2.2.2. Differentiation by polarization 28 2.2.3. Active glasses 30 2.2.4. Auto-stereoscopic screens 31 2.2.5. Virtual reality headsets 33 2.3. Motion parallax restitution 34 2.3.1. Pseudoscopic movement 34 2.3.2. Correcting pseudoscopic movements 35 2.3.3. Monoscopic motion parallax 40 Chapter 3. Causes of Visual Fatigue in Stereoscopic Vision 41 3.1. Conflict between accommodation and convergence 41 3.2. Too much depth 44 3.3. High spatial frequencies 46 3.3.1. Limits of fusion 49 3.3.2. Comfort and high frequencies. 50 3.4. High temporal frequency 52 3.5. Conflicts with monoscopic cues 52 3.6. Vertical disparities 53 3.7. Improper device settings 55 3.7.1. Quality of image and display 55 3.7.2. Differences between left and right images 56 3.7.3. Speed of correction of pseudoscopic movements 57 Chapter 4. Short- and Long-term Consequences 59 4.1. Short-term effects 59 4.1.1. Decreasing ease of accommodation 59 4.1.2. Decrease in stereoscopic acuity 59 4.1.3. Effects on the punctum proximum 61 4.1.4. More subjective effects 61 4.2. Long-term consequences 62 4.2.1. Long-term effects on children 62 Chapter 5. Measuring Visual Fatigue 63 5.1. Visual acuity 63 5.1.1. Different possible measurements 64 5.1.2. Optotypes 64 5.2. Proximum accommodation function 65 5.3. Ease of accommodation 66 5.4. Stereoscopic acuity 67 5.4.1. Tests of distance vision 67 5.4.2. Tests of near vision 68 5.5. Disassociated heterophorias 71 5.6. Fusional reserves 72 5.7. Subjective tests 74 Chapter 6. Reducing Spatial Frequencies 75 6.1. Principle 75 6.2. Technical solution 75 6.2.1. Wavelets 76 6.2.2. BOX FILTER 92 6.2.3. Using a rolling average and other “blurs” 98 6.2.4. Comparison of algorithms 103 6.2.5. Chosen solution 114 6.3. Experiment 116 6.3.1. The task 116 6.4. Measurements of fatigue taken 118 6.4.1. Objective measurements 118 6.4.2. Procedure 119 6.4.3. The subjects 120 6.5. Result 120 6.5.1. Proximum accommodation function 120 6.5.2. Ease of accommodation 121 6.5.3. Stereoscopic acuity 122 6.5.4. Effectiveness in execution of the task 122 6.5.5. Subjective measurements 123 6.5.6. Conclusions 124 6.5.7. Discussion 124 Chapter 7. Reducing the Distance Between the Virtual Cameras 131 7.1. Principle 131 7.1.1. Usefulness of stereoscopy in depth perception 132 7.1.2. The objects 133 7.1.3. Hypothesis 142 7.2. Experiment 142 7.2.1. Tasks 142 7.2.2. Experimental conditions 143 7.2.3. Subjects 144 7.2.4. Measurements 144 7.3. Results 145 7.3.1. Results for fatigue 145 7.3.2. Perception results 147 7.4. Discussion 152 7.4.1. Influence on visual fatigue 152 7.4.2. Influence on visual perception 153 Conclusion 155 Bibliography 157 Index 167

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Book SynopsisBiometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.Table of ContentsPart I: Introduction Introduction Statistical Background Part II: Primary Matching and Classification Measures False Non-Match Rate False Match Rate Receiver Operating Characteristic Curve and Equal Error Rate Part III: Biometric Specific Measures Failure to Enrol Failure to Acquire Part IV: Additional Topics and Appendices Additional Topics and Discussion Tables

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Book SynopsisMedical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.Table of Contents1. Principles of Image Generation.- 1.1 Introduction.- 1.2 Ultrasound Image Generation.- 1.2.1 The Principle of Pulse-Echo Ultrasound Imaging.- 1.2.2 B-Scan Quality and the Ultimate Limits.- 1.2.3 Propagation-Related Artifacts and Resolution Limits.- 1.2.3 Attenuation-Related Artifacts.- 1.3 X-Ray Cardiac Image Generation.- 1.3.1 LV Data Acquisition System Using X-Rays.- 1.3.2 Drawbacks of Cardiac Catheterization.- 1.4 Magnetic Resonance Image Generation.- 1.4.1 Physical Principles of Nuclear Magnetic Resonance.- 1.4.2 Basics of Magnetic Resonance Imaging.- 1.4.3 Gradient-Echo (GRE).- 1.4.4 The Latest Techniques for MR Image Generation.- 1.4.5 3-D Turbo FLASH (MP-RAGE) Technique.- 1.4.6 Non-Rectilinear k-Space Trajectory: Spiral.- 1.4.7 Fat Suppression.- 1.4.8 High Speed MRI: Perfusion-Weighted.- 1.4.9 Time of Flight (TOF) MR Angiography.- 1.4.10 Fast Spectroscopic Imaging.- 1.4.11 Recent MR Imaging Techniques.- 1.5 Computer Tomography Image Generation.- 1.5.1 Fourier Reconstruction Method.- 1.6 Positron-Emission Tomography Image Generation.- 1.6.1 Underlying Principles of.- 1.6.2 Usage of PET in Diagnosis.- 1.6.3 Fourier Slice Theorem.- 1.6.4 The Reconstruction Algorithm in PET.- 1.6.5 Image Reconstruction Using Filtered Back-Projection.- 1.7 Comparison of Imaging Modalities: A Summary.- 1.7.1 Acknowledgements.- 2. Segmentation in Echocardiographic Images.- 2.1 Introduction.- 2.2 Heart Physiology and Anatomy.- 2.2.1 Cardiac Function.- 2.2.2 Standard LV Views in 2-DEs.- 2.2.3 LV Function Assessment Using 2-DEs.- 2.3 Review of LV Boundary Extraction Techniques Applied to Echocardiographic Data.- 2.3.1 Acoustic Quantification Techniques.- 2.3.2 Image-Based Techniques.- 2.3.3 2-DE Image Processing Techniques.- 2.4Automatic Fuzzy Reasoning-Based Left Ventricular Center Point Extraction.- 2.4.1 LVCP Extraction System Overview.- 2.4.2 Stage 1: Pre-Processing.- 2.4.3 Stage 2: LVCP Features Fuzzification.- 2.4.4 Template Matching.- 2.4.5 Experimental Results.- 2.4.6 Conclusion.- 2.5 A New Edge Detection in the Wavelet Transform Domain.- 2.5.1 Multiscale Edge Detection and the Wavelet Transform.- 2.5.2 Edge Detection Based on the Global Maximum of Wavelet Transform (GMWT).- 2.5.3 GMWT Performance Analysis and Comparison.- 2.6 LV Segmentation System.- 2.6.1 Overall Reference.- 2.6.2 3D Non-Uniform Radial Intensity Sampling.- 2.6.3 LV Boundary Edge Detection on 3D Radial Intensity Matrix.- 2.6.4 Post-Processing of the Edges and Closed LVE Approximation.- 2.6.5 Automatic LV Volume Assessment.- 2.7 Conclusions.- 2.8 Acknowledgments.- 3. Cardiac Boundary Segmentation.- 3.1 Introduction.- 3.2 Cardiac Anatomy and Data Acquisitions for MR, CT, Ul-trasound and X-Rays.- 3.2.1 Cardiac Anatomy.- 3.2.2 Cardiac MR, CT, Ultrasound and X-Ray Acquisitions.- 3.3 Low- and Medium-Level LV Segmentation Techniques.- 3.3.1 Smoothing Image Data.- 3.3.2 Manual and Semi-Automatic LV Thresholding.- 3.3.3 LV Dynamic Thresholding.- 3.3.4 Edge-Based Techniques.- 3.3.5 Mathematical Morphology-Based Techniques.- 3.3.6 Drawbacks of Low-Level LV Segmentation Techniques.- 3.4 Model-Based Pattern Recognition Methods for LV Modeling.- 3.4.1 LV Active Contour Models in the Spatial and Temporal Domains.- 3.4.2 Model-Based Pattern Recognition Learning Methods.- 3.4.3 Polyline Distance Measure and Performance Terms.- 3.4.4 Data Analysis Using IdCM, InCM and the Greedy Method.- 3.5 Left Ventricle Apex Modeling: A Model-Based Approach.- 3.5.1 Longitudinal Axis and Apex Modeling.- 3.5.2 Ruled Surface Model.- 3.5.3 Ruled Surface sr and its Coefficients.- 3.5.4 Estimation of Robust Coefficients and Coordinates of the Ruled Surface.- 3.5.5 Experiment Design.- 3.5.6 Analytical Error Measure, AQin for Inlier Data.- 3.5.7 Experiments, Results and Discussions.- 3.5.8 Conclusions on LV Apex Modeling.- 3.6 Integration of Low-Level Features in LV Model-Based Cardiac Imaging: Fusion of Two Computer Vision Systems.- 3.7 General Purpose LV Validation Technique.- 3.8 LV Convex Hulling: Quadratic Training-Based Point Modeling.- 3.8.1 Quadratic Vs. Linear Optimization for Convex Hulling.- 3.9 LV Eigen Shape Modeling.- 3.9.1 Procrustes Superposition.- 3.9.2 Dimensionality Reduction Using Constraints for Joint.- 3.10 LV Neural Network Models.- 3.11 Comparative Study and Summary of the Characteristics of Model-Based Techniques.- 3.11.1 Characteristics of Model-Based LV Imaging.- 3.12 LV Quantification: Wall Motion and Tracking.- 3.12.1 LV Wall Motion Measurements.- 3.12.2 LV Volume Measurements.- 3.12.3 LV Wall Motion Tracking.- 3.13 Conclusions.- 3.13.1 Cardiac Hardware.- 3.13.2 Cardiac Software.- 3.13.3 Summary.- 3.13.4 Acknowledgments.- 4. Brain Segmentation Techniques.- 4.1 Introduction.- 4.1.1 Human Brain Anatomy and the MRI System.- 4.1.2 Applications of Brain Segmentation.- 4.2 Brain Scanning and its Clinical Significance.- 4.3 Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.3.1 Atlas-Based and Threshold-Based Techniques.- 4.3.2 Cortical Segmentation Using Probability-Based Techniques.- 4.3.3 Clustering-Based Cortical Segmentation Techniques.- 4.3.4 Mathematical Morphology-Based Cortical Segmentation Techniques.- 4.3.5 Prior Knowledge-Based Techniques.- 4.3.6 Texture-Based Techniques.- 4.3.7 Neural Network-Based Techniques.- 4.3.8 Regional Hyperstack: Fusion of Edge-Diffusion with Region-Linking.- 4.3.9 Fusion of Probability-Based with Edge Detectors, Connectivity and Region-Growing.- 4.3.10 Summary of Region-Based Techniques: Pros and Cons.- 4.4 Boundary/Surface-Based 2-D and 3-D Cortical Segmentation Techniques: Edge, Reconstruction, Parametric and Geometric Snakes/Surfaces.- 4.4.1 Edge-Based Cortical-Boundary Estimation Techniques.- 4.4.2 3-D Cortical Reconstruction From 2-D Serial Cross-Sections (Bourke/Victoria).- 4.4.3 2-D and 3-D Parametric Deformable Models for Cortical Boundary Estimation: Snakes, Fitting, Constrained, Ribbon, T-Surface, Connectedness.- 4.4.4 2-D and 3-D Geometric Deformable Models.- 4.4.5 A Note on Isosurface Extraction (Lorensen/GE).- 4.4.6 Summary of Boundary/Surface-Based Techniques: Pros and Cons.- 4.5 Fusion of Boundary/Surface with Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.5.1 2-D/3-D Regional Parametric Boundary: Fusion of Boundary with Classification (Kapur/MIT).- 4.5.2 Regional Parametric Surfaces: Fusion of Surface with Clustering (Xu/JHU).- 4.5.3 2-D Regional Geometric Boundary: Fusion of Boundary with Clustering for Cortical Boundary Estimation (Suri/Marconi).- 4.5.34 3-D Regional Geometric Surfaces: Fusion of Geometric Surface with Probability-Based Voxel Classification (Zeng/Yale).- 4.5.5 2-D/3-D Regional Geometric Surface: Fusion of Geometric Boundary/Surface with Global Shape Information (Leventon/MIT).- 4.5.6 2-D/3-D Regional Geometric Surface: Fusion of Boundary/Surface with Bayesian-Based Pixel Classification (Barillot/IRISA).- 4.5.7 Similarities/Differences Between Different Cortical Segmentation Techniques.- 4.6 3-D Visualization Using Volume Rendering and Texture Mapping.- 4.6.1 Volume Rendering Algorithm for Brain Segmentation.- 4.6.2 Texture Mapping Algorithm for Segmented Brain Visualization.- 4.7 A Note on fMRI: Algorithmic Approach for Establishing the Relationship Between Cognitive Functions and Brain Cortical Anatomy.- 4.7.1 Superiority of fMRI over PET/SPECT Imaging.- 4.7.2 Applications of fMRI.- 4.7.3 Algorithm for Superimposition of Functional and Anatomical Cortex.- 4.7.4 A Short Note on fMRI Time Course Data Analysis.- 4.7.5 Measure of Cortex Geometry.- 4.8 Discussions: Advantages, Validation and New Challenges i 2-D.- 4.8.1 Advantages of Regional Geometric Boundary/Surfaces.- 4.8.2 Validation of 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.3 Challenges in 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.4 Challenges in fMRI.- 4.9 Conclusions and the Future.- 4.9.1 Acknowledgements.- 5. Segmentation for Multiple Sclerosis Lesion.- 5.1 Introduction.- 5.2 Segmentation Techniques.- 5.2.1 Multi-Spectral Techniques.- 5.2.2 Feature Space Classification.- 5.2.3 Supervised Segmentation.- 5.2.4 Unsupervised Segmentation.- 5.2.5 Automatic Segmentation.- 5.3 AFFIRMATIVE Images.- 5.4 Image Pre-Processing.- 5.4.1 RF Inhomogeneity Correction.- 5.4.2 Image Stripping.- 5.4.3 Three Dimensional MR Image Registration.- 5.4.4 Segmentation.- 5.4.5 Flow Correction.- 5.4.6 Evaluation and Validation.- 5.5 Quantification of Enhancing Multiple Sclerosis Lesions.- 5.6 Quadruple Contrast Imaging.- 5.7 Discussion.- 5.7.1 Acknowledgements.- 6. Finite Mixture Models.- 6.1 Introduction.- 6.2 Pixel Labeling Using the Classical Mixture Model.- 6.3 Pixel Labeling Using the Spatially Variant Mixture Model.- 6.4 Comparison of CMM and SVMM for Pixel Labeling.- 6.5 Bayesian Pixel Labeling Using the SVMM.- 6.6 Segmentation Results.- 6.6.1 Computer Simulations.- 6.6.2 Application to Magnetic Resonance Images.- 6.7 Practical Aspects.- 6.8 Summary.- 6.9 Acknowledgements.- 7. MR Spectroscopy.- 7.1 Introduction.- 7.2 A Short History of Neurospectroscopic Imaging and Segmentation in Alzheimer’s Disease and Multiple Sclerosis.- 7.2.1 Alzheimer’s Disease.- 7.2.2 Multiple Sclerosis.- 7.3 Data Acquisition and Image Segmentation.- 7.3.1 Image Pre-Processing for Segmentation.- 7.3.2 Image Post-Processing for Segmentation.- 7.4 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation in Multiple Sclerosis.- 7.4.1 Automatic MRSI Segmentation and Image Processing Algorithm.- 7.4.2 Relative Metabolite Concentrations and Contribution of Gray Matter and White Matter in the Normal Human Brain.- 7.4.3 MRSI and Gadolinium-Enhanced (Gd).- 7.4.4 Lesion Load and Metabolite Concentrations by Segmentation and MRSI.- 7.4.5 MR Spectroscopic Imaging and Localization for Segmentation.- 7.4.6 Lesion Segmentation and Quantification.- 7.4.7 Magnetic Resonance Spectroscopic Imaging and Segmentation Data Processing.- 7.4.8 Statistical Analysis.- 7.5 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation of Alzheimer’s Disease.- 7.5.1 MRSI Data Acquisition Methods.- 7.5.2 H-1 MR Spectra Analysis.- 7.6 Applications of Magnetic Resonance Spectroscopic Imaging and Segmentation.- 7.6.1 Multiple Sclerosis Lesion Metabolite Characteristics and Serial Changes.- 7.6.2 zheimer’s Disease Plaque Metabolite Characteristics.- 7.7 Discussion.- 7.8 Conclusion.- 7.8.1 Acknowledgements.- 8. Fast WM/GM Boundary Estimation.- 8.1 Introduction.- 8.2 Derivation of the Regional Geometric Active Contour Model from the Classical Parametric Deformable Model.- 8.3 Numerical Implementation of the Three Speed Functions in the Level Set Framework for Geometric Snake Propagation.- 8.3.1 Regional Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.2 Gradient Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.3 Curvature Speed Term Expressed in Terms of the Level Set Function (ø).- 8.4 Fast Brain Segmentation System Based on Regional Level Sets.- 8.4.1 Overall System and Its Components.- 8.4.2 Fuzzy Membership Computation/Pixel Classification.- 8.4.3 Eikonal Equation and its Mathematical Solution.- 8.4.4 Fast Marching Method for Solving the Eikonal Equation.- 8.4.5 A Note on the Heap Sorting Algorithm.- 8.4.6 Segmentation Engine: Running the Level Set Method in the Narrow Band.- 8.5 MR Segmentation Results on Synthetic and Real Data.- 8.5.1 Input Data Set and Input Level Set Parameters.- 8.5.2 Results: Synthetic and Real.- 8.5.3 Numerical Stability, Signed Distance Transformation Computation, Sensitivity of Parameters and Speed Issues.- 8.6 Advantages of the Regional Level Set Technique.- 8.7 Discussions: Comparison with Previous Techniques.- 8.8 Conclusions and Further Directions.- 8.8.1 Acknowledgements.- 9. Digital Mammography Segmentation.- 9.1 Introduction.- 9.2 Image Segmentation in Mammography.- 9.3 Anatomy of the Breast.- 9.4 Image Acquisition and Formats.- 9.4.1 Digitization of X-Ray Mammograms.- 9.4.2 Image Formats.- 9.4.3 Image Quantization and Tree-Pyramids.- 9.5 Mammogram Enhancement Methods.- 9.6 Quantifying Mammogram Enhancement.- 9.7 Segmentation of Breast Profile.- 9.8 Segmentation of Microcalcifications.- 9.9 Segmentation of Masses.- 9.9.1 Global Methods.- 9.9.2 Edge-Based Methods.- 9.9.3 Region-Based Segmentation.- 9.9.4 ROI Detection Techniques Using a Single Breast.- 9.9.5 ROI Detection Techniques Using Breast Symmetry.- 9.9.6 Detection of Spicules.- 9.9.7 Breast Alignment for Segmentation.- 9.10 Measures of Segmentation and Abnormality Detection.- 9.11 Feature Extraction From Segmented Regions.- 9.11.1 Morphological Features.- 9.11.2 Texture Features.- 9.11.3 Other Features.- 9.12 Public Domain Databases in Mammography.- 9.12.1 The Digital Database for Screening Mammography (DDSM).- 9.12.2 LLNL/UCSF Database.- 9.12.3 Washington University Digital Mammography Database.- 9.12.4 The Mammographic Image Analysis Society (MIAS) Database.- 9.13 Classification and Measures of Performance.- 9.13.1 Classification Techniques.- 9.13.2 The Receiver Operating Characteristic Curve.- 9.14 Conclusions.- 9.15 Acknowledgements.- 10. Cell Image Segmentation for Diagnostic Pathology.- 10.1 Introduction.- 10.2 Segmentation.- 10.2.1 Feature Space Analysis.- 10.2.2 Mean Shift Procedure.- 10.2.3 Cell Segmentation.- 10.2.4 Segmentation Examples.- 10.3 Decision Support System for Pathology.- 10.3.1 Problem Domain.- 10.3.2 System Overview.- 10.3.3 Current Database.- 10.3.4 Analysis of Visual Attributes.- 10.3.5 Overall Dissimilarity Metric.- 10.3.6 Performance Evaluation and Comparisons.- 10.4 Conclusion.- 11. The Future in Segmentation.- 11.1 Future Research in Medical Image Segmentation.- 11.1.1 The Future of MR Image Generation and Physical Principles.- 11.1.2 The Future of Cardiac Imaging.- 11.2.3 The Future of Neurological Segmentation.- 11.2.4 The Future in Digital Mammography.- 11.2.5 The Future of Pathology Image Segmentation.

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Book SynopsisSmart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure.This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems.This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.Table of ContentsPart I: Sensing and Activity Monitoring Multi-Resident Activity Monitoring in Smart Homes Through Non-Wearable Non-Intrusive SensorsSon N. Tran and Qing Zhang and Vanessa Smallbon and Mohan Karunanithi Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric VideosGeorgios Kapidis, Ronald W. Poppe, Elsbeth A. van Dam, Remco C. Veltkamp, and Lucas P. J. J. Noldus A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-LivingGiovanni Schiboni, Fabio Wasner, and Oliver AmftSaving Energy on EMG-Monitoring Eyeglasses for Free-Living Eating Event Spotting Using Adaptive Duty-CyclingGiovanni Schiboni and Oliver Amft Indoor Localisation with WiFi Fingerprinting Based on a Convolutional Neural NetworkZumin Wang Unobtrusive Sensing to Assist with Post-Stroke RehabilitationChris Nugent Part II: Activity Recognition and Behaviour Analysis Energy-Based Decision Engine for Household Human Activity RecognitionAnastasios Vafeiadis, Thanasis Vafeiadis, Stelios Zikos, Stelios Krinidis, Konstantinos Votis, Dimitrios Giakoumis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Liming Chen, and Raouf Hamzaoui Distributed Context Recognition, a Systematic ReviewUmar Ahmad and Luis Lopera Exercise Type Recognition Using Transfer LearningHossein Malekmohamadi Meta-Intelligence for Behaviour RecognitionXiaodong Liu and Qi Liu Part III: User Needs and Personalisation A Conceptual Framework for Adaptive User Interfaces for Older AdultsEduardo Machado, Deepika Singhy, Federico Cruciani, Liming Chen, Sten Hankey, Fernando Salvago, Johannes Kropf, and Andreas HolzingerStudying the Technological Barriers and Needs of People with Dementia: A Quantitative StudyNikolaos Liappas, Rebeca Isabel García-Betances, José Gabriel Teriús-Padrón, and María Fernanda Cabrera-Umpiérrez Adaptive Service Robot Behaviours Based on User Mood: Towards Better Personalized Support of MCI Patients at HomeDimitrios Giakoumis, Georgia Peleka, Manolis Vasileiadis, Ioannis Kostavelis, and Dimitrios Tzovaras Part IV: Ambient Assisted Living Solutions Towards Cognitive Assisted LivingClaudia Steinberger and Judith Michael Towards Self-Management of Chronic Diseases in Smart HomesJosé G. Teriús-Padrón, Georgios Kapidis, Sarah Fallmann, Erinc Merdivan, Sten Hanke, Rebeca I. García-Betances, and María Fernanda Cabrera-UmpiérrezA Deep Learning Approach for Privacy Preservation in Assisted LivingIsmini Psychoula, Erinc Merdivany, Deepika Singhy, Liming Chen, Feng Chen, Sten Hankey, Johannes Kropfy, Andreas Holzingerx, and Matthieu GeistTowards Socially Assistive Robots for the Elderly: An End-to-End Object Search FrameworkMohammad Reza Loghmani, Timothy Patten and Markus VinczeModelling Activities of Daily Living with Petri NetsMatias Garcia-Constantino, Alexandros Konios and Chris Nugent Calculus of Context-Aware Ambients for Assisted Living System ModellingFrancois Siewe

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Book SynopsisThis book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.Table of ContentsPart I, Foundations.- AI Sculpture.- Make Me Learn.- Images and Sequences.- Why AI Works.- Learning to Sculpt.- Unleashing the Power of Generation.- The Road Most Rewarded.- The Classical World.- Part II, Applications.- To See is to Believe.- Read, Read, Read.- Lend Me Your Ear.- Create Your Shire and Rivendell.- Math to Code to Petaflops.- AI and Business.- Part III, Road Ahead.- Keep Marching on.- Benevolent AI for All.- Am I Looking at Myself?.- App. A, Solutions.- Further Reading.- Acronyms.- Glossary.- References.- Index.

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Book SynopsisThis comprehensive textbook offers a scientifically sound and at the same time practical introduction to Virtual and Augmented Reality (VR/AR). Readers will gain the theoretical foundation needed to design, implement or enhance VR/AR systems, evaluate and improve user interfaces and applications using VR/AR methods, assess and enrich user experiences, and develop a deeper understanding of how to apply VR/AR techniques. Whether utilizing the book for a principal course of study or reference reading, students of computer science, education, media, natural sciences, engineering and other subject areas can benefit from its in-depth content and vivid explanation. The modular structure allows selective sequencing of topics to the requirements of each teaching unit and provides an easy-to-use format from which to choose specific themes for individual self-study. Instructors are provided with extensive materials for creating courses as well as a foundational text upon which to build their advanced topics. The book enables users from both research and industry to deal with the subject in detail so they can properly assess the extent and benefits of VR/AR deployment and determine required resources. Technology enthusiasts and professionals can learn about the current status quo in the field of VR/AR and interested newcomers can gain insight into this fascinating world. Grounded on a solid scientific foundation, this textbook, addresses topics such as perceptual aspects of VR/AR, input and output devices including tracking, interactions in virtual worlds, real-time aspects of VR/AR systems and the authoring of VR/AR applications in addition to providing a broad collection of case studies.Table of Contents1 R. Doerner et al., Introduction to Virtual and Augmented Reality.- 2 R. Doerner and F. Steinicke, Perceptual Aspects of VR.- 3 B. Jung and A. Vitzhum, Virtual Worlds.- 4 P. Grimm at al., VR/AR Input Devices and Tracking.- 5 W. Broll et al., VR/AR Output Devices.- 6 R. Doerner et al., Interaction in Virtual Worlds.- 7 M. Buhr et al., Real-time Aspects of VR Systems.- 8 W. Broll, Augmented Reality.- 9 R. Doerner et al., VR/AR Case Studies.- 10 W. Broll et al., Authoring of VR/AR Applications.- 11 R. Doerner, Mathematical Foundations of VR/AR.

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Book SynopsisA major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.Table of ContentsIntroduction.- Fingerprint sensing.- Fingerprint analysis and representation.- Fingerprint matching.- Fingerprint classification and indexing.- Latent fingerprint recognition.- Fingerprint synthesis.- Fingerprint individuality.- Securing fingerprint systems.

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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 is open access. This book undertakes a multifaceted and integrated examination of biometric identification, including the current state of the technology, how it is being used, the key ethical issues, and the implications for law and regulation. The five chapters examine the main forms of contemporary biometrics–fingerprint recognition, facial recognition and DNA identification– as well the integration of biometric data with other forms of personal data, analyses key ethical concepts in play, including privacy, individual autonomy, collective responsibility, and joint ownership rights, and proposes a raft of principles to guide the regulation of biometrics in liberal democracies.Biometric identification technology is developing rapidly and being implemented more widely, along with other forms of information technology. As products, services and communication moves online, digital identity and security is becoming more important. Biometric identification facilitates this transition. Citizens now use biometrics to access a smartphone or obtain a passport; law enforcement agencies use biometrics in association with CCTV to identify a terrorist in a crowd, or identify a suspect via their fingerprints or DNA; and companies use biometrics to identify their customers and employees. In some cases the use of biometrics is governed by law, in others the technology has developed and been implemented so quickly that, perhaps because it has been viewed as a valuable security enhancement, laws regulating its use have often not been updated to reflect new applications. However, the technology associated with biometrics raises significant ethical problems, including in relation to individual privacy, ownership of biometric data, dual use and, more generally, as is illustrated by the increasing use of biometrics in authoritarian states such as China, the potential for unregulated biometrics to undermine fundamental principles of liberal democracy. Resolving these ethical problems is a vital step towards more effective regulation.Table of ContentsAcknowledgment1. The Rise of Biometric Identification, Fingerprints and Applied Ethics2. Facial Recognition and Privacy Rights3. DNA Identification, Joint Rights and Collective Responsibility4. Biometric and Non-Biometric Integration: Dual Use Dilemmas5. The Future of Biometrics and Liberal DemocracyIndex

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Book SynopsisThis book constitutes the refereed proceedings of the 10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022, held in Aveiro, Portugal, in May 2022. The 54 papers accepted for these proceedings were carefully reviewed and selected from 72 submissions. They deal with document analysis; medical image processing; biometrics; pattern recognition and machine learning; computer vision; and other applications. Table of ContentsDOCUMENT ANALYSIS.- Test Sample Selection for Handwriting Recognition through Language Modeling.- Classification of Untranscribed Handwritten Notarial Documents by Textual Contents.- Incremental Vocabularies in Machine Translation through Aligned Embedding Projections.- An Interactive Machine Translation Framework for Modernizing the Language of Historical Documents.- From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation.- MEDICAL IMAGE PROCESSING.- Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata.- Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection.- Deep Learning for Diagnosis of Alzheimer’s Disease with FDG-PET Neuroimaging.- Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes.- Increased Robustness in Chest X-ray Classification through Clinical Report-driven regularization.- MEDICAL APPLICATIONS.- Deep Detection Models for Measuring Epidermal Bladder Cells.- On the performance of deep learning models for respiratory sound classification trained on unbalanced data.- Automated Adequacy Assessment of Cervical Cytology Samples using Deep Learning.- Exploring Alterations in Electrocardiogram during the Postoperative Pain.- Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification.- Detection of epilepsy in EEGs using Deep Sequence models - A Comparative Study.- BIOMETRICS.- Facial Emotion Recognition for Sentiment Analysis of Social Media Data.- Heartbeat selection based on outlier removal.- Characterization of emotions through facial Electromyogram signals.- Feature selection for emotional well-being monitorization.- Temporal Convolutional Networks for Robust Face Liveness Detection.- PATTERN RECOGNITION & MACHINE LEARNING.- MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks.- Transparent management of adjacencies in the cubic grid.- Abbreviating Labelling Cost for Sentinel-2 Image Scene Classification through Active Learning.- Feature-based classification of archaeal sequences using compression-based methods.- A first approach to Image Transformation Sequence Retrieval.- Discriminative Learning of Two-Dimensional Probabilistic Context-Free Grammars for Mathematical Expression Recognition and Retrieval.- COMPUTER VISION.- Golf Swing Sequencing using Computer Vision.- Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition.- An Innovative Vision System for Floor-Cleaning Robots based on YOLOv5.- LIDAR Signature based Node Detection and Classification in graph topological maps for indoor navigation.- Event Vision in Egocentric Human Action Recognition.- An edge-based computer vision approach for determination of sulfonamides in water.- IMAGE PROCESSING.- Visual Semantic Context Encoding for Aerial Data Introspection and Domain Prediction.- An End-to-End Approach for Seam Carving Detection using Deep Neural Networks.- Proposal of a comparative framework for face super-resolution algorithms in forensics.- On the use of Transformers for end-to-end Optical Music Recognition.- Retrieval of Music-Notation Primitives via Image-to-Sequence.- Digital image conspicuous features classification using TLCNN model with SVM classifier.- Contribution of low, mid and high-level image features in predicting human similarity judgements.- On the Topological Disparity Characterization of Square-pixel Binary Image Data by a Labeled Bipartite Graph.- Learning Sparse Masks for Diffusion-based Image Inpainting.- Extracting Descriptive Words from Untranscribed Handwritten Images.- OTHER APPLICATIONS.- GMM-aided DNN Bearing Fault Diagnosis using Sparse Autoencoder Feature Extraction.- Identification of External Defects on FruitsUsing Deep Learning.- Improving Action Quality Assessment using Weighted Aggregation.- Improving Licence Plate Detection using Generative Adversarial Networks.- Film shot type classification based on camera movement styles.- The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition.- A case of study on traffic cone detection for autonomous racing on a Jetson platform.- Energy savings in residential buildings based on adaptive thermal comfort models.- Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition.- Using bus tracking data to detect potential hazard driving zones.- Dynamic PCA based statistical monitoring of air pollutant concentrations in wildfire scenarios.

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Book SynopsisThis book constitutes the proceedings of the 14th Mexican Conference on Pattern Recognition, MCPR 2022, which was held in planned to be held Ciudad Juárez, Mexico, in June 2022. The 33 papers presented in this volume were carefully reviewed and selected from 66 submissions. They are organized in the following topical sections: pattern recognition techniques; neural networks and deep learning; image and signal processing and analysis; natural language processing and recognition; robotics and remote sensing applications of pattern recognition; medical applications of pattern recognition.Table of ContentsPattern Recognition Techniques.- Hot Spots & Hot Regions Detection using Classification Algorithms in BMPs Complexes at the Protein-protein Interface with the Ground-state Energy Feature.- Clustering of Twitter Networks based on Users’ Structural Profile.- Changing Model from NGSIM Dataset.- A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants.- A Preliminary Study of SMOTE on Imbalanced Big Datasets when Dealing with Sparse and Dense High Dimensionality.- A Novel Survival Analysis-based Approach for Predicting Behavioral Probability of Mining Mixed Data Bases using Machine Learning Algorithms.- Networks and Deep Learning A CNN-based Driver’s Drowsiness and Distraction Detection System.- 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain.- Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification.- Deep Variational Method with Attention for High-Definition Face Generation.- Indoor Air Pollution Forecasting using Deep Neural Networks.- Extreme Machine Learning Architectures based on Correlation.- Image & Signal Processing and Analysis Evaluating New Set of Acoustical Features for Cry Signal Classification.- Motor Imagery Classification Using Riemannian Geometry in Multiple Frequency Bands with a Weighted Nearest Neighbors Approach.- Virtualizing 3D Real Environments Using 2D Pictures Based on Photogrammetry.- Factorized U-net for Retinal Vessel Segmentation.- Multi-view Learning for EEG Signal Classification of Imagined Speech.- Escalante Emotion Recognition using Time-frequency Distribution and GLCM Features from EEG Signals.- Natural Language Processing and Recognition Leveraging Multiple Characterizations of Social Media Users for Depression Detection Using Data Fusion.- A Wide & Deep Learning Approach for Covid-19 Tweet Classification.- Does this Tweet Report an Adverse Drug Reaction? An Enhanced BERT-based Method to Identify Drugs Side Effects in Twitter.- We Will Know Them by Their Style: Fake News Detection based on Masked n-grams.- Multi-Document Text Summarization based on Genetic Algorithm and the Relevance of Sentence Features.- ´ Robotics & Remote Sensing Applications of Pattern Recognition On Labelling Pointclouds with the Nearest Facet of Triangulated Building Models.- Dust Deposition Classification on the Receiver Tube of the Parabolic Trough Collector: A Deep Learning-based Approach.- Detection of Pain Caused By A Thermal Stimulus Using EEG and Machine Learning.- Data Mining.- Natural Language Processing and Recognition.- Document Processing and Recognition.- Fuzzy and Hybrid Techniques in Pattern Recognition.- Image Coding, Processing and Analysis.- Industrial and Medical Applications of Pattern Recognition.- Bioinformatics.- Logical Combinatorial Pattern Recognition.- Mathematical Morphology.- Artificial Intelligence Techniques and Recognition.- Pattern Recognition Principles.- Robotics & Remote Sensing Applications of Pattern Recognition.- Shape and Texture Analysis.- Signal Processing and Analysis.

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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 SynopsisThis book constitutes the refereed proceedings of the 10th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks. Table of ContentsTransformer-Encoder generated context-aware embeddings for spell correction.- Graph Augmentation for Neural Networks Using Matching-Graphs.- Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification.- Assessment of Pharmaceutical Patent Novelty using Siamese Neural Network.- A Review of Capsule Networks in Medical Image Analysis.- Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions.- Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing.- A Study on the Autonomous Detection of Impact Craters.- Minimizing Cross Intersections in Graph Drawing via Linear Splines.- Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech.- Do Minimal Complexity Least Squares Support Vector Machines Work?.- A Novel Representation of Graphical Patterns for Graph Convolution Networks.- Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi Utilization of Vision Transformer for Classification and Ranking of Video Distortions.- White Blood Cell Classification of Porcine Blood Smear Images.- Medical Deepfake Detection using 3-Dimensional Neural Learning.

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Book SynopsisThis book constitutes the refereed proceedings of the 5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022, held in Kingsville, TX, USA, in collaboration with the Applied AI Research Laboratory of the University of South Dakota, during December 01-02, 2022.The 31 full papers included in this book were carefully reviewed and selected from 69 submissions. They were organized in topical sections as follows: healthcare: medical imaging and informatics; computer vision and pattern recognition; internet of things and security; and signal processing and machine learning.Table of Contents​Healthcare: medical imaging and informatics.- Data Characterization for Reliable AI in Medicine.- Alzheimer’s Disease Detection using Ensemble Learning and Artificial Neural Networks.- Semi-supervised Multi-domain Learning for Medical Image Classification.- Significant CC400 functional brain parcellations based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder detection.- 2D respiratory sound analysis to detect lung abnormalities.- Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality due to Infectious Disease.- Chest X-ray Image Super-resolution via Deep Contrast Consistent Feature Network.- A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images.- Federated Learning for Lung Sound Analysis.- Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification using Thermal Image.- Image Processing and Pattern Recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood.- An Extreme Learning Machine-basedAutoEncoder (ELM-AE)for denoising knee X-ray images and grading knee osteoarthritis severity.- Computer Vision and Pattern Recognition.- Motor Imagery Classification CombiningRiemannian Geometry and Artificial Neural Networks.- Autism Spectrum Disorder Detection using Transfer Learning with VGG 19, Inception V3 and DenseNet 201.- Shrimp Shape Analysis by a Chord LengthFunction Based Methodology.- Supervised Neural Networks for Fruit Identification.- Targeted Clean-Label Poisoning Attacks On Federated Learning.- Building Marathi SentiWordNet.- A computational study on calibrated VGG19 formultimodal learning and representation insurveillance.- Automated Deep Learning based approach for Albinism Detection.- A Deep learning-based regression scheme for angle estimation in image dataset.- The classification of Native and Invasive Speciesin North America: A Transfer Learning and Random Forest Pipeline.- Internet of Things and Security.- Towards a Digital Twin Integrated DLT and IoT-based Automated Healthcare Ecosystem.- Enabling Edge Devices using Federated Learning and Big Data for Proactive Decisions.- IoT and Blockchain oriented gender determination of Bangladeshi populations.- Federated Learning based secured computational offloading in cyber-physical IoST systems.- A Hybrid Campus Security System Combined ofFace, Number-plate, and Voice Recognition.- Signal Processing and Machine.- Single-trial detection of event-related potentials with artificial examples based on coloring transformation.- Identifying the relationship between hypothesis and premise.- Data Poisoning Attack by Label Flipping onSplitFed Learning.- A Deep Learning-powered voice-enabled mathtutor for kids.

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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 three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Solving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.- Multi-Agent Hyper-Attention Policy Optimization.- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.- Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.- A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.

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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 refereed proceedings of the 15th Mexican Conference on Pattern Recognition, MCPR 2023, held in Tepic, Mexico, during June 21–24, 2023.The 30 full papers presented in this book were carefully reviewed and selected from 61 submissions. The papers are divided into the following topical sections: pattern recognition and machine learning techniques; deep learning and neural networks; medical applications of pattern recognition; language processing and recognition; and industrial applications of pattern recognition.Table of ContentsPattern Recognition and Machine Learning Techniques: Feature Analysis and Selection for Water Stream Modeling.- A Cloud-based (AWS) Machine Learning Solution to Predict Account Receivables in a Financial Institution.- A New Approach for Road Type Classification using Multi-Stage Graph Embedding Method.- Removing the Black-Box from Machine Learning.- Using Machine Learning to Identify Patterns in Learner-Submitted Code for the Purpose of Assessment.- Fitness Function Comparison for Unsupervised Feature Selection with Permutational-Based Dierential Evolution.- A Method for Counting Models on Cubic Boolean Formulas.- Automatic Identication of Learning Styles through Behavioral Patterns.- Comparison of Classiers in Challenge Scheme.- Deep Learning and Neural Networks: Robust Zero-Watermarking for Medical Images based on Deep Learning Feature Extraction.- Plant Stress Recognition Using Deep Learning and 3D Reconstruction.- Segmentation and Classification Networks for Corn/Weed Detection under Excessive Field Variabilities.- Leukocyte Recognition Using a Modified AlexNet and Image to Image GAN Data Augmentation.- Spoofing Detection for Speaker Verification with Glottal Flow and 1D Pure Convolutional Networks.- Estimation of Stokes Parameters using Deep Neural Networks.- Experimental Study of the Performance of Convolutional Neural Networks Applied in Art Media Classification.- Medical Applications of Pattern Recognition: Hadamard Layer to Improve Semantic Segmentation in Medical Images.- Patterns in Genesis of Breast Cancer Tumor.- Realistic Simulation of Event-Related Potentials and their usual Noise and Interferences for Pattern Recognition.- Chest X-ray Imaging Severity Score of COVID-19 Pneumonia.- Leukocyte Detection with Novel Fully Convolutional Network and a New Dataset of Blood Smear Complete Samples.- Comparison of Deep Learning Architectures in Classification of Microcalcifications Clusters in Digital Mammograms.- Retinal Artery and Vein Segmentation using an Image-to-image Conditional Adversarial Network.- Evaluation of Heatmaps as an Explicative Method for Classifying Acute Lymphoblastic Leukemia Cells.- Language Processing and Recognition: Machine Learning Models Applied in Sign Language Recognition.- Urdu Semantic Parsing: An Improved SEMPRE Framework for Conversion of Urdu Language Web Queries to Logical forms.- Improving the Identification of Abusive Language through Careful Design of Pre-training Tasks.- Industrial Applications of Pattern Recognition: TOPSIS Method for Multiple-Criteria Decision-Making Applied to Trajectory Selection for Autonomous Driving.- Machine-learning based Estimation of the Bending Magnitude Sensed by a Fiber Optic Device.- Graph-based Semi-Supervised Learning using Riemannian Geometry Distance for Motor Imagery Classification.

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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 book constitutes the proceedings of the 21st International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2023, held in Kumamoto, Japan, during August 2023. The 31 papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named: Computer Vision, Affective Computing and Human Interactions, Managing the Biodiversity, Robotics and Drones, Machine Learning.Table of ContentsA hybrid quantum-classical segment-based Stereo Matching algorithm.- Continuous Exposure for Extreme Low-Light Imaging.- Semi-supervised Classification and Segmentation of Forest Fire using Autoencoders.- Descriptive and coherent paragraph generation for image paragraph captioning using vision transformer and post-processing.- Pyramid Swin Transformer for Multi-Task: Expanding to more computer vision tasks.- Person activity classification from an aerial sensor based on a multi-level deep features.- Person Quick-Search Approach based on a Facial Semantic Attributes Description.- Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers.- BENet: A lightweight bottom-up framework for context-aware emotion recognition.- Yolopoint: Joint Keypoint and Object Detection.- Less-than-one shot 3d segmentation hijacking a pre-trained space-time memory network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- A Single Image Neuro-Geometric Depth Estimation.- Wave-shaping Neural Activation for Improved 3D Model Reconstruction from Sparse Point Clouds.- A Deep Learning Approach to Segment High-Content Images of the E.coli Bacteria.- Multimodal Emotion Recognition System Through Three Different Channels (MER-3C).- Multi-Modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets.- A Contrario Mosaic Analysis for Image Forensics.- IRIS SEGMENTATION TECHNIQUE USING IRIS-UNet METHOD.- Image Acquisition by Image Retrieval with Color Aesthetics.- Improved Obstructed Facial Feature Reconstruction for Emotion Recognition with Minimal Change CycleGANs.- Quality assessment for high dynamic range stereoscopic omnidirectional image system.- Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging.- Reinforcement Learning for truck Eco-driving: a serious game as driving assistance system.- Underwater mussel segmentation using smoothed shape descriptors with random forest.- A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain simulation.- Construction of a novel data set for pedestrian tree species detection using google street view data.- Texture-based Data Augmentation for Small Datasets.- Multimodal Representations for Teacher-Guided Compositional Visual Reasoning.- Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces.

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Book SynopsisThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.The ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high quality peer reviewed oral and poster presentations. Table of Contents​Computer Vision and Pattern Recognition: Temporal Global Re-Detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking.- VLNet: A Multi-task Network for Joint Vehicle and Lane Detection.- Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-View Stereo.- A Novel Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Visual Attention Network.- Efficient Few-shot Image Generation via Lightweight Octave Generative Adversarial Networks.- Incorporating Global Correlation and Local Aggregation for Efficient Visual Localization.- Deep Interactive Image Semantic and Instance Segmentation.- Learning High-Performance Spiking Neural Networks With Multi-Compartment Spiking Neurons.- Attribute Space Analysis for Image Editing.-SAGAN: Self-Attention Generative Adversarial Net-work for RGB-D Saliency Prediction.- Behavioural State Detection Algorithm for Infants and Toddlers Incorporating Multi-scale Contextual Features.- Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark.- Dual Fusion Network for Hyperspectral Semantic Segmentation.- Strip-FFT Transformer for single image deblurring.- Vision-Language Adaptive Mutual Decoder for OOV-STR.- DensityLayout: Density-conditioned Layout GAN for Visual-textual Presentation Designs.- GLTCM: Global-Local Temporal and Cross-Modal Network for Audio-Visual Event Localization.- Recent Advances in Class-Incremental Learning.- TTA-GCN: Temporal Topology Aggregation for Skeleton-Based Action Recognition.- A Stable Long-Term Tracking Method For Group-Housed Pigs.- Dynamic Attention for Isolated Sign Language Recognition with Reinforcement Learning.- A Segmentation Method based on SE Attention and U-Net for Apple Image.- Human Action Recognition Method based on spatio-temporal relationship.- Facial expression recognition from occluded images using deep convolution neural network with Vision Transformer.- Learning Discriminative Proposal Representation for Multi-Object Tracking.- Virtual-Violence: A Brand-New Dataset for Video Violence Recognition.- A novel Attention-DeblurGAN-Based Defogging Algorithm.- Multi-Modal Context-Aware Network for Scene Graph Generation.- VQA-CLPR: Turning a Visual Question Answering Model into a Chinese License Plate Recognizer.- Unsupervised Vehicle Re-Identification via Raw UAV Videos.- Distance-Aware Vector-Field and Vector Screening Strategy for 6D Object Pose Estimation.- SSTA-Net: Self-supervised Spatio-Temporal Attention Network for Action Recognition.- Gesture recognition method based on Sim-ConvNeXt model.- Research on Airborne Infrared Target Recognition Method based on Target-Environment Coupling.- Semantic and Gradient Guided Scene Text Image Super-Resolution.

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Book SynopsisThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.The ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high-quality peer reviewed oral and poster presentations.Table of Contents​Biological and Medical Image Processing: An efficient medical image fusion via online convolutional sparse coding with sample-dependent dictionary.- Multimodal Medical Image Fusion based on Multichannel Aggregated Network.- Multi-Path Feature Fusion and Channel Feature Pyramid for Brain Tumor Segmentation in MRI.- Near infrared video heart rate detection based on multi-region selection and robust principal component analysis- Neural video: A novel framework for interpreting the spatiotemporal activities of the human brain.- Local Fusion Synthetic CT Network for Improving the Quality of CBCT in Cervical Cancer Radiotherapy.- Causality-Inspired Source-Free Domain Adaptation for Medical Image Classification.- Pixel-Correlation-Based Scar Screening in Hypertrophic Myocardium.- Dictionary Matching Based 2D Thin Slice Generalized Slice-dithered Enhanced Resolution (gSlider) Variable Flip Angle T1 Mapping.- Deep Low-rank Multimodal Fusion with Inter-modal Distribution Difference Constraint for ASD Diagnosis.- Cross-domain Tongue Image Segmentation based on Deep Adversarial Networks and Entropy Minimization.- Residual Inter-slice Feature Learning for 3D Organ Segmentation.- Color and Multispectral Processing: A Cross-paired Wavelet Based Spatiotemporal Fusion Network for Remote Sensing Images.- Coupled Dense Convolutional Neural Networks with Autoencoder For Unsupervised Hyperspectral Super Resolution.- Computational Imaging: Multi-scale Non-Local Bidirectional Fusion for Video Super-Resolution.- Multi-View and Stereoscopic Processing: Learning a Deep Fourier Channel Attention Generative Adversarial Network for Light Field Image Super-Resolution.- Synthesizing a large scene with multiple NeRFs.- Neural Implicit 3D Shapes from Single Images with Spatial Patterns.- Multimedia Security: Dense Visible Watermark Removal with Progressive Feature Propagation.- When Diffusion Model Meets with Adversarial Attack: Generating Transferable Adversarial Examples on Face Recognition.- TPM: Two-stage prediction mechanism for universal adversarial patch defense.- Surveillance and remote sensing: WSAD-Net: Weakly Supervised Anomaly Detection in Untrimmed Surveillance Videos.- Multi-object Tracking in Remote Sensing Video Based on Motion and Multi-scale Local Cost Volume.- Density Map Augmentation-based Point-to-point Vehicle Counting and Localization in Remote Sensing Imagery with Limited Resolution.- Large Window Attention based Transformer Network for Change Detection of Remote Sensing Images.- U-TEN:An Unsupervised Two-branch Enhancement Network for Object Detection under Complex-Light Condition.- Virtual Reality: Design of virtual assembly guidance system based on mixed reality.- Blind Omnidirectional Image Quality Assessment Based on Swin Transformer with Scanpath-Oriented.- Audio-visual Saliency for Omnidirectional Videos.

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Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.

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Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.

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Book SynopsisThis book offers an overview of the field of continuous biometric authentication systems, which capture and continuously authenticate biometrics from user devices. This book first covers the traditional methods of user authentication and discusses how such techniques have become cumbersome in the world of mobile devices and short usage sessions. The concept of continuous biometric authentication systems is introduced and their construction is discussed. The different biometrics that these systems may utilise (e.g.: touchscreen-gesture interactions) are described and relevant studies surveyed. It also surveys important considerations and challenges.This book brings together a wide variety of key motivations, components and advantages of continuous biometric authentication systems. The overview is kept high level, so as not to limit the scope to any single device, biometric trait, use-case, or scenario. Therefore, the contents of this book are applicable to devices ranging from smartphones to desktop computers, utilising biometrics ranging from face recognition to keystroke dynamics. It also provides metrics from a variety of existing systems such that users can identify the advantages and disadvantages of different approaches.This book targets researchers and lecturers working in authentication, as well as advanced-level students in computer science interested in this field. The book will also be of interest to technical professionals working in cyber security.Table of ContentsIntroduction.- Traditional Authentication.- Continuous Authentication.- Biometrics for Continuous Authentication.- Considerations and Challenges.- Conclusion.

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Book SynopsisResearchers, academicians and professionals expone in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering.

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Book SynopsisThis book explores the possible applications of Artificial Intelligence in Virtual environments. These were previously mainly associated with gaming, but have largely extended their area of application, and are nowadays used for promoting collaboration in work environments, for training purposes, for management of anxiety and pain, etc.. The development of Artificial Intelligence has given new dimensions to the research in this field.

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Book SynopsisCognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

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Book SynopsisThis book focuses on the use of AI/ML-based techniques to solve issues related to IoT-based environments, as well as their applications. It addresses, among others, signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction, software-defi ned networking, congestion control, communication network optimization, security, and anomaly detection.

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