Image processing Books
John Wiley & Sons Inc Computer Vision and Imaging in Intelligent
Book SynopsisComputer Vision and Imaging in Intelligent Transportation Systems Robert P.Table of ContentsList of Contributors xiii Preface xvii Acknowledgments xxi About the Companion Website xxiii 1 Introduction 1 Raja Bala and Robert P. Loce 1.1 Law Enforcement and Security 1 1.2 Efficiency 4 1.3 Driver Safety and Comfort 5 1.4 A Computer Vision Framework for Transportation Applications 7 1.4.1 Image and Video Capture 8 1.4.2 Data Preprocessing 8 1.4.3 Feature Extraction 9 1.4.4 Inference Engine 10 1.4.5 Data Presentation and Feedback 11 Part I Imaging from the Roadway Infrastructure 15 2 Automated License Plate Recognition 17 Aaron Burry and Vladimir Kozitsky 2.1 Introduction 17 2.2 Core ALPR Technologies 18 2.2.1 License Plate Localization 19 2.2.2 Character Segmentation 24 2.2.3 Character Recognition 28 2.2.4 State Identification 38 3 Vehicle Classification 47 Shashank Deshpande, Wiktor Muron and Yang Cai 3.1 Introduction 47 3.2 Overview of the Algorithms 48 3.3 Existing AVC Methods 48 3.4 LiDAR Imaging-Based 49 3.4.1 LiDAR Sensors 49 3.4.2 Fusion of LiDAR and Vision Sensors 50 3.5 Thermal Imaging-Based 53 3.5.1 Thermal Signatures 53 3.5.2 Intensity Shape-Based 56 3.6 Shape- and Profile-Based 58 3.6.1 Silhouette Measurements 60 3.6.2 Edge-Based Classification 65 3.6.3 Histogram of Oriented Gradients 67 3.6.4 Haar Features 68 3.6.5 Principal Component Analysis 69 3.7 Intrinsic Proportion Model 72 3.8 3D Model-Based Classification 74 3.9 SIFT-Based Classification 74 3.10 Summary 75 4 Detection of Passenger Compartment Violations 81 Orhan Bulan, Beilei Xu, Robert P. Loce and Peter Paul 4.1 Introduction 81 4.2 Sensing within the Passenger Compartment 82 4.2.1 Seat Belt Usage Detection 82 4.2.2 Cell Phone Usage Detection 83 4.2.3 Occupancy Detection 83 4.3 Roadside Imaging 84 4.3.1 Image Acquisition Setup 84 4.3.2 Image Classification Methods 85 4.3.3 Detection-Based Methods 94 5 Detection of Moving Violations 101 Wencheng Wu, Orhan Bulan, Edgar A. Bernal and Robert P. Loce 5.1 Introduction 101 5.2 Detection of Speed Violations 101 5.2.1 Speed Estimation from Monocular Cameras 102 5.2.2 Speed Estimation from Stereo Cameras 108 5.2.3 Discussion 115 5.3 Stop Violations 115 5.3.1 Red Light Cameras 115 5.4 Other Violations 125 5.4.1 Wrong-Way Driver Detection 125 5.4.2 Crossing Solid Lines 126 6 Traffic Flow Analysis 131 Rodrigo Fernandez, Muhammad Haroon Yousaf, Timothy J. Ellis, Zezhi Chen and Sergio A. Velastin 6.1 What is Traffic Flow Analysis? 131 6.1.1 Traffic Conflicts and Traffic Analysis 131 6.1.2 Time Observation 132 6.1.3 Space Observation 133 6.1.4 The Fundamental Equation 133 6.1.5 The Fundamental Diagram 133 6.1.6 Measuring Traffic Variables 134 6.1.7 Road Counts 135 6.1.8 Junction Counts 135 6.1.9 Passenger Counts 136 6.1.10 Pedestrian Counts 136 6.1.11 Speed Measurement 136 6.2 The Use of Video Analysis in Intelligent Transportation Systems 137 6.2.1 Introduction 137 6.2.2 General Framework for Traffic Flow Analysis 137 6.2.3 Application Domains 143 6.3 Measuring Traffic Flow from Roadside CCTV Video 144 6.3.1 Video Analysis Framework 144 6.3.2 Vehicle Detection 146 6.3.3 Background Model 146 6.3.4 Counting Vehicles 149 6.3.5 Tracking 150 6.3.6 Camera Calibration 150 6.3.7 Feature Extraction and Vehicle Classification 152 6.3.8 Lane Detection 153 6.3.9 Results 155 6.4 Some Challenges 156 7 Intersection Monitoring Using Computer Vision Techniques for Capacity, Delay, and Safety Analysis 163 Brendan Tran Morris and Mohammad Shokrolah Shirazi 7.1 Vision-Based Intersection Analysis: Capacity, Delay, and Safety 163 7.1.1 Intersection Monitoring 163 7.1.2 Computer Vision Application 164 7.2 System Overview 165 7.2.1 Tracking Road Users 166 7.2.2 Camera Calibration 169 7.3 Count Analysis 171 7.3.1 Vehicular Counts 171 7.3.2 Nonvehicular Counts 173 7.4 Queue Length Estimation 173 7.4.1 Detection-Based Methods 174 7.4.2 Tracking-Based Methods 175 7.5 Safety Analysis 177 7.5.1 Behaviors 178 7.5.2 Accidents 182 7.5.3 Conflicts 185 7.6 Challenging Problems and Perspectives 187 7.6.1 Robust Detection and Tracking 187 7.6.2 Validity of Prediction Models for Conflict and Collisions 188 7.6.3 Cooperating Sensing Modalities 189 7.6.4 Networked Traffic Monitoring Systems 189 7.7 Conclusion 189 8 Video-Based Parking Management 195 Oliver Sidla and Yuriy Lipetski 8.1 Introduction 195 8.2 Overview of Parking Sensors 197 8.3 Introduction to Vehicle Occupancy Detection Methods 200 8.4 Monocular Vehicle Detection 200 8.4.1 Advantages of Simple 2D Vehicle Detection 200 8.4.2 Background Model–Based Approaches 200 8.4.3 Vehicle Detection Using Local Feature Descriptors 202 8.4.4 Appearance-Based Vehicle Detection 203 8.4.5 Histograms of Oriented Gradients 204 8.4.6 LBP Features and LBP Histograms 207 8.4.7 Combining Detectors into Cascades and Complex Descriptors 208 8.4.8 Case Study: Parking Space Monitoring Using a Combined Feature Detector 208 8.4.9 Detection Using Artificial Neural Networks 211 8.5 Introduction to Vehicle Detection with 3D Methods 213 8.6 Stereo Vision Methods 215 8.6.1 Introduction to Stereo Methods 215 8.6.2 Limits on the Accuracy of Stereo Reconstruction 216 8.6.3 Computing the Stereo Correspondence 217 8.6.4 Simple Stereo for Volume Occupation Measurement 218 8.6.5 A Practical System for Parking Space Monitoring Using a Stereo System 218 8.6.6 Detection Methods Using Sparse 3D Reconstruction 220 9 Video Anomaly Detection 227 Raja Bala and Vishal Monga 9.1 Introduction 227 9.2 Event Encoding 228 9.2.1 Trajectory Descriptors 229 9.2.2 Spatiotemporal Descriptors 231 9.3 Anomaly Detection Models 233 9.3.1 Classification Methods 233 9.3.2 Hidden Markov Models 234 9.3.3 Contextual Methods 234 9.4 Sparse Representation Methods for Robust Video Anomaly Detection 236 9.4.1 Structured Anomaly Detection 237 9.4.2 Unstructured Video Anomaly Detection 243 9.4.3 Experimental Setup and Results 245 9.5 Conclusion and Future Research 253 Part II Imaging from and within the Vehicle 257 10 Pedestrian Detection 259 Shashank Deshpande and Yang Cai 10.1 Introduction 259 10.2 Overview of the Algorithms 259 10.3 Thermal Imaging 260 10.4 Background Subtraction Methods 261 10.4.1 Frame Subtraction 261 10.4.2 Approximate Median 262 10.4.3 Gaussian Mixture Model 263 10.5 Polar Coordinate Profile 263 10.6 Image-Based Features 265 10.6.1 Histogram of Oriented Gradients 265 10.6.2 Deformable Parts Model 266 10.6.3 LiDAR and Camera Fusion–Based Detection 266 10.7 LiDAR Features 268 10.7.1 Preprocessing Module 268 10.7.2 Feature Extraction Module 268 10.7.3 Fusion Module 268 10.7.4 LIPD Dataset 270 10.7.5 Overview of the Algorithm 270 10.7.6 LiDAR Module 272 10.7.7 Vision Module 275 10.7.8 Results and Discussion 276 10.7.8.1 LiDAR Module 276 10.7.8.2 Vision Module 276 10.8 Summary 280 11 Lane Detection and Tracking Problems in Lane Departure Warning Systems 283 Gianni Cario, Alessandro Casavola and Marco Lupia 11.1 Introduction 283 11.2 LD: Algorithms for a Single Frame 285 11.2.1 Image Preprocessing 285 11.2.2 Edge Extraction 287 11.2.3 Stripe Identification 291 11.2.4 Line Fitting 294 11.3 LT Algorithms 297 11.3.1 Recursive Filters on Subsequent N frames 298 11.3.2 Kalman Filter 298 11.4 Implementation of an LD and LT Algorithm 299 11.4.1 Simulations 300 11.4.2 Test Driving Scenario 300 11.4.3 Driving Scenario: Lane Departures at Increasing Longitudinal Speed 300 11.4.4 The Proposed Algorithm 302 11.4.5 Conclusions 303 12 Vision-Based Integrated Techniques for Collision Avoidance Systems 305 Ravi Satzoda and Mohan Trivedi 12.1 Introduction 305 12.2 Related Work 307 12.3 Context Definition for Integrated Approach 307 12.4 ELVIS: Proposed Integrated Approach 308 12.4.1 Vehicle Detection Using Lane Information 309 12.4.2 Improving Lane Detection using On-Road Vehicle Information 312 12.5 Performance Evaluation 313 12.5.1 Vehicle Detection in ELVIS 313 12.5.2 Lane Detection in ELVIS 316 12.6 Concluding Remarks 319 13 Driver Monitoring 321 Raja Bala and Edgar A. Bernal 13.1 Introduction 321 13.2 Video Acquisition 322 13.3 Face Detection and Alignment 323 13.4 Eye Detection and Analysis 325 13.5 Head Pose and Gaze Estimation 326 13.5.1 Head Pose Estimation 326 13.5.2 Gaze Estimation 328 13.6 Facial Expression Analysis 332 13.7 Multimodal Sensing and Fusion 334 13.8 Conclusions and Future Directions 336 14 Traffic Sign Detection and Recognition 343 Hasan Fleyeh 14.1 Introduction 343 14.2 Traffic Signs 344 14.2.1 The European Road and Traffic Signs 344 14.2.2 The American Road and Traffic Signs 347 14.3 Traffic Sign Recognition 347 14.4 Traffic Sign Recognition Applications 348 14.5 Potential Challenges 349 14.6 Traffic Sign Recognition System Design 349 14.6.1 Traffic Signs Datasets 352 14.6.2 Colour Segmentation 354 14.6.3 Traffic Sign's Rim Analysis 359 14.6.4 Pictogram Extraction 364 14.6.5 Pictogram Classification Using Features 365 14.7 Working Systems 369 15 Road Condition Monitoring 375 Matti Kutila, Pasi Pyykonen, Johan Casselgren and Patrik Jonsson 15.1 Introduction 375 15.2 Measurement Principles 376 15.3 Sensor Solutions 377 15.3.1 Camera-Based Friction Estimation Systems 377 15.3.2 Pavement Sensors 379 15.3.3 Spectroscopy 380 15.3.4 Roadside Fog Sensing 382 15.3.5 In-Vehicle Sensors 383 15.4 Classification and Sensor Fusion 386 15.5 Field Studies 390 15.6 Cooperative Road Weather Services 394 15.7 Discussion and Future Work 395 Index 399
£94.95
John Wiley & Sons Inc Adobe Photoshop CC For Dummies
Book SynopsisTable of ContentsIntroduction 1 About This Book 1 Conventions Used in This Book 2 Icons Used in This Book 3 How to Use This Book 3 Part 1: Getting Started with Photoshop CC 5 Chapter 1: An Overview of Photoshop 7 Exploring Adobe Photoshop 7 What Photoshop is designed to do 8 Other things you can do with Photoshop 9 Viewing Photoshop’s Parts and Processes 10 Reviewing basic computer operations 10 Photoshop’s incredible selective Undo 12 Installing Photoshop: Need to know 14 Chapter 2: Knowing Just Enough about Digital Images 17 What Exactly is a Digital Image? 18 The True Nature of Pixels 18 How Many Pixels Can Dance on the Head of a Pin? 21 Resolution revelations 21 Resolving image resolution 22 File Formats: Which Do You Need? 30 Formats for digital photos 31 Formats for web graphics 33 Formats for commercial printing 34 Formats for PowerPoint and Word 36 Chapter 3: Taking the Chef’s Tour of Your Photoshop Kitchen 37 Food for Thought: How Things Work 38 Ordering from the menus 39 Your platter full of panels 40 The tools of your trade 42 Get Cookin’ with Customization 44 Clearing the table: Custom workspaces 44 Spoons can’t chop: Creating tool presets 47 Season to Taste: The Photoshop Settings 48 Standing orders: Setting the Preferences 48 Ensuring consistency: Color Settings 55 When Good Programs Go Bad: Fixing Photoshop 57 Chapter 4: From Pics to Prints: Photoshop for Beginners 59 Bringing Images into Photoshop 59 Downloading from your digital camera 60 Scanning prints 61 Keeping Your Images Organized 66 Creating a folder structure 66 Using Adobe Bridge 67 Renaming image files easily 69 Printing Your Images 71 Cropping to a specific aspect ratio 71 Remembering resolution 73 Controlling color using File ➪ Print 74 Considering color management solutions 75 Printing alternatives 76 Sharing Your Images 77 Emailing and AirDropping your images 78 Creating PDFs and websites 78 Part 2: Easy Enhancements for Digital Images 79 Chapter 5: Making Tonality and Color Look Natural 81 Adjusting Tonality to Make Your Images Pop 82 Histograms Simplified 82 Using Photoshop’s Auto Corrections 84 Levels and Curves and You 85 Level-headed you! 86 Tonal corrections with the eyedroppers 89 Adjusting your curves without dieting 90 Grabbing Even More Control 92 Using Shadows/Highlights 93 Changing exposure after the fact 96 Using Photoshop’s toning tools 96 What is Color in Photoshop? 97 Which color mode should you choose? 98 Does a color model make a difference? 101 Why should you worry about color depth? 102 Making Color Adjustments in Photoshop 104 Choosing color adjustment commands 106 Manual corrections in individual channels 117 The People Factor: Flesh Tone Formulas 118 Chapter 6: The Adobe Camera Raw Plug-In 121 Understanding the Raw Facts 121 What’s the big deal about Raw? 123 Working in Raw 124 The Camera Raw Interface 126 Camera Raw’s Tools and buttons 126 The histogram 132 The preview area 132 Workflow Options and presets 133 Making Adjustments in Camera Raw’s Edit Panel 134 The Basic section 134 The Curve section 137 The Detail section 137 The Color Mixer section 138 The Color Grading section 138 The Optics and Geometry sections 140 The Effects section 141 The Calibration section 141 The Camera Raw Cancel, Done, and Open buttons 142 Chapter 7: Fine-Tuning Your Fixes 143 What is a Selection? 144 Feathering and Anti-aliasing 146 Making Your Selections with Tools 148 Marquee selection tools 148 Lasso selection tools 152 The Object Selection tool 153 The Quick Selection tool 153 The Magic Wand tool 154 Select and Mask 155 Your Selection Commands 156 The primary selection commands 157 The Color Range command 158 The Focus Area command 159 The Select ➪ Subject command 160 The Select ➪ Sky command 161 Selection modification commands 161 Transforming the shape of selections 161 Edit in Quick Mask mode 163 The mask-related selection commands 164 Masks: Not Just for Halloween Anymore 164 Saving and loading selections 165 Editing an alpha channel 165 Adding masks to layers and Smart Objects 167 Masking with vector paths 167 Adjustment Layers: Controlling Changes 168 Adding an adjustment layer 168 Limiting your adjustments 170 Chapter 8: Common Problems and Their Cures 173 Making People Prettier 174 Getting the red out digitally 174 The digital fountain of youth 175 Dieting digitally 176 De-glaring glasses 179 Whitening teeth 179 Reducing Noise in Your Images 179 Decreasing digital noise 180 Eliminating luminance noise 181 Fooling Around with Mother Nature 181 Removing the unwanted from photos 181 Eliminating the lean: Fixing perspective 185 Rotating images precisely 187 Adding a beautiful sky 188 Part 3: Creating “Art” in Photoshop 189 Chapter 9: Combining Images 191 Compositing Images: 1 + 1 = 1 192 Understanding layers 192 Why you should use Smart Objects 194 Using the basic blending modes 195 Opacity, transparency, and layer masks 198 Creating clipping groups 199 Making composited elements look natural 200 Making Complex Selections 201 Vanishing Point 204 Creating Panoramas with Photomerge 208 Chapter 10: Precision Edges with Vector Paths 211 Pixels, Paths, and You 212 Easy Vectors: Using Shapes 213 Your basic shape tools 214 The Custom Shape tool 216 More custom shapes — free! 217 Changing the appearance of the shape layer 219 Simulating a multicolor shape layer 220 Using Your Pen Tool to Create Paths 221 Understanding paths 222 Clicking and dragging your way down the path of knowledge 222 A closer look at the Paths panel 226 Customizing Any Path 229 Adding, deleting, and moving anchor points 230 Combining paths 232 Tweaking type for a custom font 233 Chapter 11: Dressing Up Images with Layer Styles 235 What Are Layer Styles? 235 Using the Styles Panel 237 Creating Custom Layer Styles 239 Exploring the Layer Style menu 239 Exploring the Layer Style dialog box 241 Layer effects basics 242 Opacity, fill, and advanced blending 251 Saving Your Layer Styles 254 Adding styles to the Styles panel 254 Preserving your layer styles 255 Chapter 12: Giving Your Images a Text Message 257 Making a Word Worth a Thousand Pixels 258 A type tool for every season, or reason 260 What are all those options? 262 Taking control of your text with panels 266 The panel menus — even more options 269 Working with Styles 271 Putting a picture in your text 272 Creating Paragraphs with Type Containers 274 Selecting alignment or justification 276 Ready, BREAK! Hyphenating your text 277 Shaping Up Your Language with Warp Text and Type on a Path 278 Applying the predefined warps 278 Customizing the course with paths 279 Chapter 13: Painting in Photoshop 283 Discovering Photoshop’s Painting Tools 284 Painting with the Brush tool 286 Adding color with the Pencil tool 289 Removing color with the Eraser tool 289 Working with Panels and Selecting Colors 290 An overview of options 290 Creating and saving custom brush tips 293 Picking a color 294 Fine Art Painting with Specialty Brush Tips and the Mixer Brush 297 Exploring erodible brush tips 297 Introducing airbrush and watercolor tips 297 Mixing things up with the Mixer Brush 298 Filling, Stroking, Dumping, and Blending Colors 300 Deleting and dumping to add color 300 Using gradients 301 Chapter 14: Filters: The Fun Side of Photoshop 305 Smart Filters: Your Creative Insurance Policy 306 The Filters You Really Need 307 Sharpening to focus the eye 308 Unsharp Mask 308 Smart Sharpen 310 Shake Reduction 311 Blurring images and selections 312 The other Blur filters 315 Correcting for the vagaries of lenses 316 Cleaning up with Reduce Noise 320 Getting Creative and Artistic 321 Photo to painting with the Oil Paint filter 321 Working with the Filter Gallery 322 Push, Pull, and Twist with Liquify 325 What Are Neural Filters? 327 The original Neural Filters 328 Neural Filters in public beta testing 329 Proposed Neural Filters 330 Do I Need Those Other Filters? 330 Adding drama with Lighting Effects 331 Maximum and Minimum 331 Bending and bubbling 332 Creating clouds 332 Part 4: Power Photoshop 333 Chapter 15: Streamlining Your Work in Photoshop 335 Ready, Set, Action! 336 Recording your own Actions 337 Working with the Batch command 342 Find It Fast with Discover 344 Creating Contact Sheets and Presentations 344 Creating a PDF presentation 345 Collecting thumbnails in a contact sheet 347 Scanning Multiple Photos in One Pass 349 Sticking to the Script 350 Chapter 16: Working with Video and Animation 353 Importing and Enhancing Video Clips 353 Getting video into Photoshop 354 Adjusting the length of video and audio clips 356 Adding adjustment layers and painting on video layers 357 Transitioning, titling, and adding special effects 358 Transforming video layers 361 Rendering and exporting video 361 Creating Animations in Photoshop 362 Building frame-based animations 362 Creating frame content 363 Tweening to create intermediary frames 365 Specifying frame rate 366 Optimizing and saving your animation 366 Part 5: The Part of Tens 367 Chapter 17: Ten Specialized Features of Photoshop CC 369 Using Smart Object Stack Modes 370 The Mean Stack Mode 372 Working with 3D Artwork 372 Creating 3D objects 373 Adding 3D objects 373 Rendering and saving 3D scenes 374 Measuring, Counting, and Analyzing Pixels 374 Measuring length, area, and more 374 Calculating with Vanishing Point 376 Counting crows or maybe avian flu 376 Viewing Your DICOM Medical Records 377 Ignoring MATLAB 378 Chapter 18: Ten Ways to Integrate Your iPad 379 Using Sidecar to Add an iPad to Your Screen 379 Sidecar System Requirements 380 Arranging Your iPad’s Screen 380 Mirroring the Screens 381 Maximizing the Screen Space 381 Making Use of Photoshop on the iPad 382 Using the Cloud with Photoshop on the iPad and Desktop 383 Using Other Adobe iPad Apps 384 Does the iPad Replace My Wacom Tablet? 384 Setting Wacom Tablet Preferences for Touch Keys and Touch Ring 385 Chapter 19: Ten Things to Know about HDR 387 Understanding HDR 387 Capturing for Merge to HDR Pro 389 Preparing Raw “Exposures” in Camera Raw 390 Working with Merge to HDR Pro 391 Saving 32-Bit HDR Images 394 HDR Toning 394 Painting and the Color Picker in 32-Bit 395 Filters and Adjustments in 32-Bit 396 Selections and Editing in 32-Bit 397 Printing HDR Images 397 Appendix: Photoshop CC’S Blending Modes 399 Index 403
£23.99
John Wiley & Sons Inc Machine Learning Algorithms for Signal and Image
Book SynopsisMachine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systTable of ContentsSection-1 Machine & Deep Learning techniques for Image Processing 1.1 Image Features in Machine Learning 1.2 Image Segmentation and Classification using Deep Learning 1.3 Deep Learning based Synthetic Aperture Radar Image Classification 1.4 Design Perspectives of Multitask Deep Learning Models and Applications 1.5 Image Reconstruction using Deep Learning 1.6 Machine and Deep Learning Techniques for Image Super-Resolution Section-2 Machine & Deep Learning techniques for Text and Speech Processing 2.1 Machine and Deep Learning Techniques for Text and Speech Processing 2.2 Manipuri Handwritten Script Recognition using Machine and Deep Learning 2.3 Comparison of Different Text Extraction Techniques for Complex Color Images 2.4 Smart Text Reader System for Blind Person using Machine and Deep Learning 2.5 Machine Learning Techniques for Deaf People 2.6 Design and Development of Chatbot based on Reinforcement Learning 2.7 DNN based Speech Quality Enhancement and Multi-speaker Separation for Automatic Speech Recognition System 2.8 Design and Development of Real-Time Music Transcription using Digital Signal Processing Section-3 Applications of Signal and Image Processing with Machine & Deep learning techniques 3.1 Role of Machine Learning in Wrist Pulse Analysis 3.2 An Explainable Convolutional Neural Network based Method for Skin Lesion Classification from Dermoscopic Images 3.3 Future of Machine-Learning and Deep-Learning in Health-Care Monitoring System 3.4 Usage of AI & Wearable IoT Devices for Healthcare Data: A Study 3.5 Impact of IoT in Biomedical Applications using Machine and Deep Learning 3.6 Wireless Communications using Machine Learning and Deep Learning 3.7 Applications of Machine Learning and Deep Learning in Smart Agriculture 3.8 Structural Damage Prediction from Earthquakes using Deep Learning 3.9 Machine Learning and Deep Learning Techniques in Social Sciences 3.1O Green Energy using Machine and Deep Learning 3.11 Light Deep CNN Approach for Multi-Label Pathology Classification using Frontal Chest X-Ray Index
£109.80
Taylor & Francis Ltd Medical Image Processing Reconstruction and
Book SynopsisDifferently oriented specialists and students involved in image processing and analysis need to have a firm grasp of concepts and methods used in this now widely utilized area. This book aims at being a single-source reference providing such foundations in the form of theoretical yet clear and easy to follow explanations of underlying generic concepts.Medical Image Processing, Reconstruction and Analysis Concepts and Methods explains the general principles and methods of image processing and analysis, focusing namely on applications used in medical imaging. The content of this book is divided into three parts: Part I Images as Multidimensional Signals provides the introduction to basic image processing theory, explaining it for both analogue and digital image representations. Part II Imaging Systems as Data Sources offers a non-traditional view on imaging modalities, explaining their prTable of ContentsPART I Images as Multidimensional Signals Chapter 1 Analogue (Continuous-Space) Image Representation Chapter 2 Digital Image Representation PART II Imaging Systems as Data Sources Chapter 3 Planar X-Ray Imaging Chapter 5 Magnetic Resonance Imaging Chapter 6 Nuclear Imaging Chapter 7 Ultrasonography Chapter 8 Other Modalities PART III Image Processing and Analysis Chapter 9 Reconstructing Tomographic Images Chapter 10 Image Fusion Chapter 11 Image Enhancement Chapter 12 Image Restoration Chapter 13 Lower-Level Image Analysis Chapter 14 Selected Higher-Level Image Analysis Methods Chapter 15 Medical Image Processing Environment
£199.50
Taylor & Francis Ltd Handbook of Nuclear Medicine and Molecular
Book SynopsisMathematical modelling is an important part of nuclear medicine. Therefore, several chapters of this book have been dedicated towards describing this topic. In these chapters, an emphasis has been put on describing the mathematical modelling of the radiation transport of photons and electrons, as well as on the transportation of radiopharmaceuticals between different organs and compartments. It also includes computer models of patient dosimetry. Two chapters of this book are devoted towards introducing the concept of biostatistics and radiobiology. These chapters are followed by chapters detailing dosimetry procedures commonly used in the context of diagnostic imaging, as well as patient-specific dosimetry for radiotherapy treatments. For safety reasons, many of the methods used in nuclear medicine and molecular imaging are tightly regulated. Therefore, this volume also highlights the basic principles for radiation protection. It discusses the process of how guidelines and reTable of ContentsContentsPreface...............................................................................................................................viiEditor..................................................................................................................................ixContributors.......................................................................................................................xiChapter 1 Introduction to Biostatistics..........................................................................1Johan Gustafsson and Markus NilssonChapter 2 Radiobiology....................................................................................................17Lidia Strigari and Marta CremonesiChapter 3 Diagnostic Dosimetry.............................................................................................33Lennart Johansson† and Martin AnderssonChapter 4 Time- activity Curves: Data, Models, Curve Fitting, and Model Selection..........................69Gerhard GlattingChapter 5 Tracer Kinetic Modelling and Its Use in PET Quantification..............................................83Mark Lubberink and Michel KooleChapter 6 Principles of Radiological Protection in Healthcare..........................................................101Soren MattssonChapter 7 Controversies in Nuclear Medicine Dosimetry..................................................................115Michael G. StabinChapter 8 Monte Carlo Simulation of Photon and Electron Transport in Matter..............................123Jose M. Fernandez-VareaChapter 9 Patient Models for Dosimetry Applications..........................................................141Michael G. StabinChapter 10 Patient- specific Dosimetry Calculations.............................................................155Manuel Bardies, Naomi Clayton, Gunjan Kayal, and Alex Vergara GilChapter 11 Whole- body Dosimetry..................................................169Jonathan GearChapter 12 Personalized Dosimetry in Radioembolization........................................................................................183Remco Bastiaannet and Hugo W.A.M. de JongChapter 13 Thyroid Imaging and Dosimetry..........................................................................207Michael Lassmann and Heribert HanscheidChapter 14 Bone Marrow Dosimetry........................................................................................223Cecilia HindorfChapter 15 Cellular and Multicellular Dosimetry...................................................................235Roger W. HowellChapter 16 Alpha- particle Dosimetry................................................................267Stig PalmChapter 17 Staff Radiation Protection........................................................................275Lena JonssonChapter 18 IAEA Support to Nuclear Medicine...........................................................293Gian Luca Poli
£166.25
Apress The Essential Guide to Flash Games Building Interactive Entertainment with ActionScript
Book SynopsisThe Essential Guide to Flash Games is a unique tool for Flash game developers.Table of Contents The Second Game Theory Creating an AS3 Game Framework Creating Super Click Laying the Groundwork for Flak Cannon Building the Flak Cannon Game Loop Laying the Groundwork for No Tanks! Creating the Full No Tanks! Game Creating the Color Drop Casual Puzzle Game Creating the Dice Battle Puzzle Game Blit Scrolling in a Tile-Based World Creating an Optimized Post-Retro Game Creating a Viral Game: Tunnel Panic
£42.74
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Beginning 3D Game Development with Unity 4
Book SynopsisBeginning 3D Game Development with Unity 4 is perfect for those who would like to come to grips with programming Unity. You may be an artist who has learned 3D tools such as 3ds Max, Maya, or Cinema 4D, or you may come from 2D tools such as Photoshop and Illustrator. On the other hand, you may just want to familiarize yourself with programming games and the latest ideas in game production. This book introduces key game production concepts in an artist-friendly way, and rapidly teaches the basic scripting skills you''ll need with Unity. It goes on to show how you, as an independent game artist, can create interactive games, ideal in scope for today''s casual and mobile markets, while also giving you a firm foundation in game logic and design. The first part of the book explains the logic involved in game interaction, and soon has you creating game assets through simple examples that you can build upon and gradually expand. In the second part, yoTable of Contents 01. Introduction to Game Development 02. Unity UI basics 03. Introduction to Scripting 04. Terrain Generation and Environment 05. Exploring Navigation 06. Cursor Control and Interaction 07. Importing Assets 08. Action Objects 09. Managing State 10. Exploring Transitions 11. Physics and Special Effects 12. Message Text and HUD 13. Inventory Logic 14. Managing Inventory 15. Dialogue Trees 16. Mecanim 17. Game Environment 18. Setting up the Game 19. Menus and Levels
£67.49
Taylor & Francis Inc Brief Notes in Advanced DSP
Book SynopsisBased on the authors' research in Fourier analysis, Brief Notes in Advanced DSP: Fourier Analysis with MATLAB addresses many concepts and applications of digital signal processing (DSP). The included MATLAB codes illustrate how to apply the ideas in practice.The book begins with the basic concept of the discrete Fourier transformation and its properties. It then describes lifting schemes, integer transformations, the discrete cosine transform, and the paired transform method for calculating the discrete Hadamard transform. The text also examines the decomposition of the 1D signal by so-called section basis signals as well as new forms of 2D signal/image representation and decomposition by direction signals/images. Focusing on Fourier transform wavelets and GivensHaar transforms, the last chapter discusses the problem of signal multiresolution.This book presents numerous interesting problems and concepts of unitary trTable of ContentsDiscrete Fourier Transform. Integer Fourier Transform. Cosine Transform. Hadamard Transform. Paired Transform-Based Decomposition. Fourier Transform and Multiresolution. References. Index.
£128.25
Springer New York The Grammar of Graphics Statistics and Computing
Trade ReviewFrom the reviews of the second edition: "This fascinating book deconstructs the process of producing graphics and in doing so raises many fascinating questions on the nature and representation of information...This second edition is almost twice the size of the original, with six new chapters and substantial revisions." Short Book Reviews of the International Statistical Institute, December 2005 "When the first edidtion of this book appeared in 2000 it was much praised. I called it a tour de force of the highest order. (Wainer, 2001), Edward Wegman (2000) argued that it was destined to become a classic. Now, six years later this very fine book has been much improved." Howard Wainer for Psychometrika "...The second edition is an impressive expansion beyond a quite remarkable first edition. The text remains dense and even more encyclopedic, but it is a pleasure to read, whether a novice or an expert in graphics...this book is a bargain...The second edition is a must-have volume for anyone interested in graphics." Thomas E. Bradstreet for the Journal of the American Statistical Association, December 2006 "I find myself still thinking about the book and its ideas, several weeks after I finished reading it. I love that kind of book." Mark Bailey for Techometrics, Vol. 49, No. 1, February 2007 "Warts and all, The Grammar of Graphics is a richly rewarding work, an outstanding achievement by one of the leaders of statistical graphics. Seek it out." Nicholas J. Cox for the Journal of Statistical Software, January 2007 "The second edition is a quite fascinating book as well, and it comes with many color graphics. Anyone working in this field can see how many hours the author (plus coworkers) has spent on such a volume. … Demands for good graphics are high and this book will help to wetten the appetite to create future computer packages that will meet this demand. An occasional reader will get insights into a modern world of computing … ." (Wolfgang Polasek, Statistical Papers, Vol. 48, 2007)Table of ContentsSyntax.- How To Make a Pie.- Data.- Variables.- Algebra.- Scales.- Statistics.- Geometry.- Coordinates.- Aesthetics.- Facets.- Guides.- Semantics.- Space.- Time.- Uncertainty.- Analysis.- Control.- Automation.- Reader.- Coda.
£127.49
Springer An Introduction to Computer Graphics for Artists
Book SynopsisPreface.- Acknowledgements.- List of Figures.- List of Tables.- Introduction.- CG Basics.- Observation Skills.- Measurements.- Modeling 1: Polygons.- Resolution.- Optimization.- Validation.- Texture Coordinates (UVs).- Shaders and Texturing.- Rendering.- Animation.- Modeling 2: NURBS.- Modeling 3: Advanced.- Industry Standards.- Appendix.- Glossary.- Index.Trade ReviewFrom the reviews of the second edition:“This book presents basic CG topics in a novel, skill-related way that better reflects real-world, entertainment-related industry expectations and standards. … this attractive full-color book is an excellent resource for study topics and curricula. I recommend it to professionals in the entertainment industry … and to any other readers interested in state-of-the-art innovations and improvements and new modeling technologies as applied to artistic design.” (Athanasios D. Styliadis, Computing Reviews, November, 2013)Table of ContentsPreface.- Acknowledgements.- List of Figures.- List of Tables.- Introduction.- CG Basics.- Observation Skills.- Measurements.- Modeling 1: Polygons.- Resolution.- Optimization.- Validation.- Texture Coordinates (UVs).- Shaders and Texturing.- Rendering.- Animation.- Modeling 2: NURBS.- Modeling 3: Advanced.- Industry Standards.- Appendix.- Glossary.- Index.
£62.99
Springer New York OneShot Color Astronomical Imaging
Book SynopsisBecause this book is specifically devoted to one-shot imaging, "One-Shot Color Astronomical Imaging" begins by looking at all the basics - what equipment will be needed, how color imaging is done, and most importantly, what specific steps need to be followed after the one-shot color images are taken.What is one-shot color imaging?Trade ReviewFrom the book reviews:“The book contains lots of good advice and tips for any imager – even if you have a mono CCD camera. This is a great book for imaging as it takes through all the steps required in order to generate pleasing images. One Shot Colour Astronomical Imaging is thoroughly recommended if you want a good overview of how to get into imaging no matter whether you have a mono or colour camera.” (astronomylog.co.uk, July, 2014)“There is a way to save time by using a so-called one-shot color CCD camera … . This book is about using such a camera, the equipment needed, the set-up for imaging, some tips and suggestions … it is an important guide to getting started with this kind of equipment. The book would also be of interest to those astrophotographers who are just getting started in the field … . Very useful and concise, and also inspirational.” (Kadri Tinn, AstroMadness.com, July, 2014)Table of ContentsDigital Imaging.- One-Shot Color vs. Multiple Color Filter Exposures.- Evaluating Your Existing Equipment.- Choosing the Right Stuff.- Finding Targets to Image.- Setting Up Your Imaging Confirmation.- Polar Alignment, Focusing, and Framing.- Calibration.- Taking Exposures and Auto Guiding.- Stack 'Em Up.- Histogram Display.- Image Processing and Enhancement.- Displaying Your Images.- More Things You Can Do.- Additional Resources.
£28.49
Taylor & Francis Inc Visualization Analysis and Design
Book SynopsisTrade Review"Visualization is a young field with only a few primary texts available. What distinguishes this one is the way it synthesizes past work to develop a comprehensive framework for design and analysis. … The author builds a framework for understanding the key elements of visualization and develops a synthesis of current best practices. … The book is filled with examples from the universe of visualization. Virtually all the possibilities for visualization design are illustrated with specific examples. … This is an attractive book, one that’s likely to be a fundamental source for the field. It’s worth a look for anyone with even a passing interest."—MAA Reviews, March 2015"Munzner’s Visualization Analysis and Design is the first comprehensive textbook covering the topic of information visualization. She covers basic theory, careful analysis of the design space, design methodology, and numerous practical examples. Her taxonomic approach is ideally suited for use in the classroom to guide students through the many design decisions associated with creating effective visualizations."—Chris North, Virginia Tech"Visualization Analysis and Design does a fantastic job in providing a framework and language for analyzing, critiquing, and discussing visualizations. This book provides a wonderful foundation for the field."—Miriah Meyer, University of Utah"Visualization Analysis and Design is a book that fills the gaps in the existing literature. It offers a synthesized view and a framework most useful to guide the newcomer to visualization. The nested model that unifies design and evaluation is particularly enlightening. The book is truly valuable for an initial InfoVis course at the graduate level."—Beatriz Sousa Santos, Universidade de Aveiro"Munzner elegantly synthesizes an astounding amount of cutting-edge work on visualization into a clear, engaging, and comprehensive textbook that will prove indispensable to students, designers, and researchers."—Steven Franconeri, Northwestern University"A very detailed and thorough discussion of many topics central to information visualization. A must-read for researchers, sophisticated practitioners, and graduate students."—Jim Foley, Georgia Institute of Technology"Munzner's new book is thorough and beautiful. It belongs on the shelf of anyone whose work and life are touched and enriched by visualization."—Chris Johnson, University of Utah"Visualization is often presented in the classroom as a motley collection of techniques and algorithms designed to unearth insights from data. Tamara Munzner is one of the leading voices of the school that seeks to establish visualization as a discipline resting on well-understood pillars of perception, cognition, and interaction. This new volume from Tamara reflects her expansive views and presents visualization as a wholesome and essential topic for this age of Big Data and all things analytic."—Raghu Machiraju, The Ohio State University"Tamara Munzner is one of the world’s very top researchers in information visualization, and this meticulously crafted volume is probably the most thoughtful and deep synthesis the field has yet seen."—Michael McGuffin, École de Technologie Supérieure"Tamara Munzner shares her deep insights in visualization with us in this excellent textbook, equally useful for students and experts in the field. Highly structured, with many examples that illustrate the underlying framework."—Jarke van Wijk, Eindhoven University of Technology"This book provides the most comprehensive coverage of the fundamentals of visualization design that I have found. It is a much-needed and long-awaited resource for both teachers and practitioners of visualization."—Kwan-Liu Ma, University of California, Davis"Visualization is fundamental to meeting the unprecedented challenges and exploiting the wonderful opportunities of the ever-expanding deluge of data confronting virtually every field. Tamara Munzner’s new book provides a principled treatment of visualization design. This is the visualization textbook I have long awaited. It emphasizes abstraction, design principles, and the importance of evaluation and interactivity."—Jim Hollan, University of California, San Diego"Tamara Munzner is an outstanding scientist, a gifted scholar, and deeply cares about bringing research into practice … She is the driving force behind unifying methodology and terminology in the proliferating field of information visualization. Providing guidance, preventing mistakes, and showing best practices are the ingredients of the secret sauce that make this work so indispensable. The book puts together much of the wisdom she has accumulated over the years in a systematic, well-structured form and shapes the field of visualization in an unprecedented way. It helps to better understand problems, design better systems, avoid common mistakes, and better communicate results. Simply a must-read for everyone concerned with visualization!"—Wolfgang Aigner, St. Pölten University of Applied Sciences"Visualization Analysis and Design gives a framework that formalizes a notion of effective visualization. By connecting design choices with visualization idioms, Munzner provides readers with tools to both articulate the analysis of visualizations and to invent their own."—Joshua A. Levine, Clemson University"Without a doubt, Visualization Analysis and Design is the most comprehensive book on information visualization (InfoVis) to date … Perhaps what is most impressive about this book is its coverage in both the breadth and depth of relevant InfoVis techniques and topics. It will be immediately accessible to both newcomers and veterans of InfoVis who are interested in catching up to the state of the art. The book can be used both as a textbook in a classroom setting or as a reference book in any visualization research group."—Remco Chang, Tufts University"Visualization Analysis and Design is a pleasure to read for the students in my course. Tamara Munzner is striving to make visualization accessible to a very broad audience and is succeeding. This is a textbook we were waiting for."—Torsten Möller, University of Vienna "I like this book because it begins with providing a good way to think about visualization at a high level, and then follows through by providing the building blocks to use in achieving those goals. I think this book will be valuable to both researchers and practitioners because it provides well-grounded foundations as well as a framework to build upon them."—Michael Gleicher, University of Wisconsin–Madison"This highly readable and well-organized book not only covers the fundamentals of visualization design, but also provides a solid framework for analyzing visualizations and visualization problems with concrete examples from the academic community. I am looking forward to teaching from this book and sharing it with my research group."—Michele C. Weigle, Old Dominion University"Dr. Munzner’s work is at the right balance of theory of application—it is not just focused on the why, which is important, but the how. Concrete guidance at every point of the visualization design process is provided, an invaluable resource for instruction and learning."—T.J. Jankun-Kelly, Mississippi State University"Visualization is a young field with only a few primary texts available. What distinguishes this one is the way it synthesizes past work to develop a comprehensive framework for design and analysis. … The author builds a framework for understanding the key elements of visualization and develops a synthesis of current best practices. … The book is filled with examples from the universe of visualization. Virtually all the possibilities for visualization design are illustrated with specific examples. … This is an attractive book, one that’s likely to be a fundamental source for the field. It’s worth a look for anyone with even a passing interest."—MAA Reviews, March 2015"Munzner’s Visualization Analysis and Design is the first comprehensive textbook covering the topic of information visualization. She covers basic theory, careful analysis of the design space, design methodology, and numerous practical examples. Her taxonomic approach is ideally suited for use in the classroom to guide students through the many design decisions associated with creating effective visualizations."—Chris North, Virginia Tech"Visualization Analysis and Design does a fantastic job in providing a framework and language for analyzing, critiquing, and discussing visualizations. This book provides a wonderful foundation for the field."—Miriah Meyer, University of Utah"Visualization Analysis and Design is a book that fills the gaps in the existing literature. It offers a synthesized view and a framework most useful to guide the newcomer to visualization. The nested model that unifies design and evaluation is particularly enlightening. The book is truly valuable for an initial InfoVis course at the graduate level."—Beatriz Sousa Santos, Universidade de Aveiro"Munzner elegantly synthesizes an astounding amount of cutting-edge work on visualization into a clear, engaging, and comprehensive textbook that will prove indispensable to students, designers, and researchers."—Steven Franconeri, Northwestern University"A very detailed and thorough discussion of many topics central to information visualization. A must-read for researchers, sophisticated practitioners, and graduate students."—Jim Foley, Georgia Institute of Technology"Munzner's new book is thorough and beautiful. It belongs on the shelf of anyone whose work and life are touched and enriched by visualization."—Chris Johnson, University of Utah"Visualization is often presented in the classroom as a motley collection of techniques and algorithms designed to unearth insights from data. Tamara Munzner is one of the leading voices of the school that seeks to establish visualization as a discipline resting on well-understood pillars of perception, cognition, and interaction. This new volume from Tamara reflects her expansive views and presents visualization as a wholesome and essential topic for this age of Big Data and all things analytic."—Raghu Machiraju, The Ohio State University"Tamara Munzner is one of the world’s very top researchers in information visualization, and this meticulously crafted volume is probably the most thoughtful and deep synthesis the field has yet seen."—Michael McGuffin, École de Technologie Supérieure"Tamara Munzner shares her deep insights in visualization with us in this excellent textbook, equally useful for students and experts in the field. Highly structured, with many examples that illustrate the underlying framework."—Jarke van Wijk, Eindhoven University of Technology"This book provides the most comprehensive coverage of the fundamentals of visualization design that I have found. It is a much-needed and long-awaited resource for both teachers and practitioners of visualization."—Kwan-Liu Ma, University of California, Davis"Visualization is fundamental to meeting the unprecedented challenges and exploiting the wonderful opportunities of the ever-expanding deluge of data confronting virtually every field. Tamara Munzner’s new book provides a principled treatment of visualization design. This is the visualization textbook I have long awaited. It emphasizes abstraction, design principles, and the importance of evaluation and interactivity."—Jim Hollan, University of California, San Diego"Tamara Munzner is an outstanding scientist, a gifted scholar, and deeply cares about bringing research into practice … She is the driving force behind unifying methodology and terminology in the proliferating field of information visualization. Providing guidance, preventing mistakes, and showing best practices are the ingredients of the secret sauce that make this work so indispensable. The book puts together much of the wisdom she has accumulated over the years in a systematic, well-structured form and shapes the field of visualization in an unprecedented way. It helps to better understand problems, design better systems, avoid common mistakes, and better communicate results. Simply a must-read for everyone concerned with visualization!"—Wolfgang Aigner, St. Pölten University of Applied Sciences"Visualization Analysis and Design gives a framework that formalizes a notion of effective visualization. By connecting design choices with visualization idioms, Munzner provides readers with tools to both articulate the analysis of visualizations and to invent their own."—Joshua A. Levine, Clemson University"Without a doubt, Visualization Analysis and Design is the most comprehensive book on information visualization (InfoVis) to date … Perhaps what is most impressive about this book is its coverage in both the breadth and depth of relevant InfoVis techniques and topics. It will be immediately accessible to both newcomers and veterans of InfoVis who are interested in catching up to the state of the art. The book can be used both as a textbook in a classroom setting or as a reference book in any visualization research group."—Remco Chang, Tufts University"Visualization Analysis and Design is a pleasure to read for the students in my course. Tamara Munzner is striving to make visualization accessible to a very broad audience and is succeeding. This is a textbook we were waiting for."—Torsten Möller, University of Vienna"I like this book because it begins with providing a good way to think about visualization at a high level, and then follows through by providing the building blocks to use in achieving those goals. I think this book will be valuable to both researchers and practitioners because it provides well-grounded foundations as well as a framework to build upon them."—Michael Gleicher, University of Wisconsin–Madison"This highly readable and well-organized book not only covers the fundamentals of visualization design, but also provides a solid framework for analyzing visualizations and visualization problems with concrete examples from the academic community. I am looking forward to teaching from this book and sharing it with my research group."—Michele C. Weigle, Old Dominion University"Dr. Munzner’s work is at the right balance of theory of application—it is not just focused on the why, which is important, but the how. Concrete guidance at every point of the visualization design process is provided, an invaluable resource for instruction and learning."—T.J. Jankun-Kelly, Mississippi State UniversityTable of ContentsWhat's Vis, and Why Do It? What: Data Abstraction. Why: Task Abstraction. Analysis: Four Levels for Validation. Marks and Channels. Rules of Thumb. Arrange Tables. Arrange Spatial Data. Arrange Networks and Trees. Map Color and Other Channels. Manipulate View. Facet into Multiple Views. Reduce Items and Attributes. Embed: Focus+Context. Analysis Case Studies. Bibliography.
£63.64
Taylor & Francis Inc Integrating Scale in Remote Sensing and GIS
Book SynopsisIntegrating Scale in Remote Sensing and GIS serves as the most comprehensive documentation of the scientific and methodological advances that have taken place in integrating scale and remote sensing data. This work addresses the invariants of scale, the ability to change scale, measures of the impact of scale, scale as a parameter in process models, and the implementation of multiscale approaches as methods and techniques for integrating multiple kinds of remote sensing data collected at varying spatial, temporal, and radiometric scales. Researchers, instructors, and students alike will benefit from a guide that has been pragmatically divided into four thematic groups: scale issues and multiple scaling; physical scale as applied to natural resources; urban scale; and human health/social scale. Teeming with insights that elucidate the significance of scale as a foundation for geographic analysis, this book is a vital resource to those seriously involved in the field Trade Review"This book provides a new and comprehensive view of what scale means in today's rapidly advancing world of geographic information technologies. The authors and editors are some of the most reputable figures in the field, and passionate about creating more awareness of the importance of scale, and more knowledge of its properties and impacts. It is a very welcome addition to the literature on the topic, one that should be part of the library of every environmental or social scientist."—Michael F. Goodchild, University of California, Santa Barbara, USA"This book is a superb mix of theory and a wide range of impactful applications, and at the same time integrates this with modern concepts and data sources such as complexity science and crowd-sourcing. I recommend this book to readers who are keen to understand the real world, and to know how to manipulate spatial and space-time data in a principled way."—Peter M. Atkinson, Lancaster University, United Kingdom"The scale is a fundamental concept in geographical analysis, and this book addresse[s] the importance of scale in remote sensing (or broadly GIScience) from different per-spectives. This well-organized book includes four themes (13 Chapters), namely scale/multi-scaling issues, physical scale, human scale, and social scale."—Mingshu Wang, University of Twente, NetherlandsTable of ContentsIntroduction. Fundamentals of Multiscaled Remote Sensing Data for GIS Integration. Scale and Remote Sensing and GIS Integration: A Revisit of the Issues. Remote Sensing: Advances in Sensors and Data. Integration of Multispatial, Multitemporal, and Multispectral Remote Sensing Data in GIS: Progress and Challenges. Theory, Methods, and Techniques for Multiscale Data Integration. Computational and Technological Issues. Implementation of Multiscale Approaches: Methods and Examples. Modeling Methods for GIS Integration of Multiscaled Remote Sensing Data. Multiscaled Data Fusion for GIS Integration. Uncertainty and Error Analysis in Remote Sensing Data Integration with GIS. Geographic Object-Based Image Analysis. Temporal Analysis for Remote Sensing/GIS Integration. Applications of Multiscaled Remote Sensing and GIS. Approaches to Land Use/Land Change with Multiscaled Remote Sensing Data. Multiscaled Remote Sensing Data for Analysis of Landscape Heterogeneity. Environmental Modeling with Multiscaled Data. Use of Hyperspectral Data Remote Sensing Data in GIS. Analysis of Multiscaled Thermal Remote Sensing Data. Multiscaled Remote Sensing Data and GIS for Modeling Land Surface Processes. GIS, Multiscaled Remote Sensing Data for Climate Change Analysis. Integration of GPS, GIS, and Multiscaled Remote Sensing Data. Real Time Data and GIS Integration Applications. Multiscaled Remote Sensing Data, GIS Integration, and the Future. Summary. Epilogue.
£156.75
APress 3D Printing with MatterControl
Book SynopsisIn3D Printing With MatterControl, Joan Horvath and Rich Cameron, the team behindMastering 3D Printing, explain step-by-step how to use the MatterControl program, which allows you to control many common types of 3D printers (including both cartesian and delta style machines).3D Printing With MatterControlcan stand alone, or it can be a companion toMastering 3D Printingto show you how to install, configure, and use best practices with your printer and printing software. The book includes both step by step software walkthroughs and case studies with typical 3D printed objects. Whether you are a "maker" or a teacher of makers,3D Printing with MatterControlwill show you how to get the most out of your printer with the new standard for open source 3D printing software. While there are books available on 3D printers, and even a few on software to make models for printers, there are few good sources covering the software thatactually controls these printers. MatterControl is emerging as the leading open source software for 3D printers, and3DPrinting With MatterControlcovers this new standard in this brief book.Table of ContentsPart 1: The 3D-Printing EcosystemChapter 1. The Desktop 3D PrinterChapter 2. What is MatterControl?Chapter 3. Downloading and Configuring MatterControlPart 2: The 3D-Printing ProcessChapter 4. Making a 3D ModelChapter 5. Slicing a 3D ModelChapter 6. Controlling your 3D PrinterChapter 7. Material ConsiderationsChapter 8. Special CasesPart 3: Your Printer at WorkChapter 9. File and Settings Management and the Touch TabletChapter 10. Case Studies and Classroom TipsChapter 11. MatterControl PluginsChapter 12. Troubleshooting and Post-ProcessingAppendix A. Supported Printer ManufacturersAppendix B. Links
£31.34
APress Practical Machine Learning and Image Processing
Book Synopsis Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You''ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You''ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you''ll explore how models are made in real time and then deployed using various DevOps tools. All the concepTable of ContentsChapter 1: Installation and Environment Setup Chapter Goal: Making System Ready for Image Processing and Analysis No of pages 20 Sub -Topics (Top 2) 1. Installing Jupyter Notebook 2. Installing OpenCV and other Image Analysis dependencies 3. Installing Neural Network Dependencies Chapter 2: Introduction to Python and Image Processing Chapter Goal: Introduction to different concepts of Python and Image processing Application on it. No of pages: 50 Sub - Topics (Top 2) 1. Essentials of Python 2. Terminologies related to Image Analysis Chapter 3: Advanced Image Processing using OpenCV Chapter Goal: Understanding Algorithms and their applications using Python No of pages: 100 Sub - Topics (Top 2): 1. Operations on Images 2. Image Transformations Chapter 4: Machine Learning Approaches in Image Processing Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario No of pages: 100 Sub - Topics (Top 2): 1. Image Classification and Segmentation 2. Applying Supervised and Unsupervised Learning approaches on Images using Python Chapter 5: Real Time Use Cases Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book No of pages: 100 Sub - Topics (Top 5): 1. Facial Detection 2. Facial Recognition 3. Hand Gesture Movement Recognition 4. Self-Driving Cars Conceptualization: Advanced Lane Finding 5. Self-Driving Cars Conceptualization: Traffic Signs Detection Chapter 6: Appendix A Chapter Goal: Advanced concepts Introduction No of pages: 50 Sub - Topics (Top 2): 1. AdaBoost and XGBoost 2. Pulse Coupled Neural Networks
£46.74
O'Reilly Media Machine Learning Pocket Reference
Book SynopsisWith detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.
£19.19
Taylor & Francis Inc The Image Processing Handbook
Book SynopsisConsistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more.Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition: Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more thanTrade Review"With a new co-author (the same Brent Neal who has collaborated with him before in writing the excellent book Measuring Shape), John Russ has again produced a winner—a textbook and reference book that belongs on the shelf, and perhaps on the desk, of anyone involved in digital imaging. Even if you have a copy of one of the previous editions, this is a highly worthwhile addition." —Microscopy and Microanalysis, October 2016 Table of ContentsIntroduction. Acquiring Images. Printing and Storage. Human Vision. Correcting Imaging Defects. Image Enhancement in the Spatial Domain. Processing Images in Frequency Space. Segmentation and Thresholding. Processing Binary Images. Image Measurements. Feature Measurements. Characterizing Shape. Correlation, Classification, Identification, and Matching. 3D Imaging. 3D Processing and Measurement. Imaging Surfaces. References.
£175.75
IGI Global 3D Modeling and Animation: Synthesis and Analysis Techniques for the Human Body
Book Synopsis3D Modeling and Animation: Synthesis and Analysis Techniques for the Human Body aims to cover the areas of modeling and animating 3D synthetic human models at a level that is useful to students, researchers, software developers and content generators. This book provides a reference for the state-of-the-art methods, delivered by the leading researchers in this area, who are invited to contribute to the appropriate chapters according to their expertise. The reader will be presented with the latest, research-level, techniques for the analysis and synthesis of still and moving human bodies, with particular emphasis in facial and gesture characteristics.Table of ContentsIntroduction: Advances in Vision-Based Human Body Modeling, Virtual Character Definition and Animation within the MPEG-4 Standard, A Survey of Passive Camera Calibration, Real-time Analysis of Human Body Parts, and Gesture-Activity Recognition in 3D, Facial Expression and Gesture Analysis for Emotionally-rich Man-machine Interaction, Techniques for Face Motion & Expression Analysis on Monocular Images, Analysis and Synthesis of Facial Expressions, Modeling and Synthesis of Realistic Visual Speech in 3D, Automatic 3-D Face Model Adaptation with two Comp exity Modes for Visual Communication, Learning 3D Face Animation Model: Methods and Applications, Synthesis and Analysis Techniques for the Human Body: R&D Projects
£72.00
Nova Science Publishers Inc Image Fusion: Principles, Technology &
Book Synopsis
£127.99
Apple Academic Press Inc. Intelligent Systems: Advances in Biometric
Book SynopsisThis volume helps to fill the gap between data analytics, image processing, and soft computing practices. Soft computing methods are used to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality research that presents the current trends, advanced methods, and hybridized techniques relating to data analytics and intelligent systems. The book also features case studies related to medical diagnosis with the use of image processing and soft computing algorithms in particular models. Providing extensive coverage of biometric systems, soft computing, image processing, artificial intelligence, and data analytics, the chapter authors discuss the latest research issues, present solutions to research problems, and look at comparative analysis with earlier results. Topics include some of the most important challenges and discoveries in intelligent systems today, such as computer vision concepts and image identification, data analysis and computational paradigms, deep learning techniques, face and speaker recognition systems, and more.Table of ContentsPart 1: Biometric Systems And Image Processing 1. Intelligent Techniques: An Overview 2. A Survey on Artificial Intelligence Techniques Used in Bio-Metric Systems 3. Speech-Based Biometric Using Odia Phonetics 4. Deep Learning Techniques to Classify and Analyze Medical Imaging Data 5. Face Recognition System: An Overview 6. An Overview on the Concept of Speaker Recognition 7. Analysis of a Unimodal and Multimodal Biometric System Part 2: Soft Computing And Data Analytics 8. A Heuristic Approach of Parameter Tuning in a Smote-Based Preprocessing Algorithm for Imbalanced Ordinal Classification 9. Aspects of Deep Learning: Hyper-Parameter Tuning, Regularization, and Normalization 10. Super-Resolution of Reconstruction of Infrared Images Adopting Counter Neural Networks 11. High-End Tools and Technologies for Managing Data in the Age of Big Data 12. An AI-Based Chatbot Using Deep Learning Part 3: Intelligent Systems And Hybrid Systems 13. A Real-Time Data Analytics-Based Crop Diseases Recognition System 14. Image Caption Generation with Beam Search
£117.90
ISTE Ltd Change Detection and Image Time-Series Analysis
Book SynopsisChange Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.Table of ContentsContents Preface xi Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1 Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU and Xiaohua TONG 1.1. Introduction 1 1.2. Unsupervised change detection in multispectral images 3 1.2.1.Relatedconcepts 3 1.2.2.Openissuesandchallenges 7 1.2.3. Spectral–spatial unsupervised CD techniques 7 1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9 1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9 1.3.2. Multiscale morphological compressed change vector analysis 11 1.3.3. Superpixel-level compressed change vector analysis 15 1.4.Datasetdescriptionandexperimentalsetup 18 1.4.1.Datasetdescription 18 1.4.2.Experimentalsetup 22 1.5.Resultsanddiscussion 24 1.5.1.ResultsontheXuzhoudataset 24 1.5.2. Results on the Indonesia tsunami dataset 24 xv 1.6.Conclusion 28 1.7.Acknowledgements 29 1.8.References 29 Chapter 2 Change Detection in Time Series of Polarimetric SAR Images 35 Knut CONRADSEN, Henning SKRIVER, MortonJ.CANTY andAllanA.NIELSEN 2.1. Introduction 35 2.1.1.Theproblem 36 2.1.2 Important concepts illustrated by means of the gamma distribution 39 2.2.Testtheoryandmatrixordering 45 2.2.1. Test for equality of two complex Wishart distributions 45 2.2.2. Test for equality of k-complex Wishart distributions 47 2.2.3. The block diagonal case 49 2.2.4.TheLoewnerorder 52 2.3.Thebasicchangedetectionalgorithm 53 2.4.Applications 55 2.4.1.Visualizingchanges 58 2.4.2.Fieldwisechangedetection 59 2.4.3. Directional changes using the Loewner ordering 62 2.4.4. Software availability 65 2.5.References 70 Chapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73 Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ, Arnaud BRELOY and Frédéric PASCAL 3.1. Introduction 73 3.2.Datasetdescription 76 3.3.StatisticalmodelingofSARimages 77 3.3.1.Thedata 77 3.3.2.Gaussianmodel 77 3.3.3.Non-Gaussianmodeling 83 3.4.Dissimilaritymeasures 84 3.4.1.Problemformulation 84 3.4.2. Hypothesis testing statistics 85 3.4.3.Information-theoreticmeasures 87 3.4.4.Riemanniangeometrydistances 89 3.4.5.Optimaltransport 90 3.4.6.Summary 91 3.4.7. Results of change detectors on the UAVSAR dataset 91 3.5. Change detection based on structured covariances 94 3.5.1. Low-rank Gaussian change detector 96 3.5.2. Low-rank compound Gaussian change detector 97 3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100 3.6.Conclusion 102 3.7.References 103 Chapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109 Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN and Lionel BOMBRUN 4.1. Introduction 109 4.2.Parametricmodelingofconvnetfeatures 110 4.3.Anomalydetectioninimagetimeseries 113 4.4.Functionalimagetimeseriesclustering 119 4.5.Conclusion 123 4.6.References 123 Chapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127 Fatima KARBOU, Guillaume JAMES, Philippe DURAND and Abdourrahmane M. ATTO 5.1. Introduction 127 5.2.Testareaanddata 129 5.3.WetsnowdetectionusingSentinel-1 129 5.4.Metricstodetectwetsnow 133 5.5.Discussion 138 5.6.Conclusion 143 5.7.Acknowledgements 143 5.8.References 143 Chapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145 Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC and Pedro MORETTIN 6.1. Introduction 145 6.2. Random field model of a cyclone texture 148 6.2.1.Cyclonetexturefeature 149 6.2.2. Wavelet-based power spectral densities and cyclone fields 150 6.2.3. Fractional spectral power decay model 153 6.3.Cyclonefieldeyedetectionandtracking 157 6.3.1.Cycloneeyedetection 157 6.3.2.Dynamicfractalfieldeyetracking 158 6.4. Cyclone field intensity evolution prediction 159 6.5.Discussion 161 6.6.Acknowledgements 163 6.7.References 163 Chapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167 Minh-Tan PHAM and Grégoire MERCIER 7.1. Introduction 167 7.2. Texture representation and characterization using local extrema 169 7.2.1.Motivationandapproach 169 7.2.2. Local extrema keypoints within SAR images 172 7.3.Unsupervisedchangedetection 175 7.3.1. Proposed framework 175 7.3.2. Weighted graph construction from keypoints 176 7.3.3.Changemeasure(CM)generation 178 7.4.Experimentalstudy 179 7.4.1. Data description and evaluation criteria 179 7.4.2.Changedetectionresults 181 7.4.3.Sensitivitytoparameters 185 7.4.4.ComparisonwiththeNLMmodel 188 7.4.5. Analysis of the algorithm complexity 191 7.5.Applicationtoglacierflowmeasurement 192 7.5.1. Proposed method 193 7.5.2.Results 194 7.6.Conclusion 196 7.7.References 197 Chapter 8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201 Andrea GARZELLI and Claudia ZOPPETTI 8.1. Introduction 201 8.2. Proposed method 203 8.2.1.Testsiteanddata 206 8.3.SARprocessing 209 8.4.Opticalprocessing 215 8.5.Combinationlayer 217 8.6.Results 219 8.7.Conclusion 220 8.8.References 221 Chapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223 Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE 9.1. Introduction 223 9.2. Overview of the change detection problem 225 9.2.1. Change detection methods for multispectral images 227 9.2.2. Challenges addressed in this chapter 230 9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231 9.3.1. Magnitude image statistical mixture model 231 9.3.2.Bayesiandecision 233 9.3.3. Numerical approach to parameter estimation 234 9.4. A compound multiclass statistical model of the difference image 239 9.4.1. Difference image statistical mixture model 240 9.4.2. Magnitude image statistical mixture model 245 9.4.3.Bayesiandecision 248 9.4.4. Numerical approach to parameter estimation 249 9.5.Experimentalresults 253 9.5.1.Datasetdescription 253 9.5.2.Experimentalsetup 256 9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256 9.5.4. Test 2: Multiclass Rician mixture model 260 9.6.Conclusion 266 9.7.References 267 List of Authors 275 Index 277 Summary of Volume 2 281
£124.15
ISTE Ltd Change Detection and Image Time Series Analysis
Book SynopsisChange Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253
£124.15
Springer London Ltd Principles of Digital Image Processing:
Book SynopsisThis book provides a modern, algorithmic introduction to digital image p- cessing, designed to be used both by learners desiring a ?rm foundation on which to build and practitioners in search of critical analysis and modern - plementations of the most important techniques. This updated and enhanced paperbackedition ofourcomprehensivetextbookDigital Image Processing: An Algorithmic Approach Using Java packages the original material into a series of compactvolumes, therebysupporting a ?exiblesequenceofcoursesindigital image processing. Tailoring the contents to the scope of individual semester courses is also an attempt to provide a?ordable (and "backpack-compatible") textbooks without comprimising the quality and depth of content. Oneapproachtolearninganewlanguageistobecomeconversantinthecore vocabulary and to start using it right away. At ?rst, you may only know how to ask for directions, order co?ee, and so on, but once you become con?dent with the core, you will start engaging others in "conversations" and rapidly learn how to get things done. This step-by-step approach works equally well in many areas of science and engineering. In this ?rst volume, ostentatiously titled Fundamental Techniques,wehave attemptedtocompilethecore"vocabulary" ofdigitalimageprocessing,starting from the basic concepts and elementary properties of digital images through simple statistics and point operations, fundamental ? ltering techniques, loc- ization of edges and contours, and basic operations on color images. Mastering these most commonly used techniques and algorithms will enable you to start being productive right away.Trade ReviewFrom the reviews: "This slim volume is the first of a three-volume set. … the book’s overall coverage is sound--well written, with plenty of illustrative examples and elegant diagrams. … this book is a fine introduction to image processing, and some topics--color, in particular--are very well done indeed. … In summary, this is mostly a fine text. … with the addition of more exercises, this would be an excellent introduction to the field." (Alasdair McAndrew, ACM Computing Reviews, August, 2009)Table of ContentsDigital Images.- ImageJ.- Histograms.- Point Operations.- Filters.- Edges and Contours.- Morphological Filters.- Color Images.
£29.69
ISTE Ltd and John Wiley & Sons Inc Image Processing
Book SynopsisComputer-aided automatic processing of images requires the control of a series of operations, which this book analyzes. Knowing the statistical properties of images, sampling them to reduce the observable world to a series of discrete values, restoring images in order to correct degradations – all these operations are explained here, together with the mathematical tools they require. Topics covered include fractal representation, mathematical morphology, wavelet representations and the detection and description of contours and shapes.Table of ContentsChapter 1. Statistical properties of images (Henri Maître). Chapter 2. Image sampling and fractal representation (Henri Maître). Chapter 3. Discrete representations (Isabelle Bloch). Chapter 4. Restoration of images (Henri Maître). Chapter 5. Mathematical morphology (Isabelle Bloch). Chapter 6. Markov fields (Florence Tupin and Marc Sigelle). Chapter 7. Wavelets and image processing (Béatrice Pesquet-Popescu and Jean-Christophe Pesquet). Chapter 8. Partial differential equations (Yann Gousseau). Chapter 9. Preprocessing (Henri Maître). Chapter 10. Detection of contours in images (Henri Maître). Chapter 11. Segmentation by regions (Henri Maître). Chapter 12. Textures (Henri Maître). Chapter 13. Description of contours and shapes (Henri Maître). List of Authors. Index.
£163.35
ISTE Ltd and John Wiley & Sons Inc Compression of Biomedical Images and Signals
Book SynopsisDuring the last decade, image and signal compression for storage and transmission purpose has seen a great expansion. But what about medical data compression? Should a medical image or a physiological signal be processed and compressed like any other data? The progress made in imaging systems, storing systems and telemedicine makes compression in this field particularly interesting. However, this compression has to be adapted to the specificities of biomedical data which contain diagnosis information. As such, this book offers an overview of compression techniques applied to medical data, including: physiological signals, MRI, X-ray, ultrasound images, static and dynamic volumetric images. Researchers, clinicians, engineers and professionals in this area, along with postgraduate students in the signal and image processing field, will find this book to be of great interest.Table of ContentsPreface xiii Chapter 1. Relevance of Biomedical Data Compression 1 Jean-Yves TANGUY, Pierre JALLET, Christel LE BOZEC and Guy FRIJA 1.1. Introduction 1 1.2. The management of digital data using PACS 2 1.2.1. Usefulness of PACS 2 1.2.2. The limitations of installing a PACS 3 1.3. The increasing quantities of digital data 4 1.3.1. An example from radiology 4 1.3.2. An example from anatomic pathology 6 1.3.3. An example from cardiology with ECG 7 1.3.4. Increases in the number of explorative examinations 8 1.4. Legal and practical matters 8 1.5. The role of data compression. 9 1.6. Diagnostic quality 10 1.6.1. Evaluation 10 1.6.2. Reticence 11 1.7. Conclusion 12 1.8. Bibliography 12 Chapter 2. State of the Art of Compression Methods 15 Atilla BASKURT 2.1. Introduction 15 2.2. Outline of a generic compression technique 16 2.2.1. Reducing redundancy 17 2.2.2. Quantizing the decorrelated information 18 2.2.3. Coding the quantized values 18 2.2.4. Compression ratio, quality evaluation 20 2.3. Compression of still images 21 2.3.1. JPEG standard 22 2.3.1.1. Why use DCT? 22 2.3.1.2. Quantization 24 2.3.1.3. Coding 24 2.3.1.4. Compression of still color images with JPEG 25 2.3.1.5. JPEG standard: conclusion 26 2.3.2. JPEG 2000 standard 27 2.3.2.1. Wavelet transform 27 2.3.2.2. Decomposition of images with the wavelet transform 27 2.3.2.3. Quantization and coding of subbands 29 2.3.2.4. Wavelet-based compression methods, serving as references 30 2.3.2.5. JPEG 2000 standard 31 2.4. The compression of image sequences 33 2.4.1. DCT-based video compression scheme 34 2.4.2. A history of and comparison between video standards 36 2.4.3. Recent developments in video compression 38 2.5. Compressing 1D signals 38 2.6. The compression of 3D objects 39 2.7. Conclusion and future developments 39 2.8. Bibliography 40 Chapter 3. Specificities of Physiological Signals and Medical Images 43 Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Jean-Yves TANGUY, Elsa ANGELINI, Christel LE BOZEC and Jean-Jacques LE JEUNE 3.1. Introduction 43 3.2. Characteristics of physiological signals 44 3.2.1. Main physiological signals 44 3.2.1.1. Electroencephalogram (EEG) 44 3.2.1.2. Evoked potential (EP) 45 3.2.1.3. Electromyogram (EMG) 45 3.2.1.4. Electrocardiogram (ECG) 46 3.2.2. Physiological signal acquisition 46 3.2.3. Properties of physiological signals 46 3.2.3.1. Properties of EEG signals 46 3.2.3.2. Properties of ECG signals 48 3.3. Specificities of medical images 50 3.3.1. The different features of medical imaging formation processes 50 3.3.1.1. Radiology 51 3.3.1.2. Magnetic resonance imaging (MRI) 54 3.3.1.3. Ultrasound 58 3.3.1.4. Nuclear medicine 62 3.3.1.5. Anatomopathological imaging 66 3.3.1.6. Conclusion 68 3.3.2. Properties of medical images 69 3.3.2.1. The size of images 70 3.3.2.2. Spatial and temporal resolution 71 3.3.2.3. Noise in medical images 72 3.4. Conclusion 73 3.5. Bibliography 74 Chapter 4. Standards in Medical Image Compression 77 Bernard GIBAUD and Joël CHABRIAIS 4.1. Introduction 77 4.2. Standards for communicating medical data 79 4.2.1. Who creates the standards, and how? 79 4.2.2. Standards in the healthcare sector 80 4.2.2.1. Technical committee 251 of CEN 80 4.2.2.2. Technical committee 215 of the ISO 80 4.2.2.3. DICOM Committee 80 4.2.2.4.Health Level Seven (HL7) 85 4.2.2.5. Synergy between the standards bodies 86 4.3. Existing standards for image compression 87 4.3.1. Image compression 87 4.3.2. Image compression in the DICOM standard 89 4.3.2.1. The coding of compressed images in DICOM 89 4.3.2.2. The types of compression available 92 4.3.2.3. Modes of access to compressed data 95 4.4. Conclusion 99 4.5. Bibliography 99 Chapter 5. Quality Assessment of Lossy Compressed Medical Images 101 Christine CAVARO-MÉNARD, Patrick LE CALLET, Dominique BARBA and Jean-Yves TANGUY 5.1. Introduction 101 5.2. Degradations generated by compression norms and their consequences in medical imaging 102 5.2.1. The block effect 102 5.2.2. Fading contrast in high spatial frequencies 103 5.3. Subjective quality assessment 105 5.3.1. Protocol evaluation 105 5.3.2. Analyzing the diagnosis reliability 106 5.3.2.1. ROC analysis 108 5.3.2.2. Analyses that are not based on the ROC method 111 5.3.3. Analyzing the quality of diagnostic criteria 111 5.3.4. Conclusion 114 5.4. Objective quality assessment 114 5.4.1. Simple signal-based metrics 115 5.4.2. Metrics based on texture analysis 115 5.4.3. Metrics based on a model version of the HVS 117 5.4.3.1. Luminance adaptation 117 5.4.3.2. Contrast sensivity 118 5.4.3.3. Spatio-frequency decomposition 118 5.4.3.4. Masking effect 119 5.4.3.5. Visual distortion measures 120 5.4.4. Analysis of the modification of quantitative clinical parameters 123 5.5. Conclusion 125 5.6. Bibliography 125 Chapter 6. Compression of Physiological Signals 129 Amine NAÏT-ALI 6.1. Introduction 129 6.2. Standards for coding physiological signals 130 6.2.1. CEN/ENV 1064 Norm 130 6.2.2. ASTM 1467 Norm 130 6.2.3. EDF norm 130 6.2.4. Other norms 131 6.3. EEG compression 131 6.3.1. Time-domain EEG compression 131 6.3.2. Frequency-domain EEG compression 132 6.3.3. Time-frequency EEG compression 132 6.3.4. Spatio-temporal compression of the EEG 132 6.3.5. Compression of the EEG by parameter extraction 132 6.4. ECG compression 133 6.4.1. State of the art 133 6.4.2. Evaluation of the performances of ECG compression methods 134 6.4.3. ECG pre-processing 135 6.4.4. ECG compression for real-time transmission 136 6.4.4.1. Time domain ECG compression 136 6.4.4.2. Compression of the ECG in the frequency domain 141 6.4.5. ECG compression for storage 144 6.4.5.1. Synchronization and polynomial modeling 145 6.4.5.2. Synchronization and interleaving 149 6.4.5.3. Compression of the ECG signal using the JPEG 2000 standard 150 6.5. Conclusion 150 6.6. Bibliography 151 Chapter 7. Compression of 2D Biomedical Images 155 Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Olivier DEFORGES and Marie BABEL 7.1. Introduction 155 7.2. Reversible compression of medical images 156 7.2.1. Lossless compression by standard methods 156 7.2.2. Specific methods of lossless compression 157 7.2.3. Compression based on the region of interest 158 7.2.4. Conclusion 160 7.3. Lossy compression of medical images 160 7.3.1. Quantization of medical images 160 7.3.1.1. Principles of vector quantization 161 7.3.1.2. A few illustrations 161 7.3.1.3. Balanced tree-structured vector quantization 163 7.3.1.4. Pruned tree-structured vector quantization 163 7.3.1.5. Other vector quantization methods applied to medical images 163 7.3.2. DCT-based compression of medical images 164 7.3.3. JPEG 2000 lossy compression of medical images 167 7.3.3.1. Optimizing the JPEG 2000 parameters for the compression of medical images 167 7.3.4. Fractal compression 170 7.3.5. Some specific compression methods 171 7.3.5.1. Compression of mammography images 171 7.3.5.2. Compression of ultrasound images 172 7.4. Progressive compression of medical images 173 7.4.1. State-of-the-art progressive medical image compression techniques 173 7.4.2. LAR progressive compression of medical images 174 7.4.2.1. Characteristics of the LAR encoding method 174 7.4.2.2. Progressive LAR encoding 176 7.4.2.3. Hierarchical region encoding 178 7.5. Conclusion 181 7.6. Bibliography 182 Chapter 8. Compression of Dynamic and Volumetric Medical Sequences 187 Azza OULED ZAID, Christian OLIVIER and Amine NAÏT-ALI 8.1. Introduction 187 8.2. Reversible compression of (2D+t) and 3D medical data sets 190 8.3. Irreversible compression of (2D+t) medical sequences 192 8.3.1. Intra-frame lossy coding 192 8.3.2. Inter-frame lossy coding 194 8.3.2.1. Conventional video coding techniques 194 8.3.2.2. Modified video coders 195 8.3.2.3. 2D+t wavelet-based coding systems limits 195 8.4. Irreversible compression of volumetric medical data sets 196 8.4.1. Wavelet-based intra coding 196 8.4.2. Extension of 2D transform-based coders to 3D data 197 8.4.2.1. 3D DCT coding 197 8.4.2.2. 3D wavelet-based coding based on scalar or vector quantization 198 8.4.2.3. Embedded 3D wavelet-based coding 199 8.4.2.4. Object-based 3D embedded coding 204 8.4.2.5. Performance assessment of 3D embedded coders 205 8.5. Conclusion 207 8.6. Bibliography 208 Chapter 9. Compression of Static and Dynamic 3D Surface Meshes 211 Khaled MAMOU, Françoise PRÊTEUX, Rémy PROST and Sébastien VALETTE 9.1. Introduction 211 9.2. Definitions and properties of triangular meshes 213 9.3. Compression of static meshes 216 9.3.1. Single resolution mesh compression 217 9.3.1.1. Connectivity coding 217 9.3.1.2. Geometry coding 218 9.3.2. Multi-resolution compression 219 9.3.2.1. Mesh simplification methods 219 9.3.2.2. Spectral methods 219 9.3.2.3. Wavelet-based approaches 220 9.4. Compression of dynamic meshes 229 9.4.1. State of the art 230 9.4.1.1. Prediction-based techniques 230 9.4.1.2. Wavelet-based techniques 231 9.4.1.3. Clustering-based techniques 233 9.4.1.4. PCA-based techniques 234 9.4.1.5. Discussion 234 9.4.2. Application to dynamic 3D pulmonary data in computed tomography 236 9.4.2.1. Data 236 9.4.2.2. Proposed approach 237 9.4.2.3. Results 238 9.5. Conclusion 239 9.6. Appendices 240 9.6.1. Appendix A: mesh via the MC algorithm 240 9.7. Bibliography 241 Chapter 10. Hybrid Coding: Encryption-Watermarking-Compression for Medical Information Security 247 William PUECH and Gouenou COATRIEUX 10.1. Introduction 247 10.2. Protection of medical imagery and data 248 10.2.1. Legislation and patient rights 248 10.2.2. A wide range of protection measures 249 10.3. Basics of encryption algorithms 251 10.3.1. Encryption algorithm classification 251 10.3.2. The DES encryption algorithm 252 10.3.3. The AES encryption algorithm 253 10.3.4. Asymmetric block system: RSA 254 10.3.5. Algorithms for stream ciphering 255 10.4. Medical image encryption 257 10.4.1. Image block encryption 258 10.4.2. Coding images by asynchronous stream cipher 258 10.4.3. Applying encryption to medical images 259 10.4.4. Selective encryption of medical images 261 10.5. Medical image watermarking and encryption 265 10.5.1. Image watermarking and health uses 265 10.5.2. Watermarking techniques and medical imagery 266 10.5.2.1. Characteristics. 266 10.5.2.2. The methods 267 10.5.3. Confidentiality and integrity of medical images by data encryption and data hiding 269 10.6. Conclusion. 272 10.7. Bibliography 273 Chapter 11. Transmission of Compressed Medical Data on Fixed and Mobile Networks 277 Christian OLIVIER, Benoît PARREIN and Rodolphe VAUZELLE 11.1. Introduction 277 11.2. Brief overview of the existing applications 278 11.3. The fixed and mobile networks 279 11.3.1. The network principles 279 11.3.1.1. Presentation, definitions and characteristics 279 11.3.1.2. The different structures and protocols 281 11.3.1.3. Improving the Quality of Service 281 11.3.2. Wireless communication systems 282 11.3.2.1. Presentation of these systems 282 11.3.2.2. Wireless specificities 284 11.4. Transmission of medical images 287 11.4.1. Contexts 287 11.4.1.1. Transmission inside a hospital 287 11.4.1.2. Transmission outside hospital on fixed networks 287 11.4.1.3. Transmission outside hospital on mobile networks 288 11.4.2. Encountered problems 288 11.4.2.1. Inside fixed networks 288 11.4.2.2. Inside mobile networks 289 11.4.3. Presentation of some solutions and directions 293 11.4.3.1. Use of error correcting codes 294 11.4.3.2. Unequal protection using the Mojette transform 297 11.5. Conclusion 299 11.6. Bibliography 300 Conclusion 303 List of Authors 305 Index 309
£150.05
ISTE Ltd and John Wiley & Sons Inc Bayesian Approach to Inverse Problems
Book SynopsisMany scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems. The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three chapters bring the theoretical notions that make it possible to cast inverse problems within a mathematical framework. The next three chapters address the fundamental inverse problem of deconvolution in a comprehensive manner. Chapters 7 and 8 deal with advanced statistical questions linked to image estimation. In the last five chapters, the main tools introduced in the previous chapters are put into a practical context in important applicative areas, such as astronomy or medical imaging.Table of ContentsIntroduction 15 Jérôme IDIER PART I. FUNDAMENTAL PROBLEMS AND TOOLS 23 Chapter 1. Inverse Problems, Ill-posed Problems 25 Guy DEMOMENT, Jérôme IDIER 1.1. Introduction 25 1.2. Basic example 26 1.3. Ill-posed problem 30 1.3.1. Case of discrete data 31 1.3.2. Continuous case 32 1.4. Generalized inversion 34 1.4.1. Pseudo-solutions 35 1.4.2. Generalized solutions 35 1.4.3. Example 35 1.5. Discretization and conditioning 36 1.6. Conclusion 38 1.7. Bibliography 39 Chapter 2. Main Approaches to the Regularization of Ill-posed Problems 41 Guy DEMOMENT, Jérôme IDIER 2.1. Regularization 41 2.1.1. Dimensionality control 42 2.1.2. Minimization of a composite criterion 44 2.2. Criterion descent methods 48 2.2.1.Criterion minimization for inversion 48 2.2.2. The quadratic case 49 2.2.3. The convex case 51 2.2.4. General case 52 2.3. Choice of regularization coefficient 53 2.3.1. Residual error energy control 53 2.3.2. “L-curve” method 53 2.3.3. Cross-validation 54 2.4. Bibliography 56 Chapter 3. Inversion within the Probabilistic Framework 59 Guy DEMOMENT, Yves GOUSSARD 3.1. Inversion and inference 59 3.2. Statistical inference 60 3.2.1. Noise law and direct distribution for data 61 3.2.2. Maximum likelihood estimation 63 3.3. Bayesian approach to inversion 64 3.4. Links with deterministic methods 66 3.5. Choice of hyperparameters 67 3.6. A priori model68 3.7. Choice of criteria 70 3.8. The linear, Gaussian case 71 3.8.1. Statistical properties of the solution 71 3.8.2. Calculation of marginal likelihood 73 3.8.3. Wiener filtering 74 3.9. Bibliography 76 PART II. DECONVOLUTION 79 Chapter 4. Inverse Filtering and Other Linear Methods 81 Guy LE BESNERAIS, Jean-François GIOVANNELLI, Guy DEMOMENT 4.1. Introduction 81 4.2. Continuous-time deconvolution 82 4.2.1. Inverse filtering 82 4.2.2. Wiener filtering 84 4.3. Discretization of the problem 85 4.3.1. Choice of a quadrature method 85 4.3.2. Structure of observation matrix H 87 4.3.3. Usual boundary conditions 89 4.3.4. Problem conditioning 89 4.3.5.Generalized inversion 91 4.4. Batch deconvolution 92 4.4.1. Preliminary choices 92 4.4.2. Matrix form of the estimate 93 4.4.3. Hunt’s method (periodic boundary hypothesis) 94 4.4.4. Exact inversion methods in the stationary case 96 4.4.5. Case of non-stationary signals 98 4.4.6. Results and discussion on examples 98 4.5. Recursive deconvolution 102 4.5.1. Kalman filtering 102 4.5.2. Degenerate state model and recursive least squares 104 4.5.3. Autoregressive state model 105 4.5.4. Fast Kalman filtering 108 4.5.5. Asymptotic techniques in the stationary case 110 4.5.6. ARMA model and non-standard Kalman filtering 111 4.5.7. Case of non-stationary signals 111 4.5.8. On-lineprocessing: 2Dcase 112 4.6. Conclusion 112 4.7. Bibliography 113 Chapter 5. Deconvolution of Spike Trains 117 Frédéric CHAMPAGNAT, Yves GOUSSARD, Stéphane GAUTIER, Jérôme IDIER 5.1. Introduction 117 5.2. Penalization of reflectivities, L2LP/L2Hy deconvolutions 119 5.2.1. Quadratic regularization 121 5.2.2. Non-quadratic regularization 122 5.2.3. L2LPorL2Hy deconvolution 123 5.3. Bernoulli-Gaussian deconvolution 124 5.3.1. Compound BG model 124 5.3.2. Various strategies for estimation 124 5.3.3. General expression for marginal likelihood 125 5.3.4. An iterative method for BG deconvolution 126 5.3.5. Other methods 128 5.4. Examples of processing and discussion 130 5.4.1. Nature of the solutions 130 5.4.2. Setting the parameters 132 5.4.3. Numerical complexity 133 5.5. Extensions 133 5.5.1. Generalization of structures of R and H 134 5.5.2. Estimation of the impulse response . . . 134 5.6. Conclusion 136 5.7. Bibliography 137 Chapter 6. Deconvolution of Images 141 Jérôme IDIER, Laure BLANC-FÉRAUD 6.1. Introduction 141 6.2. Regularization in the Tikhonov sense 142 6.2.1. Principle 142 6.2.2. Connection with image processing by linear PDE 144 6.2.3. Limits of Tikhonov’s approach 145 6.3. Detection-estimation 148 6.3.1. Principle 148 6.3.2. Disadvantages 149 6.4. Non-quadratic approach 150 6.4.1. Detection-estimation and non-convex penalization 154 6.4.2. Anisotropic diffusion by PDE 155 6.5. Half-quadratic augmented criteria 156 6.5.1. Duality between non-quadratic criteria and HQ criteria 157 6.5.2. Minimization of HQ criteria 158 6.6. Application in image deconvolution 159 6.6.1. Calculation of the solution 159 6.6.2. Example 161 6.7. Conclusion 164 6.8. Bibliography 165 PART III. ADVANCED PROBLEMS AND TOOLS 169 Chapter 7. Gibbs-Markov Image Models 171 Jérôme IDIER 7.1. Introduction 171 7.2. Bayesian statistical framework 172 7.3. Gibbs-Markov fields 173 7.3.1. Gibbs fields 174 7.3.2. Gibbs-Markov equivalence 177 7.3.3. Posterior law of a GMRF 180 7.3.4. Gibbs-Markov models for images 181 7.4. Statistical tools, stochastic sampling 185 7.4.1. Statistical tools 185 7.4.2. Stochastic sampling 188 7.5. Conclusion 194 7.6. Bibliography 195 Chapter 8. Unsupervised Problems 197 Xavier DESCOMBES, Yves GOUSSARD 8.1. Introduction and statement of problem 197 8.2. Directly observed field 199 8.2.1. Likelihood properties 199 8.2.2. Optimization 200 8.2.3. Approximations 202 8.3. Indirectly observed field 205 8.3.1. Statement of problem 205 8.3.2. EM algorithm 206 8.3.3. Application to estimation of the parameters of a GMRF 207 8.3.4. EM algorithm and gradient 208 8.3.5. Linear GMRF relative to hyperparameters 210 8.3.6. Extensions and approximations 212 8.4. Conclusion 215 8.5. Bibliography 216 PART IV. SOME APPLICATIONS 219 Chapter 9. Deconvolution Applied to Ultrasonic Non-destructive Evaluation 221 Stéphane GAUTIER, Frédéric CHAMPAGNAT, Jérôme IDIER 9.1. Introduction 221 9.2. Example of evaluation and difficulties of interpretation 222 9.2.1. Description of the part to be inspected 222 9.2.2. Evaluation principle 222 9.2.3. Evaluation results and interpretation 223 9.2.4. Help with interpretation by restoration of discontinuities 224 9.3. Definition of direct convolution model 225 9.4. Blind deconvolution 226 9.4.1. Overview of approaches for blind deconvolution 226 9.4.2. DL2Hy/DBGd econvolution 230 9.4.3. Blind DL2Hy/DBG deconvolution 232 9.5. Processing real data 232 9.5.1. Processing by blind deconvolution 233 9.5.2. Deconvolution with a measured wave 234 9.5.3. Comparison between DL2Hy and DBG 237 9.5.4. Summary 240 9.6. Conclusion 240 9.7. Bibliography 241 Chapter 10. Inversion in Optical Imaging through Atmospheric Turbulence 243 Laurent MUGNIER, Guy LE BESNERAIS, Serge MEIMON 10.1. Optical imaging through turbulence 243 10.1.1. Introduction 243 10.1.2. Image formation 244 10.1.4. Imaging techniques 249 10.2. Inversion approach and regularization criteria used 253 10.3. Measurement of aberrations 254 10.3.1. Introduction 254 10.3.2. Hartmann-Shack sensor 255 10.3.3. Phase retrieval and phase diversity 257 10.4. Myopic restoration in imaging 258 10.4.1. Motivation and noise statistic 258 10.4.2. Data processing in deconvolution from wavefront sensing 259 10.4.3. Restoration of images corrected by adaptive optics 263 10.4.4. Conclusion 267 10.5. Image reconstruction in optical interferometry (OI) 268 10.5.1. Observation model 268 10.5.2. Traditional Bayesian approach 271 10.5.3. Myopic modeling 272 10.5.4. Results 274 10.6. Bibliography 277 Chapter 11. Spectral Characterization in Ultrasonic Doppler Velocimetry 285 Jean-François GIOVANNELLI, Alain HERMENT 11.1. Velocity measurement in medical imaging 285 11.1.1. Principle of velocity measurement in ultrasound imaging 286 11.1.2. Information carried by Doppler signals 286 11.1.3.Some characteristics and limitations 288 11.1.4. Data and problems treated 288 11.2. Adaptive spectral analysis 290 11.2.1. Least squares and traditional extensions 290 11.2.2. Long AR models – spectral smoothness – spatial continuity 291 11.2.3. Kalman smoothing 293 11.2.4. Estimation of hyperparameters 294 11.2.5. Processing results and comparisons 296 11.3. Tracking spectral moments 297 11.3.1. Proposed method 298 11.3.2. Likelihood of the hyperparameters 302 11.3.3. Processing results and comparisons 304 11.4. Conclusion 306 11.5. Bibliography 307 Chapter 12. Tomographic Reconstruction from Few Projections 311 Ali MOHAMMAD-DJAFARI, Jean-Marc DINTEN 12.1. Introduction 311 12.2. Projection generation model 312 12.3. 2D analytical methods 313 12.4. 3D analytical methods 317 12.5. Limitations of analytical methods 317 12.6. Discrete approach to reconstruction 319 12.7. Choice of criterion and reconstruction methods 321 12.8. Reconstruction algorithms 323 12.8.1. Optimization algorithms for convex criteria 323 12.8.2. Optimization or integration algorithms 327 12.9. Specific models for binary objects 328 12.10. Illustrations 328 12.10.1.2D reconstruction 328 12.10.2.3Dreconstruction 329 12.11. Conclusions 331 12.12. Bibliography 332 Chapter 13. Diffraction Tomography 335 Hervé CARFANTAN, Ali MOHAMMAD-DJAFARI 13.1. Introduction 335 13.2. Modeling the problem 336 13.2.1. Examples of diffraction tomography applications 336 13.2.2. Modeling the direct problem 338 13.3. Discretization of the direct problem 340 13.3.1. Choice of algebraic framework 340 13.3.2. Method of moments 341 13.3.3. Discretization by the method of moments 342 13.4. Construction of criteria for solving the inverse problem 343 13.4.1. First formulation: estimation of x 344 13.4.2. Second formulation: simultaneous estimation of x and φ 345 13.4.3. Properties of the criteria 347 13.5. Solving the inverse problem 347 13.5.1. Successive linearizations 348 13.5.2. Joint minimization 350 13.5.3. Minimizing MAP criterion 351 13.6. Conclusion 353 13.7. Bibliography 354 Chapter 14. Imaging from Low-intensity Data 357 Ken SAUER, Jean-Baptiste THIBAULT 14.1. Introduction 357 14.2. Statistical properties of common low-intensity image data 359 14.2.1. Likelihood functions and limiting behavior 359 14.2.2. Purely Poisson measurements 360 14.2.3. Inclusion of background counting noise 362 14.2.4. Compound noise models with Poisson information 362 14.3. Quantum-limited measurements in inverse problems 363 14.3.1. Maximum likelihood properties 363 14.3.2. Bayesian estimation 366 14.4. Implementation and calculation of Bayesian estimates 368 14.4.1. Implementation for pure Poisson model 368 14.4.2. Bayesian implementation for a compound data model 370 14.5. Conclusion 372 14.6. Bibliography 372 List of Authors 375 Index 377
£170.95
ISTE Ltd and John Wiley & Sons Inc Inverse Problems in Vision and 3D Tomography
Book SynopsisThe concept of an inverse problem is a familiar one to most scientists and engineers, particularly in the field of signal and image processing, imaging systems (medical, geophysical, industrial non-destructive testing, etc.), and computer vision. In imaging systems, the aim is not just to estimate unobserved images but also their geometric characteristics from observed quantities that are linked to these unobserved quantities by a known physical or mathematical relationship. In this manner techniques such as image enhancement or addition of hidden detail can be delivered. This book focuses on imaging and vision problems that can be clearly described in terms of an inverse problem where an estimate for the image and its geometrical attributes (contours and regions) is sought. The book uses a consistent methodology to examine inverse problems such as: noise removal; restoration by deconvolution; 2D or 3D reconstruction in X-ray, tomography or microwave imaging; reconstruction of the surface of a 3D object using X-ray tomography or making use of its shading; reconstruction of the surface of a 3D landscape based on several satellite photos; super-resolution; motion estimation in a sequence of images; separation of several images mixed using instruments with different sensitivities or transfer functions; and much more.Trade Review"Apart from the high price I can recommend this book if you are interested in imaging or artificial vision." (I Programmer, 3 February 2011)Table of ContentsPreface 13 Chapter 1. Introduction to Inverse Problems in Imaging and Vision 15 Ali MOHAMMAD-DJAFARI 1.1. Inverse problems 16 1.2. Specific vision problems 21 1.3. Models for time-dependent quantities 26 1.4. Inverse problems with multiple inputs and multiple outputs (MIMO) 27 1.5. Non-linear inverse problems 30 1.6. 3D reconstructions 33 1.7. Inverse problems with multimodal observations 33 1.8. Classification of inversion methods: analytical or algebraic 34 1.9. Standard deterministic methods 40 1.10. Probabilistic methods 44 1.11. Problems specific to vision 50 1.12. Introduction to the various chapters of the book 52 1.13. Bibliography 55 Chapter 2. Noise Removal and Contour Detection 59 Pierre CHARBONNIER and Christophe COLLET 2.1. Introduction 61 2.2. Statistical segmentation of noisy images 72 2.3. Multi-band multi-scale Markovian regularization 79 2.4. Bibliography 88 Chapter 3. Blind Image Deconvolution 97 Laure BLANC-FÉRAUD, Laurent MUGNIER and André JALOBEANU 3.1. Introduction 97 3.2. The blind deconvolution problem 98 3.3. Joint estimation of the PSF and the object 103 3.4. Marginalized estimation of the impulse response 107 3.5. Various other approaches 112 3.6. Multi-image methods and phase diversity 114 3.7. Conclusion 115 3.8. Bibliography 116 Chapter 4. Triplet Markov Chains and Image Segmentation 123 Wojciech PIECZYNSKI 4.1. Introduction 124 4.2. Pairwise Markov chains (PMCs) 127 4.3. Copulas in PMCs 130 4.4. Parameter estimation 132 4.5. Triplet Markov chains (TMCs) 136 4.6. TMCs and non-stationarity 139 4.7. Hidden Semi-Markov chains (HSMCs) and TMCs 140 4.8. Auxiliary multivariate chains 144 4.9. Conclusions and outlook 148 4.10. Bibliography 149 Chapter 5. Detection and Recognition of a Collection of Objects in a Scene 155 Xavier DESCOMBES, Ian JERMYN and Josiane ZERUBIA 5.1. Introduction 155 5.2. Stochastic approaches 156 5.3. Variational approaches 167 5.4. Bibliography 184 Chapter 6. Apparent Motion Estimation and Visual Tracking 191 Etienne MÉMIN and Patrick PÉREZ 6.1. Introduction: from motion estimation to visual tracking 191 6.2. Instantaneous estimation of apparent motion 193 6.3. Visual tracking 219 6.4. Conclusions 240 6.5. Bibliography 241 Chapter 7. Super-resolution 251 Ali MOHAMMAD-DJAFARI and Fabrice HUMBLOT 7.1. Introduction 251 7.2. Modeling the direct problem 252 7.3. Classical SR methods 257 7.4. SR inversion methods 261 7.5. Methods based on a Bayesian approach 265 7.6. Simulation results 271 7.7. Conclusion 272 7.8. Bibliography 274 Chapter 8. Surface Reconstruction from Tomography Data 277 Charles SOUSSEN and Ali MOHAMMAD-DJAFARI 8.1. Introduction 277 8.2. Reconstruction of localized objects 280 8.3. Use of deformable contours for 3D reconstruction 284 8.4. Appropriate surface models and algorithmic considerations 293 8.5. Reconstruction of a polyhedric active contour 298 8.6. Conclusion 303 8.7. Bibliography 305 Chapter 9. Gauss-Markov-Potts Prior for Bayesian Inversion in Microwave Imaging 309 Olivier FÉRON, Bernard DUCHÊNE and Ali MOHAMMAD-DJAFARI 9.1. Introduction 310 9.2. Experimental configuration and modeling of the direct problem 311 9.3. Inversion in the linear case 315 9.4. Inversion in the non-linear case 325 9.5. Conclusion 335 9.6. Bibliography 336 Chapter 10. Shape from Shading 339 Jean-Denis DUROU 10.1. Introduction 339 10.2. Modeling of shape from shading 340 10.3. Resolution of shape from shading 353 10.4. Conclusion 371 10.5. Bibliography 372 Chapter 11. Image Separation 377 Hichem SNOUSSI and Ali MOHAMMAD-DJAFARI 11.1. General introduction 377 11.2. Blind image separation 378 11.3. Bayesian formulation 384 11.4. Stochastic algorithms 390 11.5. Simulation results 398 11.6. Conclusion 401 11.7. Appendix 1: a posteriori distributions 407 11.8. Bibliography 409 Chapter 12. Stereo Reconstruction in Satellite and Aerial Imaging 411 Julie DELON and Andrés ALMANSA 12.1. Introduction 411 12.2. Principles of satellite stereovision 412 12.3. Matching 415 12.4. Regularization 421 12.5. Numerical considerations 425 12.6. Conclusion 432 12.7. Bibliography 434 Chapter 13. Fusion and Multi-modality 437 Christophe COLLET, Farid FLITTI, Stéphanie BRICQ and André JALOBEANU 13.1. Fusion of optical multi-detector images without loss of information 437 13.2. Fusion of multi-spectral images using hidden Markov trees 438 13.3. Segmentation of multimodal cerebral MRI using an a priori probabilistic map 448 13.4. Bibliography 458 List of Authors 461 Index 463
£194.70
ISTE Ltd and John Wiley & Sons Inc Molecular Imaging in Nano MRI
Book SynopsisThe authors describe a technique that can visualize the atomic structure of molecules, it is necessary, in terms of the image processing, to consider the reconstruction of sparse images. Many works have leveraged the assumption of sparsity in order to achieve an improved performance that would not otherwise be possible. For nano MRI, the assumption of sparsity is given by default since, at the atomic scale, molecules aresparse structures. This work reviews the latest results on molecular imaging for nano MRI. Sparse image reconstruction methods can be categorized as either non-Bayesian or Bayesian. A comparison of the performance and complexity of several such algorithms is given.Table of ContentsIntroduction ix Chapter 1. Nano MRI 1 Chapter 2. Sparse Image Reconstruction 7 Chapter 3. Iterative Thresholding Methods 15 Chapter 4. Hyperparameter Selection Using the SURE Criterion 43 Chapter 5. Monte Carlo Approach: Gibbs Sampling 53 Chapter 6. Simulation Study 65 Bibliography 73 Index 77
£132.00
ISTE Ltd and John Wiley & Sons Inc 3D Modeling of Buildings: Outstanding Sites
Book SynopsisConventional topographic databases, obtained by capture on aerial or spatial images provide a simplified 3D modeling of our urban environment, answering the needs of numerous applications (development, risk prevention, mobility management, etc.). However, when we have to represent and analyze more complex sites (monuments, civil engineering works, archeological sites, etc.), these models no longer suffice and other acquisition and processing means have to be implemented. This book focuses on the study of adapted lifting means for “notable buildings”. The methods tackled in this book cover lasergrammetry and the current techniques of dense correlation based on images using conventional photogrammetry.Table of Contents1. Specific Requirements for the 3D Digitization of Outstanding Sites. 2. 3D Digitization Using Images. 3. 3D Digitization by Laser Scanner. 4. Complementarity of Techniques. 5. Point Cloud Processing. 6. Management and Use of Surveys.
£125.06
ISTE Ltd and John Wiley & Sons Inc Mathematical Foundations of Image Processing and
Book SynopsisMathematical Imaging is currently a rapidly growing field in applied mathematics, with an increasing need for theoretical mathematics. This book, the second of two volumes, emphasizes the role of mathematics as a rigorous basis for imaging sciences. It provides a comprehensive and convenient overview of the key mathematical concepts, notions, tools and frameworks involved in the various fields of gray-tone and binary image processing and analysis, by proposing a large, but coherent, set of symbols and notations, a complete list of subjects and a detailed bibliography. It establishes a bridge between the pure and applied mathematical disciplines, and the processing and analysis of gray-tone and binary images. It is accessible to readers who have neither extensive mathematical training, nor peer knowledge in Image Processing and Analysis. It is a self-contained book focusing on the mathematical notions, concepts, operations, structures, and frameworks that are beyond or involved in Image Processing and Analysis. The notations are simplified as far as possible in order to be more explicative and consistent throughout the book and the mathematical aspects are systematically discussed in the image processing and analysis context, through practical examples or concrete illustrations. Conversely, the discussed applicative issues allow the role of mathematics to be highlighted. Written for a broad audience – students, mathematicians, image processing and analysis specialists, as well as other scientists and practitioners – the author hopes that readers will find their own way of using the book, thus providing a mathematical companion that can help mathematicians become more familiar with image processing and analysis, and likewise, image processing and image analysis scientists, researchers and engineers gain a deeper understanding of mathematical notions and concepts.Table of ContentsPreface xvii Introduction xxv Part 5 Twelve Main Geometrical Frameworks for Binary Images 1 Chapter 21 The Set-Theoretic Framework 3 Chapter 22 The Topological Framework 9 Chapter 23 The Euclidean Geometric Framework 23 Chapter 24 The Convex Geometric Framework 37 Chapter 25 the Morphological Geometric Framework 47 Chapter 26 The Geometric and Topological Framework 57 Chapter 27 The Measure-Theoretic Geometric Framework 71 Chapter 28 The Integral Geometric Framework 89 Chapter 29 The Differential Geometric Framework 111 Chapter 30 The Variational Geometric Framework 129 Chapter 31 The Stochastic Geometric Framework 135 Chapter 32 The Stereological Framework 159 Part 6 Four Specific Geometrical Framework for Binary Images 177 Chapter 33 The Granulometric Geometric Framework 179 Chapter 34 The Morphometric Geometric Framework 189 Chapter 35 The Fractal Geometric Framework 211 Chapter 36 The Textural Geometric Framework 229 Part 7 Four 'Hybrid' Framework for Gray-Tone and Binary Images 241 Chapter 37 The Interpolative Framework 243 Chapter 38 The Bounded-Variation Framework 253 Chapter 39 The Level Set Framework 269 Chapter 40 The Distance-Map Framework 281 Concluding Discussion and Perspectives 295 Appendices 301 Tables of Notations and Symbols 303 Table of Acronyms 341 Table of Latin Phrases 347 Bibliography 349 Index of Authors 435 Index of Subjects 445
£157.45
ISTE Ltd and John Wiley & Sons Inc Digital Signal and Image Processing using MATLAB,
Book SynopsisVolume 3 of the second edition of the fully revised and updated Digital Signal and Image Processing using MATLAB, after first two volumes on the "Fundamentals" and "Advances and Applications: The Deterministic Case", focuses on the stochastic case. It will be of particular benefit to readers who already possess a good knowledge of MATLAB, a command of the fundamental elements of digital signal processing and who are familiar with both the fundamentals of continuous-spectrum spectral analysis and who have a certain mathematical knowledge concerning Hilbert spaces. This volume is focused on applications, but it also provides a good presentation of the principles. A number of elements closer in nature to statistics than to signal processing itself are widely discussed. This choice comes from a current tendency of signal processing to use techniques from this field. More than 200 programs and functions are provided in the MATLAB language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.Table of ContentsForeword ix Notations and Abbreviations xiii 1 Mathematical Concepts 1 1.1 Basic concepts on probability 1 1.2 Conditional expectation 9 1.3 Projection theorem 10 1.4 Gaussianity 13 1.5 Random variable transformation 18 1.6 Fundamental statistical theorems 21 1.7 Other important probability distributions 23 2 Statistical Inferences 25 2.1 Statistical model 25 2.2 Hypothesis tests 27 2.3 Statistical estimation 41 3 Monte-Carlo Simulation 85 3.1 Fundamental theorems 85 3.2 Stating the problem 86 3.3 Generating random variables 88 3.4 Variance reduction 99 4 Second Order Stationary Process 107 4.1 Statistics for empirical correlation 107 4.2 Linear prediction of WSS processes 111 4.3 Non-parametric spectral estimation of WSS processes 124 5 Inferences on HMM 139 5.1 Hidden Markov Models (HMM) 130 5.2 Inferences on HMM 142 5.3 Gaussian linear case: the Kalman filter 143 5.4 Discrete finite Markov case 152 6 Selected Topics 163 6.1 High resolution methods 163 6.2 Digital Communications 186 6.3 Linear equalization and the Viterbi algorithm 211 6.4 Compression 220 7 Hints and Solutions 235 H1 Mathematical concepts 235 H2 Statistical inferences 237 H3 Monte-Carlo simulation 269 H4 Second order stationary process 283 H5 Inferences on HMM 283 H6 Selected Topics 300 8 Appendices 317 A1 Miscellaneous functions 317 A2 Statistical functions 318 Bibliography 329 Index 333
£125.06
College Publications Learning and Inferring. Festschrift for Alejandro C. Frery on the Occasion of his 55th Birthday
£12.50
Springer Nature Switzerland AG Advanced Image and Video Processing Using MATLAB
Book SynopsisThis book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others. The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning，dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts. The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision.Table of ContentsIntroduction and Overview.- Matlab Functions of Image and Video.- Image and Video Segmentation.- Feature Extraction and Representation.- Common Evaluation Criterion.- Image Correction.- Image Inpainting.- Fusions.- Image Stitching.- Image Watermarking.
£53.99
Springer Nature Switzerland AG Computer Vision: Algorithms and Applications
Book SynopsisComputer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.Table of Contents
£58.49
Springer Nature Switzerland AG Foundations of Data Visualization
Book SynopsisThis is the first book that focuses entirely on the fundamental questions in visualization. Unlike other existing books in the field, it contains discussions that go far beyond individual visual representations and individual visualization algorithms. It offers a collection of investigative discourses that probe these questions from different perspectives, including concepts that help frame these questions and their potential answers, mathematical methods that underpin the scientific reasoning of these questions, empirical methods that facilitate the validation and falsification of potential answers, and case studies that stimulate hypotheses about potential answers while providing practical evidence for such hypotheses. Readers are not instructed to follow a specific theory, but their attention is brought to a broad range of schools of thoughts and different ways of investigating fundamental questions. As such, the book represents the by now most significant collective effort for gathering a large collection of discourses on the foundation of data visualization. Data visualization is a relatively young scientific discipline. Over the last three decades, a large collection of computer-supported visualization techniques have been developed, and the merits and benefits of using these techniques have been evidenced by numerous applications in practice. These technical advancements have given rise to the scientific curiosity about some fundamental questions such as why and how visualization works, when it is useful or effective and when it is not, what are the primary factors affecting its usefulness and effectiveness, and so on. This book signifies timely and exciting opportunities to answer such fundamental questions by building on the wealth of knowledge and experience accumulated in developing and deploying visualization technology in practice.Table of ContentsPart I: Theoretical Underpinnings of Data Visualization.- The Fabric of Visualization.- Visual Abstraction.- Measures in Visualization Space.- Knowledge-Assisted Visualization and Guidance.- Mathematical Foundations in Visualizations.- Transformations, Mappings and Data Summaries.- Part II: Empirical Studies in Visualization.- A Survey of Variables Used in Empirical Studies for Visualization.- Empirical Evaluations with Domain Experts.- Evaluation of Visualization Systems with Long-term Case Studies.- Vis4Vis: Visualization for (Empirical) Visualization Research.- 'Isms' in Visualization.- Open Challenges in Empirical Visualization Research.- Part III: Collaboration with Domain Experts.- Case Studies for Working with Domain Experts.- Collaboration Between Industry and University.- Collaborating Successfully with Domain Experts.- Part IV: Developing Visualizations for Broad Audiences.- Reflections on Visualization for Broad Audiences.- Reaching Broad Audiences from a Research Institute Setting.- Reaching Broad Audiences from a Large Agency Setting.- Reaching Broad Audiences from a Science Center or Museum Setting.- Reaching Broad Audiences in an Educational Setting.- Challenges and Open Issues in Visualization for Broad Audiences
£125.99
Springer Nature Switzerland AG Technology, Innovation, Entrepreneurship and Education: 3rd EAI International Conference, TIE 2019, Braga, Portugal, October 17–18, 2019, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 3rd International Conference on Technology, Innovation, Entrepreneurship and Education, TIE 2019, held in Braga, Portugal, in October 2019. The 11 full and 2 short papers focus on emerging technologies for education, entertainment, well-being, creativity, arts and business development. In addition, it aims at promoting new venture creation opportunities that emerge from these innovations, as well as innovation methods that target these core subjects.Table of ContentsInnovating and Exploring Children´s Learning.- Reading to Level Up: Gamifying Reading Fluency.- Rethinking the Design of Hotspots in Children’s Digital Picturebooks: Insights from an Exploratory Study.- Children’s tinkering activity with Collapse Informatics: the Internalization of Environmental Consciousness.- ”Play and learn”: exploring CodeCubes Innovating Media Usage.- Question & Answering interface to improve the students’ experience in an e-learning course with a virtual tutor.- Exploring the Use of Augmented Reality Concepts to Enhance the TV Viewer Experience.- Design Experiments in Nonrepresentational VR and Symmetric Texture Generation in Real-Time Innovation for Special Needs.- Didactic toy for children with special needs.- Digitally-mediated Learning Environments and Information Literacy for Active Ageing: A Pilot Study.- European video game development and disability: Reflections on data, rights, decisions and assistance Innovating Methods.- From community datamining to enterprising villagers.- The transformational effect of a designerly approach within a research project.- Visual Quotes and Physical Activity Tracking: Can Aesthetic Pleasure Motivate Our Short-term Exercise Motivation?.- Raising the Odds of Success for Innovative Product by Experimentation and Utilizing Input of Future User.
£34.19
Springer Nature Switzerland AG Unsupervised Learning in Space and Time: A Modern
Book SynopsisThis book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. Table of Contents1. Unsupervised Visual Learning: from Pixels to Seeing.- 2. Unsupervised Learning of Graph and Hypergraph Matching.- 3. Unsupervised Learning of Graph and Hypergraph Clustering.- 4. Feature Selection meets Unsupervised Learning.- 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features.- 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time.- 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks.- 8. Unsupervised Learning Towards the Future.
£107.99
Springer Nature Switzerland AG 3D Imaging, Analysis and Applications
Book SynopsisThis textbook is designed for postgraduate studies in the field of 3D Computer Vision. It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops. Overall, the book covers three main areas: ● 3D imaging, including passive 3D imaging, active triangulation 3D imaging, active time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data representation and visualisation; ● 3D shape analysis, including local descriptors, registration, matching, 3D morphable models, and deep learning on 3D datasets; and ● 3D applications, including 3D face recognition, cultural heritage and 3D phenotyping of plants. 3D computer vision is a rapidly advancing area in computer science. There are many real-world applications that demand high-performance 3D imaging and analysis and, as a result, many new techniques and commercial products have been developed. However, many challenges remain on how to analyse the captured data in a way that is sufficiently fast, robust and accurate for the application. Such challenges include metrology, semantic segmentation, classification and recognition. Thus, 3D imaging, analysis and their applications remain a highly-active research field that will continue to attract intensive attention from the research community with the ultimate goal of fully automating the 3D data capture, analysis and inference pipeline. Table of ContentsIntroduction.- Part I 3D Shape Acquisition, Representation and Visualization.- Passive 3D Imaging.- Active-triangulation 3D Imaging Systems for Industrial Inspection.- Active Time-of-Flight 3D Imaging Systems for Medium-range Applications.- Consumer-grade RGB-D cameras.- 3D Data Representation, Storage and Processing.- Part II: 3D Shape Analysis and Inference.- 3D Local Descriptors -- from Hand-crafted to Learned.- 3D Shape Registration.- 3D Shape Matching for Retrieval and Recognition.- 3D Morphable Models: the Face, Ear and Head.- Deep Learning on 3D Data.- Part III: 3D Imaging Applications.- 3D Face Recognition.- 3D Digitization of Cultural Heritage.- 3D Phenotyping of Plants.- Index
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Springer Nature Switzerland AG Image Analysis and Recognition: 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24–26, 2020, Proceedings, Part II
Book SynopsisThis two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020.The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management.Due to the corona pandemic, ICIAR 2020 was held virtually only.Table of ContentsMachine Learning.- Medical Image and Analysis.- Analysis of Histopathology Images.- Diagnosis and Screening of Ophthalmic Diseases.- Grand Challenge on Automatic Lung Cancer Patient Management.
£67.49
Springer Nature Switzerland AG Glossary of Morphology
Book SynopsisThis book is a significant novelty in the scientific and editorial landscape. Morphology is both an ancient and a new discipline that rests on Goethe's heritage and re-forms it in the present through the concepts of form and image. The latter are to be understood as structural elements of a new cultural grammar able to make the late modern world intelligible. In particular, compared to the original Goethean project, but also to C.P. Snow's idea of unifying the “two cultures”, the fields of morphological culture that are the object of this glossary have profoundly changed. The ever-increasing importance of the image as a polysemic form has made the two concepts absolutely transitive, so to speak. This is concomitant with the emergence of a culture that revolves around the image, attracting the verbal logos into its orbit. Incidentally, even the hermeneutic relationship between past and present relies more and more on the image, causing deep changes in cultural environments. Form and image are not just bridging concepts, as in the field of ancient morphology, but real transitive concepts that define the state of a culture. From the Internet to smartphones, television, advertising, etc., we are witnessing – as Horst Bredekamp observes – an immense mass of images that fill our time and affect the most diverse areas of our culture. The ancient connection between science and art recalled by Goethe emerges with unusual evidence thanks to intersecting patterns and expressive forms that are sometimes shared by different forms of knowledge. Creating a glossary and a culture of these intersections is the task of morphology, which thus enters into the boundaries between aesthetics, art, design, advertising, and sciences (from mathematics to computer science, to physics, and to biology), in order to provide the founding elements of a grammar and a syntax of the image. The latter, in its formal quality, both expressive and symbolic, is a fundamental element in the unification of the various kinds of knowledge, which in turn come to be configured, in this regard, also as styles of vision. The glossary is subdivided into contiguous sections, within a complex framework of cross-references. In addition to the two curators, the book features the collaboration of a team of scholars from the individual disciplines appearing in the glossary. Table of ContentsAesthetics.- Analogy.- Artefact.- Artifex.- Artistic Morphology.- Atmosphere.- Attractors/Basin of Attraction.- Biopolitics.- Body.- Character/State.- Chreod.- Classics.- Code (Biological).- Code (Juridical).- Colour.- Complexity.- Contour/Outline/Silhouette.- Dance.- Degeneration.- Demography.- Development/Evolution.- Device.- Diagrams.- Diaphane.- Drawing.- Dynamic System.- Eidetics.- Emergence.- Enactivism.- Epidemiology.- Epigenesis / Preformation(ism).- Epigenetic Landscape.- Epigenetics.- Ethics of image.- Evidence/Intuibility.- Extension.- Figuration/Figure/Form.- Folktale, Morphology.- Food.- Form Constancy.- Formation.- Formula
£56.99
Springer Nature Switzerland AG Fundamentals of Multimedia
Book SynopsisPREVIOUS EDITIONThis textbook introduces the “Fundamentals of Multimedia”, addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.Table of ContentsPart I: Introduction and Multimedia Data Representations.- Introduction to Multimedia.- A Taste of Multimedia.- Graphics and Image Data Representations.- Color in Image and Video.- Fundamental Concepts in Video.- Basics of Digital Audio.- Part II: Multimedia Data Compression.- Lossless Compression Algorithms.- Lossy Compression Algorithms.- Image Compression Standards.- Basic Video Compression Techniques.- MPEG Video Coding: MPEG-1, 2, 4 and 7.- Modern Video Coding Standards: H.264, H.265, and H.266.- Basic Audio Compression Techniques.- MPEG Audio Compression.- Part III: Multimedia Communications and Networking.- Network Services and Protocols for Multimedia Communications.- Internet Multimedia Content Distribution.- Multimedia over Wireless and Mobile Networks.- Cloud Computing for Multimedia Services.- Part IV: Human-Centric Interactive Multimedia.- Online Social Media Sharing.- Augmented Reality and Virtual Reality.- Content-Based Retrieval in Digital Libraries.- Cloud Computing for Multimedia Services.
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Springer Nature Switzerland AG Robotic Vision: Fundamental Algorithms in MATLAB®
Book SynopsisThis textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field — What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. “An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, StanfordTable of ContentsIntroduction.- Part I: Foundations- Representing Position and Orientation.- Part II: Computer Vision.- Light and Color.- Images and Image Processing.- Image Feature Extraction.- Part III: The Geometry of Vision.- Image Formation.- Using Multiple Images.- Index.
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Springer Nature Switzerland AG Brain-Inspired Computing: 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15–19, 2019, Revised Selected Papers
Book SynopsisThis open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures. Table of ContentsMachine Learning and Deep learning approaches in human brain mapping.- A high-resolution model of the human entorhinal cortex in the ‘BigBrain’– use case for machine learning and 3D analyses.- Deep learning-supported cytoarchitectonic mapping of the human lateral geniculate body in the BigBrain.- Brain modelling and simulation.- Computational modelling of cerebellar magnetic stimulation: the effect of washout?.- Usage and scaling of an open-source spiking multi-area model of the monkey cortex.- Exascale compute and data infrastructures for neuroscience and applications.- Modular supercomputing for neuroscience.- Fenix: Distributed e-Infrastructure Services for EBRAINS.- Independent component analysis for noise and artifact removal in three-dimensional Polarized Light Imaging.- Exascale artificial and natural neural architectures.- Brain-inspired algorithms for processing of visual data.- An hybrid attention-based system for the prediction of facial attributes.- The statistical physics of learning revisited: Typical learning curves in model scenarios.- Emotion mining: from unimodal to multimodal approaches.-
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Springer Nature Switzerland AG Document Analysis and Recognition – ICDAR 2021:
Book SynopsisThis four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.The papers are organized into the following topical sections: extracting document semantics, text and symbol recognition, document analysis systems, office automation, signature verification, document forensics and provenance analysis, pen-based document analysis, human document interaction, document synthesis, and graphs recognition.Table of ContentsExtracting Document Semantics.- MiikeMineStamps: A Long-Tailed Dataset of Japanese Stamps via Active Learning.- Deep Learning for Document Layout Generation: A First Reproducible Quantitative Evaluation and a Baseline Model.- Text and Symbol Recognition.- MRD: A Memory Relation Decoder for Online Handwritten Mathematical Expression Recognition.-Full Page Handwriting Recognition via Image to Sequence Extraction.- SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition.- IHR-NomDB: The Old Degraded Vietnamese Handwritten Script Archive Database.- Sequence Learning Model for Syllables Recognition Arranged in Two Dimensions.- Transformer for Handwritten Text Recognition using Bidirectional Post-Decoding.- Zero-Shot Chinese Text Recognition via Matching Class Embedding.- Text-conditioned Character Segmentation for CTC-based Text Recognition.-Towards Fast, Accurate and Compact Online Handwritten Chinese Text Recognition.- HCADecoder: A Hybrid CTC-Attention Decoder for Chinese Text Recognition.-Meta-learning of Pooling Layers for Character Recognition.- Document Analysis Systems.- Text-line-up: Don’t Worry about the Caret.- Multimodal Attention-based Learning for Imbalanced Corporate Documents Classification.- Light-weight Document Image Cleanup using Perceptual Loss.- Office Automation.- A New Semi-Automatic Annotation Model via Semantic Boundary Estimation for Scene Text Detection.- Searching from the Prediction of Visual and Language Model for Handwritten Chinese Text Recognition.- Towards an IMU-based Pen Online Handwriting Recognizer.- Signature Verification.- 2D vs 3D online writer identification: a comparative study.- A Handwritten Signature Segmentation Approach for Multi-resolution and Complex Documents Acquired by Multiple Sources.- Attention based Multiple Siamese Network for Offline Signature Verification.- Attention to Warp: Deep Metric Learning for Multivariate Time Series.- Document Forensics and Provenance Analysis.- Customizable Camera Verification for Media Forensic.- Density Parameters of Handwriting in Schizophrenia and Affective Disorders Assessed Using the Raygraf Computer Software.- Pen-based Document Analysis.- Language-Independent Bimodal System for Early Parkinson’s Disease Detection.-TRACE: A Differentiable Approach to Line-level Stroke Recovery for Offline Handwritten Text.- Segmentation and graph matching for online analysis of student arithmetic operations.- Applying End-to-end Trainable Approach on Stroke Extraction in Handwritten Math Expressions Images.- A Novel Sigma-Lognormal Parameter Extractor for Online Signatures.- Human Document Interaction.- Near-perfect Relation Extraction from Family Books.- Estimating Human Legibility in Historic Manuscript Images - A Baseline.- A Modular and Automated Annotation Platform for Handwritings: Evaluation on Under-resourced Languages.- Reducing the Human Effort in Text Line Segmentation for Historical Documents.- DSCNN: Dimension Separable Convolutional Neural Networks for character recognition based on inertial sensor signal.- Document Synthesis.- DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis.- Font Style that Fits an Image -- Font Generation Based on Image Context.- Bayesian Hyperparameter optimization of Deep Neural Network algorithms based on Ant Colony optimization.- End-to-End Approach for Recognition of Historical Digit Strings.- Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs.- Synthesizing Training Data for Handwritten Music Recognition.- Towards Book Cover Design via Layout Graphs.- Graphics Recognition.- Complete Optical Music Recognition via Agnostic Transcription and Machine Translation.- Improving Machine Understanding of Human Intent in Charts.- DeMatch: Towards Understanding the Panel of Chart Documents.- Sequential Next-Symbol Prediction for Optical Music Recognition.- Which Parts Determine the Impression of the Font?.- Impressions2Font: Generating Fonts by Specifying Impressions.
£42.74
Springer Nature Switzerland AG Document Analysis and Recognition – ICDAR 2021:
Book SynopsisThis four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.The papers are organized into the following topical sections: scene text detection and recognition, document classification, gold-standard benchmarks and data sets, historical document analysis, and handwriting recognition. In addition, the volume contains results of 13 scientific competitions held during ICDAR 2021.Table of ContentsScene Text Detection and Recognition.- HRRegionNet: Chinese Character Segmentation in Historical Documents with Regional Awareness.- Fast Text v. Non-text Classification of Images.- Mask Scene Text Recognizer.- Rotated Box Is Back: An Accurate Box Proposal Network for Scene Text Detection.- Heterogeneous Network Based Semi-supervised Learning For Scene Text Recognition.- Scene Text Detection with Scribble Line.- EEM: An End-to-end Evaluation Metric for Scene Text Detection and Recognition.- SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models.- Fast Recognition for Multidirectional and Multi-Type License Plates with 2D Spatial Attention.- A Multi-level Progressive Rectification Mechanism for Irregular Scene Text Recognition.- Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition.- FEDS - Filtered Edit Distance Surrogate.- Bidirectional Regression for Arbitrary-Shaped Text Detection.- Document Classification.- VML-HP: Hebrew paleography dataset.- Open Set Authorship Attribution toward Demystifying Victorian Periodicals.- A More Effective Sentence-Wise Text Segmentation Approach using BERT.- Data Augmentation for Writer Identification Using a Cognitive Inspired Model.- Key-guided Identity Document Classification Method by Graph Attention Network.- Document Image Quality Assessment via Explicit Blur and Text Size Estimation.- Analyzing the potential of Zero-Shot Recognition for Document Image Classification.- Gender Detection Based on Spatial Pyramid Matching.- EDNets: Deep Feature Learning for Document Image Classification based on Multi-view Encoder-Decoder Neural Networks.- Fast End-to-end Deep Learning Identity Document Detection, Classification and Cropping.- Gold-Standard Benchmarks and Data Sets.- Image Collation: Matching illustrations in manuscripts.- Revisiting the Coco Panoptic Metric to Enable Visual and Qualitative Analysis of Historical Map Instance Segmentation.- A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes.- GNHK: A Dataset for English Handwriting in the Wild.- Personalizing Handwriting Recognition Systems with Limited User-Specific Samples.- An Efficient Local Word Augment Approach for Mongolian Handwritten Script Recognition.- IIIT-INDIC-HW-WORDS: A Dataset for Indic Handwritten Text Recognition.- Historical Document Analysis.- AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions.- TS-Net: OCR Trained to Switch Between Text Transcription Styles.- Handwriting Recognition with Novelty.- Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction.- Data Augmentation Based on CycleGAN for Improving Woodblock-printing Mongolian Words Recognition.- SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization.- Handwriting Recognition.- Recognizing Handwritten Chinese Texts with Insertion and Swapping Using A Structural Attention Network.- Strikethrough Removal From Handwritten Words Using CycleGANs.- Iterative Weighted Transductive Learning for Handwriting Recognition.- Competition Reports.- ICDAR 2021 Competition on Scientific Literature Parsing.- ICDAR 2021 Competition on Historical Document Classification.- ICDAR 2021 Competition on Document Visual Question Answering.- ICDAR 2021 Competition on Scene Video Text Spotting.- ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment.- ICDAR 2021 Competition on Components Segmentation Task of Document Photos.- ICDAR 2021 Competition on Historical Map Segmentation.- ICDAR 2021 Competition on Time-Quality Document Image Binarization.- ICDAR 2021 Competition on On-Line Signature Verification.- ICDAR 2021 Competition on Script Identification in the Wild.- ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.- ICDAR 2021 Competition on Multimodal Emotion Recognition on Comics Scenes.- ICDAR 2021 Competition on Mathematical Formula Detection.
£42.74
Springer Nature Switzerland AG Computer Vision Systems: 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Computer Vision Systems, ICVS 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 20 papers presented were carefully reviewed and selected from 29 submissions. cover a broad spectrum of issues falling under the wider scope of computer vision in real-world applications, including among others, vision systems for robotics, autonomous vehicles, agriculture and medicine. In this volume, the papers are organized into the sections: attention systems; classification and detection; semantic interpretation; video and motion analysis; computer vision systems in agriculture.Table of ContentsAttention Systems.- Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields.- MARL: Multimodal Attentional Representation Learning for Disease Prediction.- Object Localization with Attribute Preference based on Top-Down Attention.- See the silence: improving visual-only voice activity detection by optical flow and RGB fusion.- Classification and Detection.- Score to Learn: a Comparative Analysis of Scoring Functions for Active Learning in Robotics.- Enhancing the performance of image classification through features automatically learned from depth-maps.- Object Detection on TPU Accelerated Embedded Devices.- Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification.- Semantic Interpretation.- Measuring the Sim2Real gap in 3D Object classification for different 3D data representation.- Spatially-Constrained Semantic Segmentation with Topological Μaps and Visual Εmbeddings.- Knowledge-enabled generation of semantically annotated image sequences of manipulation activities from VR demonstrations.- Make It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts.- Video and Motion Analysis.- Video Popularity Prediction through Fusing Early Viewership with Video Content.- Action Prediction during Human-Object Interaction based on DTW and Early Fusion of Human and Object Representations.- GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids.- An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset.- Computer Vision Systems in Agriculture.- Robust Counting of Soft Fruit through Occlusions with Re-identification.- Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture.- Learning Image-based Contaminant Detection in Wool Fleece from Noisy Annotations.- Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network’s Feature Pyramid Levels.
£49.49
Springer Nature Switzerland AG Handbook of Digital Face Manipulation and
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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Springer Nature Switzerland AG Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems. Table of ContentsDGM4MICCAI 2021 - Image-to-Image Translation, Synthesis.- Frequency-Supervised MRI-to-CT Image Synthesis.- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain.- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images.- Bridging the gap between paired and unpaired medical image translation.- Conditional generation of medical images via disentangled adversarial inference. -CT-SGAN: Computed Tomography Synthesis GAN.- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference.- CaCL: class-aware codebook learning for weakly supervised segmentation on diffuse image patterns.- BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification.- Evaluating GANs in medical imaging.- DGM4MICCAI 2021 - AdaptOR challenge.- Improved Heatmap-based Landmark Detection.- Cross-domain Landmarks Detection in Mitral Regurgitation.- DALI 2021.- Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph.- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency.- One-shot Learning for Landmarks Detection.- Compound Figure Separation of Biomedical Images with Side Loss.- Data Augmentation with Variational Autoencoders and Manifold Sampling.- Medical image segmentation with imperfect 3D bounding boxes.- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images.- How Few Annotations are Needed for Segmentation using a Multi-planar U-Net?.- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation.- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning.- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions.- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation.- Label Noise in Segmentation Networks : Mitigation Must Deal with Bias.- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization.- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation.
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