Image processing Books

Book SynopsisTable of Contents 1 Introduction 1.1 What is Digital Image Processing? 1.2 The Origins of Digital Image Processing 1.3 Examples of Fields that Use Digital Image Processing 1.4 Fundamental Steps in Digital Image Processing 1.5 Components of an Image Processing System 2 Digital Image Fundamentals 2.1 Elements of Visual Perception 2.2 Light and the Electromagnetic Spectrum 2.3 Image Sensing and Acquisition 2.4 Image Sampling and Quantization 2.5 Some Basic Relationships Between Pixels 2.6 Introduction to the Basic Mathematical Tools Used in Digital Image Processing 3 Intensity Transformations and Spatial Filtering 3.1 Background 3.2 Some Basic Intensity Transformation Functions 3.3 Histogram Processing 3.4 Fundamentals of Spatial Filtering 3.5 Smoothing (Lowpass) Spatial Filters 3.6 Sharpening (Highpass) Spatial Filters 3.7 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters 3.8 Combining Spatial Enhancement Methods 3.9 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering 4 Filtering in the Frequency Domain 4.1 Background 4.2 Preliminary Concepts 4.3 Sampling and the Fourier Transform of Sampled Functions 4.4 The Discrete Fourier Transform of One Variable 4.5 Extensions to Functions of Two Variables 4.6 Some Properties of the 2-D DFT and IDFT 4.7 The Basics of Filtering in the Frequency Domain 4.8 Image Smoothing Using Lowpass Frequency Domain Filters 4.9 Image Sharpening Using Highpass Filters 4.10 Selective Filtering 4.11 The Fast Fourier Transform 5 Image Restoration and Reconstruction 5.1 A Model of the Image Degradation/Restoration Process 5.2 Noise Models 5.3 Restoration in the Presence of Noise Only—Spatial Filtering 5.4 Periodic Noise Reduction Using Frequency Domain Filtering 5.5 Linear, Position-Invariant Degradations 5.6 Estimating the Degradation Function 5.7 Inverse Filtering 5.8 Minimum Mean Square Error (Wiener) Filtering 5.9 Constrained Least Squares Filtering 5.10 Geometric Mean Filter 5.11 Image Reconstruction from Projections 6 Wavelet and Other Image Transforms 6.1 Preliminaries 6.2 Matrix-based Transforms 6.3 Correlation 6.4 Basis Functions in the Time-Frequency Plane 6.5 Basis Images 6.6 Fourier-Related Transforms 6.7 Walsh-Hadamard Transforms 6.8 Slant Transform 6.9 Haar Transform 6.10 Wavelet Transforms 7 Color Image Processing 7.1 Color Fundamentals 7.2 Color Models 7.3 Pseudocolor Image Processing 7.4 Basics of Full-Color Image Processing 7.5 Color Transformations 7.6 Color Image Smoothing and Sharpening 7.7 Using Color in Image Segmentation 7.8 Noise in Color Images 7.9 Color Image Compression 8 Image Compression and Watermarking 8.1 Fundamentals 8.2 Huffman Coding 8.3 Golomb Coding 8.4 Arithmetic Coding 8.5 LZW Coding 8.6 Run-length Coding

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Book SynopsisThe complete SolidWorks reference-tutorial for beginner to advanced techniques Mastering SolidWorks is the reference-tutorial for all users. Packed with step-by-step instructions, video tutorials for over 40 chapters, and coverage of little-known techniques, this book takes you from novice to power user with clear instruction that goes beyond the basics. Fundamental techniques are detailed with real-world examples for hands-on learning, and the companion website provides tutorial files for all exercises. Even veteran users will find value in new techniques that make familiar tasks faster, easier, and more organized, including advanced file management tools that simplify and streamline pre-flight checks. SolidWorks is the leading 3D CAD program, and is an essential tool for engineers, mechanical designers, industrial designers, and drafters around the world. User friendly features such as drag-and-drop, point-and-click, and cut-and-paste tools belie the software's powerful capabilitiesTable of ContentsIntroduction xxxiii Part I Introducing SolidWorks Basics 1 Chapter 1 Introducing SolidWorks 3 Installing SolidWorks for the First Time 3 Starting SolidWorks for the First Time 6 Identifying SolidWorks Documents 9 Understanding Feature-Based Modeling 15 Understanding History-Based Modeling 16 Sketching with Parametrics 19 Understanding Design Intent 21 Editing Design Intent 21 Working with Associativity 24 The Bottom Line 24 Chapter 2 Navigating the SolidWorks Interface 25 Identifying Elements of the SolidWorks Interface 25 Making the Interface Work for You 47 The Bottom Line 59 Chapter 3 Working with Sketches and Reference Geometry .61 Creating a New Part 61 Creating a Sketch 62 Identifying Sketch Entities 64 Driving Sketches with Smart Dimensions 83 Using Tools on the Dimensions/Relations Toolbar 86 Inferencing in Sketch 89 Exploring Sketch Settings 90 Using Sketch Blocks 92 Working with Reference Geometry 93 Tutorial: Learning to Use Sketch Relations 98 Tutorial: Using Blocks and Belts 102 Tutorial: Creating Reference Geometry 105 The Bottom Line 109 Chapter 4 Creating Simple Parts and Drawings 111 Discovering Design Intent 111 Creating a Simple Part 113 Creating a Simple Assembly 125 Creating a Simple Drawing 128 The Bottom Line 142 Chapter 5 Using Visualization Techniques 143 Manipulating the View 143 Using the View Tools 149 Using the DisplayManager 157 The Display Pane 173 Part II Building Intelligence into Your Parts 181 Chapter 6 Getting More from Your Sketches 183 Editing Sketch Relations 183 Getting More from Dimensions 189 Working with Sketch Entities 189 Using Sketch Pictures 193 Using Sketch Text 196 Using Colors and Line Styles with Sketches 198 Using Other Sketch Tools 199 Sketching in 3D 201 Tutorial: Editing and Copying 204 Tutorial: Controlling Pictures, Text, Colors, and Styles 208 Tutorial: Using Metadata 211 Tutorial: Sketching Calculator 212 The Bottom Line 214 Chapter 7 Modeling with Primary Features 217 Identifying When to Use Which Tool 217 Understanding Fillet Types 231 Using Chamfers 253 Tutorial: Bracket Casting 256 The Bottom Line 260 Chapter 8 Selecting Secondary Features 261 Creating Curve Features 261 Selecting a Specialty Feature 267 Tutorial: Creating a Wire-Formed Part 280 The Bottom Line 282 Chapter 9 Patterning and Mirroring 283 Patterning in a Sketch 283 Mirroring in a Sketch 289 Working with 3D Patterns 291 Understanding Pattern Types 295 Cosmetic Patterns 307 Mirroring 3D Solids 308 Tutorial: Creating a Circular Pattern 310 Tutorial: Mirroring Features 311 Tutorial: Applying a Cosmetic Pattern 313 The Bottom Line 314 Chapter 10 Using Equations 317 Understanding Equations 317 Using Global Variables 326 Using the Modify Box 327 Using Expressions 329 Controlling Suppression States of Features 329 Linking to External Equations 330 Tutorial: Using Equations 330 The Bottom Line 333 Chapter 11 Working with Part Configurations 335 Using Design Tables 355 Creating a Simple Design Table 357 Using the Configuration Publisher 364 Tutorial: Working with Configurations and Design Tables 365 The Bottom Line 368 Chapter 12 Editing, Evaluating, and Troubleshooting 371 Using Rollback 371 Reordering Features 378 Reordering Folders 379 Using the Flyout and Detachable FeatureManagers 380 Summarizing Part Modeling Best Practices 383 Using Design for Change 385 Using Evaluation Techniques 387 Troubleshooting Errors 397 Tutorial: Utilizing Editing and Evaluation Techniques 406 The Bottom Line 412 Part III Working with Assemblies 413 Chapter 13 Building Efficient Assemblies 415 Understanding the Purpose of Assemblies 415 Identifying the Elements of an Assembly 423 Using SpeedPaks 432 Using Subassemblies 433 Using Folders 438 Working with Tree Display Options 440 Tutorial: Arranging Assemblies 443 Tutorial: Managing the FeatureManager 446 The Bottom Line 447 Chapter 14 Getting More from Mates 449 Applying Mates 449 Mating for Motion 459 Working with Advanced and Mechanical Mate Types 462 Editing and Troubleshooting 471 Examining Mate Options 474 Reviewing Mate Best Practices 475 Tutorial: Mating for Success 477 The Bottom Line 480 Chapter 15 Patterning and Mirroring Components 483 Component Patterns 483 Using Local Component Patterns 484 Using Feature-Driven Component Patterns 491 Creating a Chain Pattern 492 Understanding Other Pattern Options 494 Tutorial: Creating Component Patterns 495 The Bottom Line 497 Chapter 16 Working with Assembly Sketches and Layouts 499 Looking at the Techniques 499 Using the Layout Feature 505 Tutorial: Working with a Layout 507 The Bottom Line 513 Chapter 17 Using Assembly Tools 515 Placing Parts without Mates 515 Using Proximity Tools 520 Selecting Components 528 Reading Assembly Performance Evaluation Results 532 Using Defeature 533 Using the Hole Alignment Tool 534 The Bottom Line 535 Chapter 18 Using Libraries, Assembly Features, and Hole Wizard 537 Using Library Features 537 Creating Library Features 543 Creating Assembly Cuts 551 Making Fillets and Chamfers in Assemblies 554 Using the Hole Wizard 555 Creating Weld Beads 558 Working with Envelopes 560 Understanding Smart Components 562 Using Smart Components 563 Auto-Sizing Smart Components 566 Making Smart Components 568 Tutorial: Working with Smart Components 576 Tutorial: Working with Library Features 579 The Bottom Line 586 Chapter 19 Controlling Assembly Configurations and Display States 589 Using Display States 589 Understanding Assembly Configurations 592 Tutorial: Working with Assembly Configurations 600 The Bottom Line 604 Chapter 20 Modeling in Context 605 Understanding In-Context Modeling 605 Dealing with the Practical Details of In-Context Modeling 615 Using Other Types of External References 630 Tutorial: Working In-Context 631 The Bottom Line 635 Chapter 21 Editing, Evaluating, and Troubleshooting Assemblies 637 Working with Mates 637 Editing File Management Issues 645 Evaluating Assemblies 651 The Bottom Line 654 Chapter 22 Working with Large Scale Design 657 Using Large Design Review 657 Creating a Walk-Through 661 Creating a GridSystem 663 Transferring Data with the IFC File Type 667 The Bottom Line 668 Chapter 23 Animating with the MotionManager 669 Familiarizing Yourself with the MotionManager 670 Using the Animation Wizard 674 Animating the View 683 Animating with Key Points 691 Animating with Basic Motion 696 Using the Mate Controller 700 The Bottom Line 702 Part IV Creating Drawings 703 Chapter 24 Automating Drawings: The Basics 705 Comparing Templates and Formats 705 Creating Drawing Templates 720 Creating Blocks 725 The Bottom Line 726 Chapter 25 Working with Drawing Views 727 Creating Common View Types 727 Creating Other View Types 740 Using Display Options in Views 751 Distinguishing Views from Sheets 756 Tutorial: Working with View Types, Settings, and Options 756 The Bottom Line 762 Chapter 26 Using Annotations and Symbols 765 Using Notes 765 Using Blocks in Drawings 775 Using Symbols 779 Using Center Marks and Centerlines 780 Tutorial: Using Annotations 782 The Bottom Line 784 Chapter 27 Dimensioning and Tolerancing 787 Putting Dimensions on Drawings 787 Adding Tolerances 799 Using Dimension Styles 802 Aligning Dimensions and Annotations 802 Tutorial: Working with Dimensions and Tolerances 805 The Bottom Line 808 Chapter 28 Using Layers, Line Fonts, and Colors 809 Controlling Layers 809 Controlling Line Format 813 Hiding and Showing Edges 815 Tutorial: Using Drawing Display Tools 816 The Bottom Line 818 Chapter 29 Working with Tables and Drawings 821 Driving the Bill of Materials 821 Using Design Tables 830 Placing Hole Tables on Drawings 832 Using Revision Tables 834 Using General Tables 836 Working with Tables in Models 836 Tutorial: Using BOMs 837 Tutorial: Using Hole Tables 841 Tutorial: Using Revision Tables 843 The Bottom Line 844 Chapter 30 Creating Assembly Drawings 845 Combining Parts and Assemblies on the Same Drawing 845 Using Multi-Page Templates 847 Using Views with Special Assembly Functions 849 Using Color in Assembly Drawing Views 860 Setting Up Drawings of Large Assemblies 861 Tutorial: Creating a Simple Assembly Drawing 866 The Bottom Line 869 Part V Using Advanced and Specialized Techniques 871 Chapter 31 Modeling Multibodies 873 Using Powerful Tools Effectively 874 Understanding Multibody Techniques 876 Managing Bodies 897 Tutorials: Working with Multibodies 902 The Bottom Line 906 Chapter 32 Working with Surfaces 907 Introducing Surfaces 907 Understanding Surfacing Terminology 908 Surfacing Tools 911 Tutorial: Working with Surfaces 926 The Bottom Line 933 Chapter 33 Employing Master Model Techniques 935 Master Model Tools and Techniques 935 Using Pull Functions 937 Using Push Functions 940 Tutorial: Working with Master Model Techniques 943 The Bottom Line 947 Chapter 34 Using SolidWorks Sheet Metal Tools 949 Using the Base Flange Features 949 Making Sheet Metal Parts from Generic Models 983 Using Other Methods 989 Using Multibody Techniques with Sheet Metal 991 Tutorial: Working with the Insert Bends Method for Sheet Metal Parts 992 Tutorial: Using the Base Flange Sheet Metal Method 995 The Bottom Line 999 Chapter 35 Creating Sheet Metal Drawings 1005 Making Sheet Metal Drawings 1005 Getting the Flat Pattern 1006 Making Drawings of Multibody Sheet Metal Parts 1012 The Bottom Line 1015 Chapter 36 Creating Weldments and Weldment Drawings 1017 Sketching in 3D 1017 Using the Weldment Tools 1022 Using Nonstructural Components 1032 Using Sub-Weldments 1032 Working with Cut Lists 1033 Creating Weldment Drawings 1038 Tutorial: Working with Weldments 1040 The Bottom Line 1046 Chapter 37 Using Imported Geometry and Direct-Editing Techniques 1049 Understanding the Basics of Imported Geometry 1049 Understanding the Traditional Role of Direct Editing Tools 1060 Understanding the Strengths and Limitations of Direct Using SolidWorks Direct Editing Tools 1062 Tutorial: Importing and Repairing Solid Geometry 1067 Tutorial: Flex and Freeform 1069 The Bottom Line 1072 Chapter 38 Using Plastic Features 1073 Using Plastic Features 1073 Using Plastic Evaluation Tools 1093 The Bottom Line 1102 Chapter 39 Using Mold Tools 1107 Working with the Mold Tools Process 1107 Working Manually with Mold Tools 1122 The Bottom Line 1124 Appendixes 1127 Appendix A The Bottom Line 1127 Chapter 1: Introducing SolidWorks 1127 Chapter 2: Navigating the SolidWorks Interface 1127 Chapter 3: Working with Sketches and Reference Geometry 1128 Chapter 4: Creating Simple Parts and Drawings 1129 Chapter 5: Using Visualization Techniques 1130 Chapter 6: Getting More from Your Sketches 1130 Chapter 7: Modeling with Primary Features 1131 Chapter 8: Selecting Secondary Features 1132 Chapter 9: Patterning and Mirroring 1133 Chapter 10: Using Equations 1134 Chapter 11: Working with Part Configurations 1135 Chapter 12: Editing, Evaluating, and Troubleshooting 1135 Chapter 13: Building Efficient Assemblies 1136 Chapter 14: Getting More from Mates 1137 Chapter 15: Patterning and Mirroring Components 1138 Chapter 16: Working with Assembly Sketches and Layouts 1139 Chapter 17: Using Assembly Tools 1140 Chapter 18: Using Libraries, Assembly Features, and Hole Wizard 1141 Chapter 19: Controlling Assembly Configurations and Display States 1142 Chapter 20: Modeling in Context 1143 Chapter 21: Editing, Evaluating, and Troubleshooting Assemblies 1143 Chapter 22: Working with Large Scale Design 1144 Chapter 23: Animating with the MotionManager 1145 Chapter 24: Automating Drawings: The Basics 1145 Chapter 25: Working with Drawing Views 1146 Chapter 26: Using Annotations and Symbols 1147 Chapter 27: Dimensioning and Tolerancing 1148 Chapter 28: Using Layers, Line Fonts, and Colors 1149 Chapter 29: Working with Tables and Drawings 1150 Chapter 30: Creating Assembly Drawings 1150 Chapter 31: Modeling Multibodies 1151 Chapter 32: Working with Surfaces 1152 Chapter 33: Employing Master Model Techniques 1153 Chapter 34: Using SolidWorks Sheet Metal Tools 1155 Chapter 35: Creating Sheet Metal Drawings 1159 Chapter 36: Creating Weldments and Weldment Drawings 1160 Chapter 37: Using Imported Geometry and Direct-Editing Techniques 1161 Chapter 38: Using Plastic Features 1162 Chapter 39: Using Mold Tools 1165 Appendix B Finding Help 1169 SolidWorks Help 1169 SolidWorks Website 1170 Customer Portal 1170 Appendix C What’s on the Website 1175 System Requirements 1175 Customer Care 1176 Index 1177

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Book SynopsisPixInsight has taken the astro-imaging world by storm. As the first comprehensive postprocessing platform to be created by astro-imagers for astro-imagers, it has for many replaced other generic graphics editors as the software of choice. PixInsight has been embraced by professionals such as the James Webb (and Hubble) Space Telescope's science imager Joseph DePasquale and Calar Alto's Vicent Peris, as well as thousands of amateurs around the world. While PixInsight is extremely powerful, very little has been printed on the subject. The first edition of this book broke that mold, offering a comprehensive look into the software’s capabilities. This second edition expands on the several new processes added to the PixInsight platform since that time, detailing and demonstrating each one with a now-expanded workflow. Addressing topics such as PhotometricColorCalibration, Large-Scale Pixel Rejection, LocalNormalization and a host of other functions, this text remains the authoritative guide to PixInsight.Table of ContentsPreface.- Acknowledgements.- About the Author.- Part 1: Preprocessing.- Chapter 1: Image Evaluation.- Chapter 2: Calibration and Integration.- Chapter 3: Cosmetic Correction.- Chapter 4: One-Shot Color.- Chapter 5: Star Alignment.- Chapter 6: Image Integration.- Chapter 7: Batch Preprocessing.- Part 2: Linear Post-Processing.- Chapter 8: File Preparation.- Chapter 9: Background Modelization.- Chapter 10: Introduction to Masks.- Chapter 11: Deconvolution.- Chapter 12: Linear Color Processing and Noise Reduction.- Part 3: Nonlinear Post-Processing.- Chapter 13: Delinearization and LRGB Combination.- Chapter 14: A Look Back.- Chapter 15: Nonlinear Noise Reduction.- Chapter 16: High Dynamic Range Compression.- Chapter 17: Contrast and Sharpness.- Chapter 18: Color Saturation.- Chapter 19: Fine Finishers.- Chapter 20: Archiving and Exporting.- Chapter 21: Learning by Example: One-Shot Color and Monochromatic Workflow.- Part 4: Special Processing.- Chapter 22: Multiscale, Drizzle, High Dynamic Range, and Comet Techniques.- Chapter 23: Narrowband Processing.- Chapter 24: Mosaic Processing.- Chapter 25: More Insight.- Afterword.- Index.

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Book SynopsisTrade Review"This is a very complete Machine Learning book, as it covers statistical learning theory, both from frequentist and Bayesian perspectives. It also encompasses signal processing, probabilistic graphical models, deep learning, and latent variable modeling. It balances mathematical rigor with insightful comments to ease clear interpretation. The many examples make the text even more comprehensive. Each chapter has a well-curated list of references for further deepening on specific topics. Thus, it provides a thorough background for Machine Learning at an upper undergraduate level course. This book is also an excellent reference for practitioners to understand the necessary theory to apply Machine Learning with informed criteria." --Hamed Yazdanpanah, Postdoctoral Researcher, University of São Paulo Reviews of the previous edition: "Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars..." --Computing Reviews "Machine Learning: A Bayesian and Optimization Perspective, Academic Press, 2105, by Sergios Theodoridis is a wonderful book, up to date and rich in detail. It covers a broad selection of topics ranging from classical regression and classification techniques to more recent ones including sparse modeling, convex optimization, Bayesian learning, graphical models and neural networks, giving it a very modern feel and making it highly relevant in the deep learning era. While other widely used machine learning textbooks tend to sacrifice clarity for elegance, Professor Theodoridis provides you with enough detail and insights to understand the "fine print". This makes the book indispensable for the active machine learner." --Prof. Lars Kai Hansen, DTU Compute - Dept. Applied Mathematics and Computer Science Technical University of Denmark "Before the publication of Machine Learning: A Bayesian and Optimization Perspective, I had the opportunity to review one of the chapters in the book (on Monte Carlo methods). I have published actively in this area, and so I was curious how S. Theodoridis would write about it. I was utterly impressed. The chapter presented the material with an optimal mix of theoretical and practical contents in very clear manner and with information for a wide range of readers, from newcomers to more advanced readers. This raised my curiosity to read the rest of the book once it was published. I did it and my original impressions were further reinforced. S. Theodoridis has a great capability to disentangle the important from the unimportant and to make the most of the used space for writing. His text is rich with insights about the addressed topics that are not only helpful for novices but also for seasoned researchers. It goes without saying that my department adopted his book as a textbook in the course on machine learning." --Petar M. Djuric, Ph.D. SUNY Distinguished Professor Department of Electrical and Computer Engineering Stony Brook University, Stony Brook, USA "As someone who has taught graduate courses in pattern recognition for over 35 years, I have always looked for a rigorous book that is current and appealing to students with widely varying backgrounds. The book on Machine Learning by Sergios Theodoridis has struck the perfect balance in explaining the key (traditional and new) concepts in machine learning in a way that can be appreciated by undergraduate and graduate students as well as practicing engineers and scientists. The chapters have been written in a self-consistent way, which will help instructors to assemble different sections of the book to suit the background of students" --Rama Cellappa, Distinguished University Professor, Minta Martin Professor of Engineering, Chair, Department of Electrical and Computer Engineering, University of Maryland, USATable of Contents1. Introduction 2. Probability and stochastic Processes 3. Learning in parametric Modeling: Basic Concepts and Directions 4. Mean-Square Error Linear Estimation 5. Stochastic Gradient Descent: the LMS Algorithm and its Family 6. The Least-Squares Family 7. Classification: A Tour of the Classics 8. Parameter Learning: A Convex Analytic Path 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations 10. Sparsity-Aware Learning: Algorithms and Applications 11. Learning in Reproducing Kernel Hilbert Spaces 12. Bayesian Learning: Inference and the EM Algorithm 13. Bayesian Learning: Approximate Inference and nonparametric Models 14. Montel Carlo Methods 15. Probabilistic Graphical Models: Part 1 16. Probabilistic Graphical Models: Part 2 17. Particle Filtering 18. Neural Networks and Deep Learning 19. Dimensionality Reduction and Latent Variables Modeling

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Book SynopsisThese fifteen papers explore the ways in which recent developments in imaging, image analysis, and image display and diffusion can be applied to objects of material culture in order to enhance historians'' understanding of the period from which the objects came (in this case, the remote past). In interpreting artefacts, the historian acts out a perceptual-cognitive task of transforming often noisy and impoverished signals into semantically rich symbols that have to be set within a cultural and historical context. Engineering scientists, equipped with a range of sophisticated techniques, equipment and highly specialised knowledge, are not always as aware as they might be of the range and the exact nature of problems faced by historians in interpreting objects of material culture. By providing the opportunity for scholars from these communities to explain to each other what they are doing and how, the papers explore the ways in which the scientific contributors and the historians are thiTrade Review...a bold, original and well-illustrated collection of fifteen papers addressing state-of-the-art computer-based imaging of ancient visual culture, and it opens up a fruitful collaborative dialogue between the Humanities and the Sciences ... this volume will surely - as the Editors hoped - 'set a standard and a guideline for interdisciplinary research' * Mark Bradley, The Classical Review *Table of ContentsIntroduction ; Wooden stilus tablets from Roman Britain ; Shadow Stereo, image filtering, and constraint propagation ; Digitising cuneiform tablets ; Interpretation of ancient runic inscriptions by laser scanning ; Virtual reality, relative accuracy: modelling architecture and sculpture with VRML ; Automatic creation of virtual artefacts from video sequences ; At the foot of Pompey's statue: reconceiving Rome's theatrum lapideum ; Modelling Sagalassos: creation of a 3D archaeological virtual site ; Three-simensional laser imaging and processing in an archaeological context; ; Movements of the mental eye in pictorial space ; The potential for image analysis in numismatics ; Italian terra sigillata with applique decoration: digitising, visualising & web-publishing ; Shape from profiles ; The skull as the armature of the face: reconstructing ancient faces ; Reconstruction of a 3D mummy portrait from Roman Egypt

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Book SynopsisUnmanned aircraft systems (UAS) are rapidly emerging as flexible platforms for capturing imagery and other data across the sciences. Many colleges and universities are developing courses on UAS-based data acquisition. Fundamentals of Capturing and Processing Drone Imagery and Data is a comprehensive, introductory text on how to use unmanned aircraft systems for data capture and analysis. It provides best practices for planning data capture missions and hands-on learning modules geared toward UAS data collection, processing, and applications.FEATURES Lays out a step-by-step approach to identify relevant tools and methods for UAS data/image acquisition and processing. Provides practical hands-on knowledge with visual interpretation, well-organized and designed for a typical 16-week UAS course offered on college and university campuses. Suitable for all levels of readers and does not require prior knowTable of ContentsPart I: Getting Started with Drone Imagery and Data 1. Introduction to Capturing and Processing Drone Imagery and Data 2. An Introduction to Drone Remote Sensing and Photogrammetry 3. Choosing a Sensor for UAS Imagery Collection 4. Mission Planning for Capturing UAS Imagery 5. Drone Regulations: What You Need to Know before You Fly 6. Structure from Motion (SfM) Workflow for Processing Drone Imagery 7. Aerial Cinematography with UAS Part II: Hands-On Applications Using Drone Imagery and Data 8. Planning Unoccupied Aircraft Systems (UAS) Missions 9. Aligning and Stitching Drone-Captured Images 10. Counting Wildlife from Drone-Captured Imagery Using Visual and Semi-Automated Techniques 11. Terrain and Surface Modeling of Vegetation Height Using Simple Linear Regression 12. Assessing the Accuracy of Digital Surface Models of an Earthen Dam Derived from SfM Techniques 13. Estimating Forage Mass from Unmanned Aircraft Systems in Rangelands 14. Applications of UAS-Derived Terrain Data for Hydrology and Flood Hazard Modeling 15. Comparing UAS and Terrestrial Laser Scanning Methods for Change Detection in Coastal Landscapes 16. Digital Preservation of Historical Heritage Using 3D Models and Augmented Reality 17. Identifying Burial Mounds and Enclosures Using RGB and Multispectral Indices Derived from UAS Imagery 18. Detecting Scales of Drone-Based Atmospheric Measurements Using Semivariograms 19. Assessing the Greenhouse Gas Carbon Dioxide in the Atmospheric Boundary Layer

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Book SynopsisThe book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and feature selection for pattern classification/recognition, and advanced concepts like object classification, object tracking, image-based rendering, and image registration. Intended to be a companion to a typical teaching course on computer vision, the book takes a problem-solving approach.Table of ContentsPart I Image Formation and Image Processing. 1 Introduction to Computer Vision and Basic Concepts of Image Formation. 2 Image Processing Concepts. Part II Image Features. 3 Image Descriptors and Features. Part III Recognition. 4 Fundamental Pattern Recognition Concepts. Part IV Applications. 5 Applications of Computer Vision.

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Book SynopsisThis book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc. This text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions. This book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.Table of Contents1. Deep Learning-Based Hyperspectral Multimodal Biometric Authentication System Using Palmprint and Dorsal Hand Vein. 2. Cancelable Biometrics for Template Protection: Future Directives with Deep Learning. 3. On Training Generative Adversarial Network for Enhancement of Latent Fingerprints. 4. DeepFake Face Video Detection Using Hybrid Deep Residual Networks nad LSTM Architecture. 5. Multi-spectral Short-Wave Infrared Sensors and Convolutional Neural Networks for Biometric Presentation Attack Detection. 6. AI-Based Approach for Person Identification Using ECG Biometric. 7. Cancelable Biometric Systems from Research to Reality: The Road Less Travelled. 8. Gender Classification under Eyeglass Occluded Ocular Region: An Extensive Study Using Multi-spectral Imaging. 9. Investigation of the Fingernail Plate for Biometric Authentication using Deep Neural Networks. 10. Fraud Attack Detection in Remote Verification systems for Non-enrolled Users. 11. Indexing on Biometric Databases. 12. Iris Segmentation in the Wild Using Encoder-Decoder-Based Deep Learning Techniques. 13. PPG-Based Biometric Recognition: Opportunities with Machine and Deep Learning. 14. Current Trends of Machine Learning Techniques in Biometrics and its Applications.

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Book Synopsis"Where the Animals Go is beautiful and thrilling, a combination of the best in science and exposition, and a joy to study cover to cover." —Edward O. Wilson, University Research Professor Emeritus, Harvard UniversityTrade Review"[Where the Animals Go] is an enthralling volume, downright gorgeous in its illustrations and text. Its double intent is brilliant, too — to bring each of us closer to the animal world and to highlight fresh ways to think about conservation." -- Barbara King - NPR"Where the Animals Go elegantly elucidates the role new technologies has played in expanding our knowledge of animal migration." -- Science"Cheshire and Uberti write about billions of data points being collected—some by citizen scientists—and their ravishing maps put this information to good use…[They] show us with precision and clarity where the animals go." -- The Washington Post"This book is beautiful as well as informative and inspiring. There is no doubt it will help in our fight to save wildlife and wild habitats." -- Jane Goodall"In recent years, technology has made it possible to track animal movements from afar in more and more detail… [Cheshire and Uberti] have dipped into this deluge of data to create 50 beautiful and engaging maps that reveal the wanderings of animals." -- National Geographic"A striking example of how innovative technology can be used to increase our understanding of the natural world." -- Financial Times"This is a special kind of detective story. After millennia of using footprints, feces, feathers, broken foliage and nests to track animals, the process is now so teched up you need to read this book to find out the how, what and why." -- New Scientist"[A] stunning translation of movement onto paper." -- Scientific American"[W]ell laid out, easy to understand and a pleasure to return to many times." -- Seattle Times"An enthralling look at the world that technology can help us uncover… Exquisite." -- Emily Scragg - British Trust for Ornithology"Part coffee-table album, part scientific research compendium, [Where the Animals Go] presents these global perambulations in lush detail, reveling in their minutiae and in the technological leaps that make such observations possible. . . tracking an animal through time and space transforms it from a mere object of scientific interest into a story whose unsolved mysteries capture our imagination." -- M. R. O'Connor - Undark Magazine"[A] gorgeous data trove… Accompanying the text are beautifully designed four-color maps and other visualizations … [A]n inspiring introduction to an important area of science." -- Library Journal

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Book SynopsisThis book is a collection representing some of the most powerful and useful computer techniques in the service of art.Table of ContentsList of Figures xxi List of Tables xlv List of Algorithms xlvii Preface xlix Lorenzo Lotto lviii Giovanni Morelli and the birth of "scientific" connoisseurship lix Overview lxi Intended audience lxii Prerequisites lxiii Acknowledgements lxiv 1 Digital imaging 1 1.1 Introduction 1 1.2 Electromagnetic radiation and light 4 1.3 Interaction of electromagnetic radiation with art materials 7 1.4 Cameras and scanners 9 1.4.1 Cameras 10 1.4.2 Flatbed scanners 11 1.5 Parameters for image acquisition in the visible 12 Billy Pappas 13 1.5.1 Spatial resolution 15 1.5.2 Bit depth 16 1.5.3 Dynamic range and contrast 17 1.6 Reading digital images of art on–screen 18 1.6.1 Reading a digital image of Leonardo's La Bella Principessa 22 Leonardo da Vinci 22 1.7 Infrared photography and reflectography 25 1.8 Ultraviolet imaging 26 1.9 Multispectral and hyperspectral imaging 27 1.9.1 Hyperspectral imaging of the Archimedes Palimpsest 30 1.10 X-radiographic imaging 32 1.11 Fluorescence imaging 35 1.12 Capture of three–dimensional surfaces of art 37 1.12.1 Raking illumination 38 1.12.2 Reflectance transformation imaging (RTI) 40 1.12.3 Stereographic imaging 42 1.13 Optical coherence tomography (OCT) 43 1.14 Raman spectroscopic imaging and X-ray fluorescence imaging 45 1.14.1 Raman spectroscopic imaging (RSI) 45 1.14.2 X-ray fluorescence imaging (XRF) 46 1.15 Summary 47 1.16 Bibliographical remarks 49 2 Image processing 53 2.1 Introduction 53 2.2 Pixel–based image processing 57 2.3 Region–based image processing 61 2.3.1 Linear image processing 62 2.3.2 Nonlinear region–based image processing 63 2.3.3 Color quantization 64 2.3.4 Edge and line detection 69 2.3.5 Dilation and erosion 71 2.3.6 Skeletonization 72 2.4 Inpainting 72 2.5 Feature extraction 74 2.5.1 Keypoint extraction 75 2.5.2 Craquelure and crazing analysis 78 2.5.3 Computational tests for counterproofing by Jan van der Heyden 81 Jan van der Heyden 83 2.6 Segmentation 86 2.6.1 Deep nets for image segmentation 88 2.7 Geometric transformations 95 2.8 Chamfer transform and Chamfer distance 101 2.8.1 Tests for copying of Jan van Eyck's portraits of Niccolò Albergati 103 2.9 Discrete Fourier and wavelet transforms 111 2.9.1 Discrete Fourier transform (DFT) 111 2.9.2 Canvas support weave analysis 114 2.9.3 Discrete wavelet transform (DWT) 116 2.10 Compositing and integrating art images 118 2.10.1 Image compositing 118 2.10.2 Superresolution 119 2.11 Image separation 123 2.12 Summary 123 2.13 Bibliographical remarks 125 3 Color analysis 129 3.1 Introduction 129 3.2 Visible–light spectra and color appearance 132 3.3 Overview of human color vision 133 3.3.1 Properties of color descriptions 134 3.3.2 Opponent color processing and unique hues 137 3.3.3 Humanist descriptions of color 138 3.3.4 Spatial aspects of color perception 139 Josef Albers 140 3.3.5 Color and lightness constancy and brightness perception 141 3.3.6 Quantitative descriptions and additive color mixing 141 3.3.7 Representing artists' palettes 145 3.4 Physics of color in art materials 147 3.4.1 Pigments and color appearance 147 3.5 Representing color arising from mixing paints 151 3.5.1 Identifying pigments in artworks based on spectra 152 3.6 Digital rejuvenation of pigment colors 154 3.6.1 Digital rejuvenation of faded artworks 157 Georges Seurat 158 3.7 Digital cleaning of paintings 160 3.8 Summary 164 3.9 Bibliographical remarks 165 4 Brush stroke and mark analysis 171 4.1 Introduction 171 Cy Twombly 173 4.2 Analysis of printed lines and marks 175 Katsushika Hokusai 178 4.3 Inferring tools from marks 182 Sheila Waters 184 4.3.1 Analysis of brush strokes 185 4.3.2 Segmenting and isolating brush strokes computationally 187 4.3.3 Extracting opaque marks in multiple layers 189 Vincent Willem van Gogh 193 4.3.4 Visual evidence of authorship of Pollock's drip paintings 194 Jackson Pollock 195 4.3.5 Extracting layers of translucent brush strokes 195 4.4 Characterizing the shapes of strokes and marks 203 4.5 Global methods for inferring sequences of marks in paintings 206 4.6 Summary 208 4.7 Bibliographical remarks 208 5 Perspective and geometric analysis 211 5.1 Introduction 211 5.2 Projective geometry 214 5.2.1 The mathematics of projection 216 5.2.2 One–point, two–point, and three–point perspectives 222 5.2.3 Parallel or orthographic perspective in Asian art 223 5.3 Estimating the center of projection 224 5.3.1 Foreshortening and size comparisons of depicted objects 230 Piero della Francesca 231 5.3.2 Cross–ratio analysis 232 5.3.3 Estimating the center of projection from object sizes 234 5.4 Estimating geometric accuracy in artworks 235 5.4.1 Hans Memling's Flower Still-Life 235 Hans Memling 237 5.4.2 The carpet in Lorenzo Lotto's Husband and Wife 238 5.4.3 The chandelier in the Arnolfini Portrait 238 Jan van Eyck 243 5.4.4 Warping Andrea Mantegna's Lamentation of Christ to make consistent perspective 251 5.4.5 Dewarping the murals in Sennedjem's Tomb 252 5.4.6 Warping de Chirico's Ariadne to make consistent perspective 255 Giorgio de Chirico 256 5.4.7 Robert Campin and workshop's Mérode Altarpiece 257 Robert Campin 258 5.5 Slant anamorphic art 260 Ed Ruscha (Edward Joseph Ruscha IV) 260 5.5.1 Hans Holbein's The Ambassadors 263 Hans Holbein 263 5.6 Inferring depth from projected images 264 5.6.1 Computing a three–dimensional model from one perspective image 265 Masaccio 266 5.6.2 Computing a three–dimensional model from two perspective images 267 5.7 Summary 271 5.8 Bibliographical remarks 272 6 Optical analysis 275 6.1 Introduction 275 6.2 Reflection and refraction 277 6.3 Plane mirrors 278 6.3.1 Virtual image formation by plane mirrors 279 6.3.2 Depictions of plane mirrors in art 281 6.3.3 Diego Velázquez’s Las Meninas 283 Diego Velázquez 284 6.4 Convex spherical mirrors 288 6.4.1 Virtual image formation by convex spherical mirrors 290 6.4.2 Jan van Eyck’s Portrait of Giovanni Arnolfini and his Wife 292 6.4.3 Claude glass 297 6.4.4 Parmigianino’s Self–Portrait in a Convex Mirror 298 Parmigianino (Girolamo Francesco Maria Mazzola) 298 6.4.5 Hans Memling's Virgin and Child and Maarten van Nieuwenhove 304 6.4.6 Dewarping images in generalized cylindrical mirrors 308 6.5 Conical and cylindrical mirrors and anamorphic art 312 6.5.1 Conical mirror anamorphic art 313 6.5.2 Cylindrical mirror anamorphic art 317 6.6 Concave spherical mirrors 318 6.6.1 Virtual image formation by concave mirrors 320 6.6.2 Real image formation by concave mirrors 322 6.7 Converging lenses 323 6.7.1 Virtual image formation by converging lenses 325 6.7.2 Real image formation by convex lenses 327 6.8 Camera lucida and camera obscura 328 6.8.1 Camera lucida 328 6.8.2 Camera obscura 331 6.8.3 Depth of field, depth of focus, and blur spots 333 6.9 Optical projections and the creation of art 336 6.9.1 Jan van Eyck's Portrait of Giovanni Arnolfini and his wife 337 6.9.2 Caravaggio's Supper at Emmaus 342 6.9.3 Lorenzo Lotto's Husband and Wife 345 6.9.4 Johannes Vermeer's Lady at the Virginals with a Gentleman 349 Johannes Vermeer 349 6.9.5 Canaletto's Piazza San Marco 363 Canaletto (Giovanni Antonio Canal) 364 6.9.6 Photorealists 364 Philip Barlow 366 6.10 Refraction and nonimaging optics in art 366 6.10.1 Leonardo's Salvator Mundi 366 6.11 Summary 371 6.12 Bibliographical remarks 372 7 Lighting analysis 377 7.1 Introduction 377 7.2 Basic shadows 381 7.2.1 General classes of lighting analysis methods 383 7.3 Cast–shadow analysis 383 7.3.1 Illumination from two or more point-sources 388 7.3.2 Cast–shadow analysis under geometric constraints 388 7.4 Lighting information from highlights 389 7.4.1 Illumination direction from highlights on simple estimated shapes 393 7.5 The optics of diffuse reflections 394 7.6 Inferring illumination from plane surfaces 396 Georges de la Tour 398 7.7 Interreflection 400 7.8 Occluding–contour algorithms 401 7.8.1 Single–point occluding–contour algorithm 403 7.8.2 General occluding–contour algorithm 405 Caravaggio (Michelangelo Merisi da Caravaggio) 407 7.8.3 Lightfield occluding–contour algorithm 408 Garth Herrick 409 7.8.4 Theory of the lightfield occluding–contour algorithm 410 7.8.5 Application of the lightfield occluding–contour algorithm 415 7.9 Computer graphics for the analysis of lighting 418 7.9.1 Georges de la Tour's Christ in the Carpenter's Studio (model) 419 7.9.2 Johannes Vermeer's Girl with a Pearl Earring 421 7.9.3 René Magritte's The Menaced Assassin 422 7.9.4 Bidirectional reflectance distribution functions (BRDFs) 424 7.9.5 Caravaggio's The Calling of St. Matthew 425 7.10 Shape–from–shading algorithms 426 7.10.1 Shape–from–shading by deep neural networks 429 7.10.2 Shape–from–shading for estimating both illumination and depth 430 7.11 Integrating lighting estimates 433 7.11.1 Integrating one–dimensional lighting estimates 433 7.11.2 Integrating two–dimensional lighting estimates 436 7.12 Lighting analysis for dating depicted scenes 439 7.13 Summary 442 7.14 Bibliographical remarks 444 8 Object analysis 449 8.1 Introduction 449 8.2 Image–based object classification 452 8.2.1 Feature–based object recognition 452 8.3 Feature–based analysis of faces and bodies 454 8.3.1 Feature–based analysis of body pose 464 8.3.2 Feature–based analysis of head poses 466 8.4 Deep neural network–based object recognition 468 Jacques-Louis David 472 8.4.1 Transfer training 472 8.5 Summary 474 8.6 Bibliographical remarks 475 9 Style and composition analysis 477 9.1 Introduction 477 9.2 Automatic classification of style 480 9.3 Compositional balance 482 9.3.1 Computational balance of actors 485 9.4 Geometric properties of composition 486 9.4.1 Design in Piet Mondrian's Neoplastic paintings 487 Piet Mondrian 487 9.5 Analysis of trends and similarities in artistic style 497 9.5.1 Trends in landscape compositions 498 9.5.2 Large–scale trends in the development of style 502 9.5.3 Graph representations of stylistic similarities 503 9.6 Style transfer 505 9.6.1 Style transfer by deep networks 505 9.6.2 Rejuvenating tapestries 506 9.6.3 Coloration of black–and–white photographs of artworks 507 9.6.4 Style transfer for visualizing underdrawings 509 9.7 Recovering Rembrandt's complete The Night Watch 513 Rembrandt 514 9.8 Computational generation of images for art analysis 516 9.8.1 Computational recovery of lost artworks 518 9.9 Summary 521 9.10 Bibliographical remarks 522 10 Semantic analysis 525 10.1 Introduction 525 Jacques-Louis David 528 10.2 Semantics and visual art 534 10.2.1 Natural language processing and knowledge representation 536 10.3 Meaning through associations 538 10.3.1 Signifiers and signifieds 538 10.4 Semantics of color 544 10.5 Identifying saints by their attributes 546 Andrea del Verrocchio 549 10.6 Learning associations between signifiers and signifieds 550 Harmen Steenwijck 551 10.7 Meaning through artistic style 554 10.7.1 Context in the creation of meaning 556 10.8 Automatic image captioning and question answering 557 10.8.1 Image captioning 557 10.8.2 Automatic answering of questions about artworks 559 10.9 Meaning through shape relations and associations 563 Rogier van der Weyden 563 10.9.1 Recognizing meaning–bearing stories 565 Albrecht Dürer 567 10.10 Summary 568 10.11 Bibliographical remarks 569 Appendix 573 A Symbols, acronyms, and mathematical notation 573 A.1 Mathematical notation, definitions, and operations 573 A.2 Solving simultaneous linear equations 578 A.3 Lagrange optimization 579 A.4 Basis functions 580 A.5 Discrete Fourier analysis and synthesis 580 A.6 Discrete wavelet transform 582 A.7 Spherical harmonics 582 B Probability 584 B.1 Accuracy, precision, and recall 585 B.2 Conditional probability 585 B.3 The definition of information 586 B.4 Hidden Markov models (HMMs) 586 C Bayes' theorem and reasoning about uncertainty 588 C.1 Statistical independence 588 C.2 Maximum likelihood estimation 589 C.3 Bias and variance 591 C.4 Intersection over Union metric 592 D Deep neural networks 593 E Ray tracing and image formation in mirrors and lenses 596 E.1 Converging lenses 596 E.2 Diverging lenses 599 E.3 Mirrors 600 E.4 The focal length and radius of curvature of a spherical mirror 602 E.5 Spherical versus parabolic mirrors 603 F Resources 604 Epilog 607 Glossary 609 Bibliography 615 Figure credits 673 Timeline of artists 682 Index of artists 683 Index 687 About the book 713

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Book SynopsisThis book describes the principles of image and video compression techniques and introduces current and popular compression standards, such as the MPEG series. Derivations of relevant compression algorithms are developed in an easy-to-follow fashion. Numerous examples are provided in each chapter to illustrate the concepts.Table of ContentsPreface. 1 Introduction. 1.1 What is Source Coding? 1.2 Why is Compression Necessary? 1.3 Image and Video Compression Techniques. 1.4 Video Compression Standards. 1.5 Organization of the Book. 1.6 Summary. References. 2 Image Acquisition. 2.1 Introduction. 2.2 Sampling a Continuous Image. 2.3 Image Quantization. 2.4 Color Image Representation. 2.5 Summary. References. Problems. 3 Image Transforms. 3.1 Introduction. 3.2 Unitary Transforms. 3.3 Karhunen–Loeve Transform. 3.4 Properties of Unitary Transforms. 3.5 Summary. References. Problems. 4 Discrete Wavelet Transform. 4.1 Introduction. 4.2 Continuous Wavelet Transform. 4.3 Wavelet Series. 4.4 Discrete Wavelet Transform. 4.5 Efficient Implementation of 1D DWT. 4.6 Scaling and Wavelet Filters. 4.7 Two-Dimensional DWT. 4.8 Energy Compaction Property. 4.9 Integer or Reversible Wavelet. 4.10 Summary. References. Problems. 5 Lossless Coding. 5.1 Introduction. 5.2 Information Theory. 5.3 Huffman Coding. 5.4 Arithmetic Coding. 5.5 Golomb–Rice Coding. 5.6 Run–Length Coding. 5.7 Summary. References. Problems. 6 Predictive Coding. 6.1 Introduction. 6.2 Design of a DPCM. 6.3 Adaptive DPCM. 6.4 Summary. References. Problems. 7 Image Compression in the Transform Domain. 7.1 Introduction. 7.2 Basic Idea Behind Transform Coding. 7.3 Coding Gain of a Transform Coder. 7.4 JPEG Compression. 7.5 Compression of Color Images. 7.6 Blocking Artifact. 7.7 Variable Block Size DCT Coding. 7.8 Summary. References. Problems. 8 Image Compression in the Wavelet Domain. 8.1 Introduction. 8.2 Design of a DWT Coder. 8.3 Zero-Tree Coding. 8.4 JPEG2000. 8.5 Digital Cinema. 8.6 Summary. References. Problems. 9 Basics of Video Compression. 9.1 Introduction. 9.2 Video Coding. 9.3 Stereo Image Compression. 9.4 Summary. References. Problems. 10 Video Compression Standards. 10.1 Introduction. 10.2 MPEG-1 and MPEG-2 Standards. 10.3 MPEG-4. 10.4 H.264. 10.5 Summary. References. Problems. Index.

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Book SynopsisProgrammers, scientists, and engineers are always in need of newer techniques and algorithms to manipulate and interpret images. Algorithms for Image Processing and Computer Vision is an accessible collection of algorithms for common image processing applications that simplifies complicated mathematical calculations.Table of ContentsPreface xxi Chapter 1 Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls 1 OpenCV 2 The Basic OpenCV Code 2 The IplImage Data Structure 3 Reading and Writing Images 6 Image Display 7 An Example 7 Image Capture 10 Interfacing with the AIPCV Library 14 Website Files 18 References 18 Chapter 2 Edge-Detection Techniques 21 The Purpose of Edge Detection 21 Traditional Approaches and Theory 23 Models of Edges 24 Noise 26 Derivative Operators 30 Template-Based Edge Detection 36 Edge Models: The Marr-Hildreth Edge Detector 39 The Canny Edge Detector 42 The Shen-Castan (ISEF) Edge Detector 48 A Comparison of Two Optimal Edge Detectors 51 Color Edges 53 Source Code for the Marr-Hildreth Edge Detector 58 Source Code for the Canny Edge Detector 62 Source Code for the Shen-Castan Edge Detector 70 Website Files 80 References 82 Chapter 3 Digital Morphology 85 Morphology Defined 85 Connectedness 86 Elements of Digital Morphology — Binary Operations 87 Binary Dilation 88 Implementing Binary Dilation 92 Binary Erosion 94 Implementation of Binary Erosion 100 Opening and Closing 101 MAX — A High-Level Programming Language for Morphology 107 The ‘‘Hit-and-Miss’’ Transform 113 Identifying Region Boundaries 116 Conditional Dilation 116 Counting Regions 119 Grey-Level Morphology 121 Opening and Closing 123 Smoothing 126 Gradient 128 Segmentation of Textures 129 Size Distribution of Objects 130 Color Morphology 131 Website Files 132 References 135 Chapter 4 Grey-Level Segmentation 137 Basics of Grey-Level Segmentation 137 Using Edge Pixels 139 Iterative Selection 140 The Method of Grey-Level Histograms 141 Using Entropy 142 Fuzzy Sets 146 Minimum Error Thresholding 148 Sample Results From Single Threshold Selection 149 The Use of Regional Thresholds 151 Chow and Kaneko 152 Modeling Illumination Using Edges 156 Implementation and Results 159 Comparisons 160 Relaxation Methods 161 Moving Averages 167 Cluster-Based Thresholds 170 Multiple Thresholds 171 Website Files 172 References 173 Chapter 5 Texture and Color 177 Texture and Segmentation 177 A Simple Analysis of Texture in Grey-Level Images 179 Grey-Level Co-Occurrence 182 Maximum Probability 185 Moments 185 Contrast 185 Homogeneity 185 Entropy 186 Results from the GLCM Descriptors 186 Speeding Up the Texture Operators 186 Edges and Texture 188 Energy and Texture 191 Surfaces and Texture 193 Vector Dispersion 193 Surface Curvature 195 Fractal Dimension 198 Color Segmentation 201 Color Textures 205 Website Files 205 References 206 Chapter 6 Thinning 209 What Is a Skeleton? 209 The Medial Axis Transform 210 Iterative Morphological Methods 212 The Use of Contours 221 Choi/Lam/Siu Algorithm 224 Treating the Object as a Polygon 226 Triangulation Methods 227 Force-Based Thinning 228 Definitions 229 Use of a Force Field 230 Subpixel Skeletons 234 Source Code for Zhang-Suen/Stentiford/Holt Combined Algorithm 235 Website Files 246 References 247 Chapter 7 Image Restoration 251 Image Degradations — The Real World 251 The Frequency Domain 253 The Fourier Transform 254 The Fast Fourier Transform 256 The Inverse Fourier Transform 260 Two-Dimensional Fourier Transforms 260 Fourier Transforms in OpenCV 262 Creating Artificial Blur 264 The Inverse Filter 270 The Wiener Filter 271 Structured Noise 273 Motion Blur — A Special Case 276 The Homomorphic Filter — Illumination 277 Frequency Filters in General 278 Isolating Illumination Effects 280 Website Files 281 References 283 Chapter 8 Classification 285 Objects, Patterns, and Statistics 285 Features and Regions 288 Training and Testing 292 Variation: In-Class and Out-Class 295 Minimum Distance Classifiers 299 Distance Metrics 300 Distances Between Features 302 Cross Validation 304 Support Vector Machines 306 Multiple Classifiers — Ensembles 309 Merging Multiple Methods 309 Merging Type 1 Responses 310 Evaluation 311 Converting Between Response Types 312 Merging Type 2 Responses 313 Merging Type 3 Responses 315 Bagging and Boosting 315 Bagging 315 Boosting 316 Website Files 317 References 318 Chapter 9 Symbol Recognition 321 The Problem 321 OCR on Simple Perfect Images 322 OCR on Scanned Images — Segmentation 326 Noise 327 Isolating Individual Glyphs 329 Matching Templates 333 Statistical Recognition 337 OCR on Fax Images — Printed Characters 339 Orientation — Skew Detection 340 The Use of Edges 345 Handprinted Characters 348 Properties of the Character Outline 349 Convex Deficiencies 353 Vector Templates 357 Neural Nets 363 A Simple Neural Net 364 A Backpropagation Net for Digit Recognition 368 The Use of Multiple Classifiers 372 Merging Multiple Methods 372 Results From the Multiple Classifier 375 Printed Music Recognition — A Study 375 Staff Lines 376 Segmentation 378 Music Symbol Recognition 381 Source Code for Neural Net Recognition System 383 Website Files 390 References 392 Chapter 10 Content-Based Search — Finding Images by Example 395 Searching Images 395 Maintaining Collections of Images 396 Features for Query by Example 399 Color Image Features 399 Mean Color 400 Color Quad Tree 400 Hue and Intensity Histograms 401 Comparing Histograms 402 Requantization 403 Results from Simple Color Features 404 Other Color-Based Methods 407 Grey-Level Image Features 408 Grey Histograms 409 Grey Sigma — Moments 409 Edge Density — Boundaries Between Objects 409 Edge Direction 410 Boolean Edge Density 410 Spatial Considerations 411 Overall Regions 411 Rectangular Regions 412 Angular Regions 412 Circular Regions 414 Hybrid Regions 414 Test of Spatial Sampling 414 Additional Considerations 417 Texture 418 Objects, Contours, Boundaries 418 Data Sets 418 Website Files 419 References 420 Systems 424 Chapter 11 High-Performance Computing for Vision and Image Processing 425 Paradigms for Multiple-Processor Computation 426 Shared Memory 426 Message Passing 427 Execution Timing 427 Using clock() 428 Using QueryPerformanceCounter 430 The Message-Passing Interface System 432 Installing MPI 432 Using MPI 433 Inter-Process Communication 434 Running MPI Programs 436 Real Image Computations 437 Using a Computer Network — Cluster Computing 440 A Shared Memory System — Using the PC Graphics Processor 444 GLSL 444 OpenGL Fundamentals 445 Practical Textures in OpenGL 448 Shader Programming Basics 451 Vertex and Fragment Shaders 452 Required GLSL Initializations 453 Reading and Converting the Image 454 Passing Parameters to Shader Programs 456 Putting It All Together 457 Speedup Using the GPU 459 Developing and Testing Shader Code 459 Finding the Needed Software 460 Website Files 461 References 461 Index 465

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Book SynopsisDescribes the operating principles of analog MOS integrated circuits and how to design and use such circuits. The initial section explores general properties of analog MOS integrated circuits and the math and physics background required. The remainder of the book is devoted to the design of circuits.Table of ContentsTransformation Methods. MOS Devices as Circuit Elements. MOS Operational Amplifiers. Switched-Capacitor Filters. Nonfiltering Applications of Switched-Capacitor Circuits. Nonideal Effects in Switched-Capacitor Circuits. Systems Considerations and Applications. Index.

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Book SynopsisA proven, cost-effective approach to solving analog signal processing design problems Most design problems involving analog circuits require a great deal of creativity to solve. But, as the authors of this groundbreaking guide demonstrate, finding solutions to most analog signal processing problems does not have to be that difficult. Analog Signal Processing presents an original, five-step, design-oriented approach to solving analog signal processing problems using standard ICs as building blocks. Unlike most authors who prescribe a bottom-up approach, Professors Pallás-Areny and Webster cast design problems first in functional terms and then develop possible solutions using available ICs, focusing on circuit performance rather than internal structure. The five steps of their approach move from signal classification, definition of desired functions, and description of analog domain conversions to error classification and error analysis. Featuring 90 worked exTable of ContentsSignals and Signal Processing. Voltage Amplification. Current-to-Voltage and Voltage-to-Current Conversion. Linear Analog Functions. AC/DC Signal Conversion. Other Nonlinear Analog Functions. Analog Signal Filtering. Analog Signal Switching, Multiplexing and Sampling. Error Analysis and Reduction. Interference and Its Reduction. Noise, Drift and Their Reduction. Appendices. Index.

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Book SynopsisUsing a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping.Table of ContentsSTATE FEEDBACK. Design Tools for Stabilization. Adaptive Backstepping Design. Tuning Functions Design. Modular Design with Passive Identifiers. Modular Design with Swapping Identifiers. OUTPUT FEEDBACK. Output-Feedback Design Tools. Tuning Functions Designs. Modular Designs. Linear Systems. Appendices. Bibliography. Index.

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Book SynopsisA readable, understandable introduction to DSP for professionals and students alike... This practical guide is a welcome alternative to more complicated introductions to DSP.Table of ContentsThe Development of Digital Signal Processing. Why Do It Digitally Anyway? Converting Analog to Digital. Filtering. Transforming Signals into the Frequency Domain. Encoding of Waveforms-Increasing the Channel Bandwidth. Practical DSP Hardware Design Issues. DSP System Design Flow. Glossary of Acronyms. Index.

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Book SynopsisA practical guide to using the TMS320C31 DSP Starter Kit With applications and demand for high-performing digital signalprocessors expanding rapidly, it is becoming increasingly importantfor today''s students and practicing engineers to master real-timedigital signal processing (DSP) techniques. Digital Signal Processing: Laboratory Experiments Using C and theTMS320C31 DSK offers users a practical--and economicalm--approachto understanding DSP principles, designs, and applications.Demonstrating Texas Instruments'' (TI) state-of-the-art, low-pricedDSP Starter Kit (DSK), this book clearly illustrates and integratespractical aspects of real-time DSP implementation techniques andcomplex DSP concepts into lab exercises and experiments. TI''sTMS320C31 digital signal processor provides substantial performancebenefits for designs that have floating-point capabilitiessupported by high-level language compilers. Most chapters begin with a theoretical discussion followedTable of ContentsDigital Signal Processing Development System. Architecture and Instruction Set of the TMS320C3x Processor. Input and Output with the DSK. Finite Impulse Response Filters. Infinite Impulse Response Filters. Fast Fourier Transform. Adaptive Filters. DSP Applications and Projects. Appendices. References. Index.

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Book SynopsisAn up-to-date analysis of the SAR wavefront reconstruction signal theory and its digital implementation. With the advent of fast computing and digital information processing techniques, synthetic aperture radar (SAR) technology has become both more powerful and more accurate.Table of ContentsRange Imaging. Cross-Range Imaging. SAR Radiation Pattern. Generic Synthetic Aperture Radar. Spotlight Synthetic Aperture Radar. Stripmap Synthetic Aperture Radar. Circular Synthetic Aperture Radar. Monopulse Synthetic Aperture Radar. Bibliography. Index.

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Book SynopsisThis text is designed to give students a strong grounding in the mathematical basis of photogrammetry while introducing them to related fields, such as remote sensing and digital image processing.Suitable for undergraduate photogrammetry courses typically aimed at junior and senior students, and for graduate-level courses at the Master''s level. Excellent reference for those working in related fields.Table of Contents1 Introduction 1 2 Elementary Photogrammetry 13 3 Photogrammetric Sensing Systems 33 4 Mathematical Concepts in Photogrammetry 80 5 Resection, Intersection, and Triangulation 107 6 Digital Photogrammetry 152 7 Photogrammetric Instruments 203 8 Photogrammetric Products 225 9 Close-range Photogrammetry 247 10 Analysis of Multispectral and Hyperspectral Image Data 276 11 Active Sensing Systems 301 Appendix A: Mathematics for Photogrammetry 351 Appendix B: Least Squares Adjustment 387 Appendix C: Linearization of Photogrammetric Condition Equations 423 Appendix D: Mathematical Description of Linear Features 433 Appendix E: Further Consideration of the Rotation Matrix 446 Appendix F: Orbital Photogrammetry 455 Appendix G: Software of Photogrammetric Applications 464 Index 473

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Book SynopsisThis book responds to the dramatic growth in digital signal processing (DSP) over the past decade. While its focal point is signal modeling, the book integrates and explores the relationships of signal modeling to the important problems of optimal filtering, spectral estimation, and adaptive filtering.Table of ContentsBackground. Discrete-Time Random Processes. Signal Modeling. The Levinson Recursion. Lattice Filters. Wiener Filtering. Spectrum Estimation. Adaptive Filtering. Appendix. Table of Symbols. Index.

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Book SynopsisA concise introduction to the design and analysis of digital signal processors. Unique in its presentation of advanced topics at the undergraduate level. Contains excellent graphics and includes coverage of the A/D-digital filter and D/A structures of digital systems. Each chapter includes many carefully worked-out examples and concludes with a summary and problems.Table of ContentsFundamentals of Discrete-Time Systems. The Z-Transform. Analog Filter Design. Digital Filter Design. Realizations of Filters. The Discrete Fourier Transform.

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Book SynopsisWith the constant increase in applications involving image processing and multimedia procedures digital signal processing (DSP) is important for modern information engineering.Trade Review"The scope of this reference and tutorial is to introduce the algorithm basics of such processing...and new design strategies for filters in applications using spatial and frequency design constraints." (SciTech Book News Vol. 25, No. 2 June 2001)Table of ContentsContents. Preface. Introduction. Multidimensional Signals and Systems. Spatio-Temporal Scanning of Multidimensional Signals. Discrete Signals and Linear Systems. Elementary Filter Structures and the z-Tranform. Discrete Fourier Transform. Design of IIR Filters. Characteristics and Design of FIR Filters. Characteristics and Design of 2D FIR Filters for Video Signal Processing. Operators for Image Processing. Rank Order Filters. Bibliography. Index.

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Book SynopsisRandom Signals, Noise and Filtering develops the theory of random processes and its application to the study of systems and analysis of random data. The text covers three important areas: (1) fundamentals and examples of random process models, (2) applications of probabilistic models: signal detection, and filtering, and (3) statistical estimation--measurement and analysis of random data to determine the structure and parameter values of probabilistic models. This volume by Breipohl and Shanmugan offers the only one-volume treatment of the fundamentals of random process models, their applications, and data analysis.Table of ContentsPreface and Introduction. Review of Probability and Random Variables. Random Processes and Sequences. Response of Systems to Random Inputs. Special Classes of Random Processes. Signal Detection. Linear Minimum MSE Filtering. Statistics. Estimating Parameters of Random Processes from Data. Appendices.

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Book SynopsisThe use of synthetic aperture radar (SAR) represents a new era in remote sensing technology. A complete handbook for anyone who must design an SAR system capable of reliably producing high quality image data products, free from image artifacts and calibrated in terms of the target backscatter coefficient. Combines fundamentals underlying the SAR imaging process and the practical system engineering required to produce quality images from a real SAR system. Beginning with a broad overview of SAR technology, it goes on to examine SAR system capabilities and components and detail the techniques required for design and development of the SAR ground data system with emphasis on the correlation processing. Intended for SAR system engineers and researchers, it is generously illustrated for maximum clarity.Table of ContentsThe Radar Equation. The Matched Filter and Pulse Compression. Imaging and the Rectangular Algorithm. Ancillary Processes in Image Formation. SAR Flight System. Radiometric Calibration of SAR Data. Geometric Calibration of SAR Data. The SAR Ground System. Other Imaging Algorithms. Appendices. List of Acronyms. Index.

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Book SynopsisA topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.Table of ContentsMathematical Preliminaries of Neurocomputing. Architectures and Electronic Implementation of Neural Network Models. Unconstrained Optimization and Learning Algorithms. Neural Networks for Linear, Quadratic Programming and Linear Complementarity Problems. A Neural Network Approach to the On-Line Solution of a System of Linear Algebraic Equations and Related Problems. Neural Networks for Matrix Algebra Problems. Neural Networks for Continuous, Nonlinear, Constrained Optimization Problems. Neural Networks for Estimation, Identification and Prediction. Neural Networks for Discrete and Combinatorial Optimization Problems. Appendices. Subject Index.

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Book SynopsisDigital signal processing (DSP) covers a wide range of applications such as signal acquisition, analysis, transmission, storage, and synthesis. Special attention is needed for the VLSI (very large scale integration) implementation of high performance DSP systems with examples from video and radar applications.Table of ContentsBasic CMOS Circuits. Implementation of Fundamental Operations. Measures for Increasing Performance. Array Processor Architectures. Filter Structures. Implementations of the Discrete Fourier Transform. Programmable Digital Signal Processors. Multiprocessor Systems. Implementation Strategies. References. Index.

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Book SynopsisIntroductory Digital Signal Processing with Computer Applications Second Edition Paul A. Lynn formerly: Imperial College of Science, Technology and Medicine, London, UK and Wolfgang Fuerst United Nations, New York, USA An excellent introductory book (Review of the First Edition in the International Journal of Electrical Engineering Education) .Table of ContentsPreface to the First Edition ix Preface to the Second Edition xiii 1 Introduction 1 2 Time-Domain Analysis 32 3 Frequency-Domain Analysis: The Discrete Fourier Series and the Fourier Transform 65 4 Frequency-Domain Analysis: the z-Transform 98 5 Design of Nonrecursive Digital Filters 132 6 Design of Recursive Digital Filters 167 7 The Discrete and Fast Fourier Transforms 211 8 FFT Processing 251 9 Random Digital Signals 284 10 Random DSP 316 Appendix A1 Computer Programs 351 Appendix A2 Continuous-time Fourier Analysis 433 Answers to Selected Problems 467 Bibliography 473 Index 475

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Book SynopsisSignal analysis gives an insight into the properties of signals and stochastic processes by methodology. Linear transforms are integral to the continuing growth of signal processes as they characterize and classify signals. In particular, those transforms that provide time-frequency signal analysis are attracting greater numbers of researchers and are becoming an area of considerable importance. The key characteristic of these transforms, along with a certain time-frequency localization called the wavelet transform and various types of multirate filter banks, is their high computational efficiency. It is this computational efficiently which accounts for their increased application. This book provides a complete overview and introduction to signal analysis. It presents classical and modern signal analysis methods in a sequential structure starting with the background to signal theory. Progressing through the book the author introduces more advanced topics in an easy to understand style.Trade Review"...excellent and interesting reading for digital signal processing engineers and designers and for postgraduate students in electrical and computer faculties." (Mathematical Reviews, 2002d)Table of ContentsSignals and Signal Spaces. Integral Signal Representations. Discrete Signal Representations. Examples of Discrete Transforms. Transforms and Filters for Stochastic Processes. Filter Banks. Short-Time Fourier Analysis. Wavelet Transform. Non-Linear Time-Frequency Distributions. Bibliography. Index.

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