Computer vision Books
De Gruyter Artificial Intelligence for Virtual Reality
Book SynopsisThis book explores the possible applications of Artificial Intelligence in Virtual environments. These were previously mainly associated with gaming, but have largely extended their area of application, and are nowadays used for promoting collaboration in work environments, for training purposes, for management of anxiety and pain, etc.. The development of Artificial Intelligence has given new dimensions to the research in this field.
£117.80
Springer International Publishing AG Data Preprocessing in Data Mining
Book SynopsisData Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.Trade ReviewFrom the book reviews:“This book is a comprehensive collection of data preprocessing techniques used in data mining. Any readers who practice data mining will find it beneficial … . This book is an excellent guideline in the topic of data preprocessing for data mining. It is suitable for both practitioners and researchers who would like to use datasets in their data mining projects.” (Xiannong Meng, Computing Reviews, December, 2014)Table of ContentsIntroduction.- Data Sets and Proper Statistical Analysis of Data Mining Techniques.- Data Preparation Basic Models.- Dealing with Missing Values.- Dealing with Noisy Data.- Data Reduction.- Feature Selection.- Instance Selection.- Discretization.- A Data Mining Software Package Including Data Preparation and Reduction: KEEL.
£151.99
John Wiley & Sons Inc Handbook of Machine and Computer Vision: The Guide for Developers and Users
Book SynopsisThe second edition of this accepted reference work has been updated to reflect the rapid developments in the field and now covers both 2D and 3D imaging. Written by expert practitioners from leading companies operating in machine vision, this one-stop handbook guides readers through all aspects of image acquisition and image processing, including optics, electronics and software. The authors approach the subject in terms of industrial applications, elucidating such topics as illumination and camera calibration. Initial chapters concentrate on the latest hardware aspects, ranging from lenses and camera systems to camera-computer interfaces, with the software necessary discussed to an equal depth in later sections. These include digital image basics as well as image analysis and image processing. The book concludes with extended coverage of industrial applications in optics and electronics, backed by case studies and design strategies for the conception of complete machine vision systems. As a result, readers are not only able to understand the latest systems, but also to plan and evaluate this technology. With more than 500 images and tables to illustrate relevant principles and steps.Trade Review[The editor] has compiled a wealth of information that addresses topics at both practical and theoretical levels. The book covers areas such as general system-design principles, lighting, optics, camera systems, computer interfaces, and algorithms. ...Section nine, "Machine Vision in Manufacturing," ...provides a solid introduction to what vision systems can do and the problems they can solve. Designers will ...find much useful information in the section "Lighting in Machine Vision." This chapter provides just the type of explanations and illustrations that help engineers properly plan and assemble light sources for an application. If you still perceive lighting techniques to be black magic, turn to this chapter for advice. I cannot think of another source that offers as much practical information about lighting. This reference book provides many helpful diagrams and photographs that illustrate how algorithms work, the results of lighting components in various ways, and how camera systems operate. UBM Tech 2006Table of ContentsPreface Second Edition xxiii Preface First Edition xxv List of Contributors xxvii 1 Processing of Information in the Human Visual System 1 Frank Schaeffel 1.1 Preface 1 1.2 Design and Structure of the Eye 1 1.3 Optical Aberrations and Consequences for Visual Performance 3 1.4 Chromatic Aberration 10 1.5 Neural Adaptation to Monochromatic Aberrations 11 1.6 Optimizing Retinal Processing with Limited Cell Numbers, Space, and Energy 11 1.7 Adaptation to Different Light Levels 12 1.8 Rod and Cone Responses 14 1.9 Spiking and Coding 16 1.10 Temporal and Spatial Performance 17 1.11 ON/OFF Structure, Division of the Whole Illuminance Amplitude 18 1.12 Consequences of the Rod and Cone Diversity on Retinal Wiring 18 1.13 Motion Sensitivity in the Retina 19 1.14 Visual Information Processing in Higher Centers 20 1.14.1 Morphology 21 1.14.2 Functional Aspects – Receptive Field Structures and Cortical Modules 22 1.15 Effects of Attention 23 1.16 Color Vision, Color Constancy, and Color Contrast 23 1.17 Depth Perception 25 1.18 Adaptation in the Visual System to Color, Spatial, and Temporal Contrast 26 1.19 Conclusions 26 Acknowledgements 28 References 28 2 Introduction to Building a Machine Vision Inspection 31 Axel Telljohann 2.1 Preface 31 2.2 Specifying a Machine Vision System 32 2.2.1 Task and Benefit 32 2.2.2 Parts 33 2.2.2.1 Different Part Types 33 2.2.3 Part Presentation 33 2.2.4 Performance Requirements 34 2.2.4.1 Accuracy 34 2.2.4.2 Time Performance 34 2.2.5 Information Interfaces 34 2.2.6 Installation Space 35 2.2.7 Environment 35 2.2.8 Checklist 35 2.3 Designing a Machine Vision System 36 2.3.1 Camera Type 36 2.3.2 Field of View 37 2.3.3 Resolution 38 2.3.3.1 Camera Sensor Resolution 38 2.3.3.2 Spatial Resolution 38 2.3.3.3 Measurement Accuracy 38 2.3.3.4 Calculation of Resolution 39 2.3.3.5 Resolution for a Line Scan Camera 39 2.3.4 Choice of Camera, Frame Grabber, and Hardware Platform 40 2.3.4.1 Camera Model 40 2.3.4.2 Frame Grabber 40 2.3.4.3 Pixel Rate 40 2.3.4.4 Hardware Platform 41 2.3.5 Lens Design 41 2.3.5.1 Focal Length 42 2.3.5.2 Lens Flange Focal Distance 43 2.3.5.3 Extension Tubes 43 2.3.5.4 Lens Diameter and Sensor Size 43 2.3.5.5 Sensor Resolution and Lens Quality 43 2.3.6 Choice of Illumination 44 2.3.6.1 Concept: Maximize Contrast 44 2.3.6.2 Illumination Setups 44 2.3.6.3 Light Sources 45 2.3.6.4 Approach to the Optimum Setup 45 2.3.6.5 Interfering Lighting 46 2.3.7 Mechanical Design 46 2.3.8 Electrical Design 46 2.3.9 Software 46 2.3.9.1 Software Library 47 2.3.9.2 Software Structure 47 2.3.9.3 General Topics 48 2.4 Costs 48 2.5 Words on Project Realization 49 2.5.1 Development and Installation 49 2.5.2 Test Run and Acceptance Test 49 2.5.3 Training and Documentation 50 2.6 Examples 50 2.6.1 Diameter Inspection of Rivets 50 2.6.1.1 Task 50 2.6.1.2 Specification 51 2.6.1.3 Design 51 2.6.2 Tubing Inspection 55 2.6.2.1 Task 55 2.6.2.2 Specification 55 2.6.2.3 Design 56 3 Lighting in Machine Vision 63 Irmgard Jahr 3.1 Introduction 63 3.1.1 Prologue 63 3.1.2 The Involvement of Lighting in the Complex Machine Vision Solution 63 3.2 Demands on Machine Vision lighting 67 3.3 Light used in Machine Vision 70 3.3.1 What is Light? Axioms of Light 70 3.3.2 Light and Light Perception 73 3.3.3 Light Sources for Machine Vision 76 3.3.3.1 Incandescent Lamps/Halogen Lamps 77 3.3.3.2 Metal Vapor Lamps 78 3.3.3.3 Xenon Lamps 79 3.3.3.4 Fluorescent Lamps 81 3.3.3.5 LEDs (Light Emitting Diodes) 82 3.3.3.6 Lasers 85 3.3.4 The Light Sources in Comparison 86 3.3.5 Considerations for Light Sources: Lifetime, Aging, Drift 86 3.3.5.1 Lifetime 86 3.3.5.2 Aging and Drift 88 3.4 Interaction of Test Object and Light 91 3.4.1 Risk Factor Test Object 91 3.4.1.1 What Does the Test Object do With the Incoming Light? 92 3.4.1.2 Reflection/Reflectance/Scattering 92 3.4.1.3 Total Reflection 95 3.4.1.4 Transmission/Transmittance 96 3.4.1.5 Absorption/Absorbance 97 3.4.1.6 Diffraction 99 3.4.1.7 Refraction 100 3.4.2 Light Color and Part Color 101 3.4.2.1 Visible Light (VIS) – Monochromatic Light 101 3.4.2.2 Visible Light (VIS) – White Light 103 3.4.2.3 Infrared Light (IR) 104 3.4.2.4 Ultraviolet (UV) Light 106 3.4.2.5 Polarized Light 107 3.5 Basic Rules and Laws of Light Distribution 109 3.5.1 Basic Physical Quantities of Light 110 3.5.2 The Photometric Inverse Square Law 111 3.5.3 The Constancy of Luminance 113 3.5.4 What Light Arrives at the Sensor – Light Transmission Through the Lens 114 3.5.5 Light Distribution of Lighting Components 115 3.5.6 Contrast 118 3.5.7 Exposure 120 3.6 Light Filters 121 3.6.1 Characteristic Values of Light Filters 121 3.6.2 Influences of Light Filters on the Optical Path 123 3.6.3 Types of Light Filters 124 3.6.4 Anti-Reflective Coatings (AR) 126 3.6.5 Light Filters for Machine Vision 127 3.6.5.1 UV Blocking Filter 127 3.6.5.2 Daylight Suppression Filter 128 3.6.5.3 IR Suppression Filter 128 3.6.5.4 Neutral Filter/Neutral Density Filter/Gray Filter 129 3.6.5.5 Polarization Filter 130 3.6.5.6 Color Filters 130 3.6.5.7 Filter Combinations 131 3.7 Lighting Techniques and Their Use 131 3.7.1 How to Find a Suitable Lighting? 131 3.7.2 Planning the Lighting Solution – Influence Factors 133 3.7.3 Lighting Systematics 135 3.7.3.1 Directional Properties of the Light 135 3.7.3.2 Arrangement of the Lighting 138 3.7.3.3 Properties of the Illuminated Field 138 3.7.4 The Lighting Techniques in Detail 140 3.7.4.1 Diffuse Bright Field Incident Light (No. 1, Table 3.14) 140 3.7.4.2 Directed Bright Field Incident Light (No. 2, Table 3.14) 142 3.7.4.3 Telecentric Bright Field Incident Light (No. 3, Table 3.14) 143 3.7.4.4 Structured Bright Field Incident Light (No. 4, Table 3.14) 145 3.7.4.5 Diffuse Directed Partial Bright Field Incident Light (Nos. 1 and 2, Table 3.14) 148 3.7.4.6 Diffuse/Directed Dark Field Incident Light (Nos. 5 and 6, Table 3.14) 152 3.7.4.7 The Limits of the Incident Lighting 154 3.7.4.8 Diffuse Bright Field Transmitted Lighting (No. 7, Table 3.14) 155 3.7.4.9 Directed Bright Field Transmitted Lighting (No. 8, Table 3.14) 157 3.7.4.10 Telecentric Bright Field Transmitted Lighting (No. 9, Table 3.14) 158 3.7.4.11 Diffuse/Directed Transmitted Dark Field Lighting (Nos. 10 and 11, Table 3.14) 161 3.7.5 Combined Lighting Techniques 162 3.8 Lighting Control 163 3.8.1 Reasons for Light Control – The Environmental Industrial Conditions 164 3.8.2 Electrical Control 164 3.8.2.1 Stable Operation 164 3.8.2.2 Brightness Control 166 3.8.2.3 Temporal Control: Static-Pulse-Flash 167 3.8.2.4 Some Considerations for the Use of Flash Light 168 3.8.2.5 Temporal and Local Control: Adaptive Lighting 171 3.8.3 Geometrical Control 173 3.8.3.1 Lighting from Large Distances 173 3.8.3.2 Light Deflection 175 3.8.4 Suppression of Ambient and Extraneous Light – Measures for a Stable Lighting 175 3.9 Lighting Perspectives for the Future 176 References 177 4 Optical Systems in Machine Vision 179 Karl Lenhardt 4.1 A Look at the Foundations of Geometrical Optics 179 4.1.1 From Electrodynamics to Light Rays 179 4.1.2 Basic Laws of Geometrical Optics 181 4.2 Gaussian Optics 183 4.2.1 Reflection and Refraction at the Boundary between two Media 183 4.2.2 Linearizing the Law of Refraction – The Paraxial Approximation 185 4.2.3 Basic Optical Conventions 186 4.2.3.1 Definitions for Image Orientations 186 4.2.3.2 Definition of the Magnification Ratio β 186 4.2.3.3 Real and Virtual Objects and Images 187 4.2.3.4 Tilt Rule for the Evaluation of Image Orientations by Reflection 188 4.2.4 Cardinal Elements of a Lens in Gaussian Optics 189 4.2.4.1 Focal Lengths f and f ′ 192 4.2.4.2 Convention 192 4.2.5 Thin Lens Approximation 193 4.2.6 Beam-Converging and Beam-Diverging Lenses 193 4.2.7 Graphical Image Constructions 195 4.2.7.1 Beam-Converging Lenses 195 4.2.7.2 Beam-Diverging Lenses 195 4.2.8 Imaging Equations and Their Related Coordinate Systems 195 4.2.8.1 Reciprocity Equation 196 4.2.8.2 Newton’s Equations 197 4.2.8.3 General Imaging Equation 198 4.2.8.4 Axial Magnification Ratio 200 4.2.9 Overlapping of Object and Image Space 200 4.2.10 Focal Length, Lateral Magnification, and the Field of View 200 4.2.11 Systems of Lenses 202 4.2.12 Consequences of the Finite Extension of Ray Pencils 205 4.2.12.1 Effects of Limitations of the Ray Pencils 205 4.2.12.2 Several Limiting Openings 207 4.2.12.3 Characterizing the Limits of Ray Pencils 210 4.2.12.4 Relation to the Linear Camera Model 212 4.2.13 Geometrical Depth of Field and Depth of Focus 214 4.2.13.1 Depth of Field as a Function of the Object Distance p 215 4.2.13.2 Depth of Field as a Function of β 216 4.2.13.3 Hyperfocal Distance 217 4.2.13.4 Permissible Size for the Circle of Confusion d ′ 218 4.2.14 Laws of Central Projection–Telecentric System 219 4.2.14.1 Introduction to the Laws of Perspective 219 4.2.14.2 Central Projection from Infinity – Telecentric Perspective 228 4.3 Wave Nature of Light 235 4.3.1 Introduction 235 4.3.2 Rayleigh–Sommerfeld Diffraction Integral 236 4.3.3 Further Approximations to the Huygens–Fresnel Principle 238 4.3.3.1 Fresnel’s Approximation 239 4.3.4 Impulse Response of an Aberration-Free Optical System 241 4.3.4.1 Case of Circular Aperture, Object Point on the Optical Axis 243 4.3.5 Intensity Distribution in the Neighborhood of the Geometrical Focus 244 4.3.5.1 Special Cases 246 4.3.6 Extension of the Point Spread Function in a Defocused Image Plane 248 4.3.7 Consequences for the Depth of Field Considerations 249 4.3.7.1 Diffraction and Permissible Circle of Confusion 249 4.3.7.2 Extension of the Point Spread Function at the Limits of the Depth of Focus 250 4.3.7.3 Useful Effective f -Number 251 4.4 Information Theoretical Treatment of Image Transfer and Storage 252 4.4.1 Physical Systems as Linear Invariant Filters 252 4.4.1.1 Invariant Linear Systems 255 4.4.1.2 Note to the Representation of Harmonic Waves 259 4.4.2 Optical Transfer Function (OTF) and the Meaning of Spatial Frequency 260 4.4.2.1 Note on the Relation Between the Elementary Functions in the Two Representation Domains 261 4.4.3 Extension to the Two-Dimensional Case 261 4.4.3.1 Interpretation of Spatial Frequency Components (r, s) 261 4.4.3.2 Reduction to One-Dimensional Representations 262 4.4.4 Impulse Response and MTF for Semiconductor Imaging Devices 265 4.4.5 Transmission Chain 267 4.4.6 Aliasing Effect and the Space-Variant Nature of Aliasing 267 4.4.6.1 Space-Variant Nature of Aliasing 274 4.5 Criteria for Image Quality 277 4.5.1 Gaussian Data 277 4.5.2 Overview on Aberrations of the Third Order 277 4.5.2.1 Monochromatic Aberrations of the Third Order (Seidel Aberrations) 278 4.5.2.2 Chromatic Aberrations 278 4.5.3 Image Quality in the Space Domain: PSF, LSF, ESF, and Distortion 278 4.5.3.1 Distortion 280 4.5.4 Image Quality in the Spatial Frequency Domain: MTF 281 4.5.4.1 Parameters that Influence the Modulation Transfer Function 282 4.5.5 Other Image Quality Parameters 283 4.5.5.1 Relative Illumination (Relative Irradiance) 283 4.5.5.2 Deviation from Telecentricity (for Telecentric Lenses only) 284 4.5.6 Manufacturing Tolerances and Image Quality 284 4.5.6.1 Measurement Errors due to Mechanical Inaccuracies of the Camera System 285 4.6 Practical Aspects: How to Specify Optics According to the Application Requirements? 285 4.6.1 Example for the Calculation of an Imaging Constellation 287 References 289 5 Camera Calibration 291 Robert Godding 5.1 Introduction 291 5.2 Terminology 292 5.2.1 Camera, Camera System 292 5.2.2 Coordinate Systems 292 5.2.3 Interior Orientation and Calibration 293 5.2.4 Exterior and Relative Orientation 293 5.2.5 System Calibration 293 5.3 Physical Effects 293 5.3.1 Optical System 293 5.3.2 Camera and Sensor Stability 294 5.3.3 Signal Processing and Transfer 294 5.4 Mathematical Calibration Model 295 5.4.1 Central Projection 295 5.4.2 Camera Model 295 5.4.3 Focal Length and Principal Point 297 5.4.4 Distortion and Affinity 297 5.4.5 Radial Symmetrical Distortion 297 5.4.6 Radial Asymmetrical and Tangential Distortion 299 5.4.7 Affinity and Nonorthogonality 299 5.4.8 Variant Camera Parameters 299 5.4.9 Sensor Flatness 301 5.4.10 Other Parameters 301 5.5 Calibration and Orientation Techniques 302 5.5.1 In the Laboratory 302 5.5.2 Using Bundle Adjustment to Determine Camera Parameters 302 5.5.2.1 Calibration Based Exclusively on Image Information 302 5.5.2.2 Calibration and Orientation with Additional Object Information 304 5.5.2.3 Extended System Calibration 307 5.5.3 Other Techniques 307 5.6 Verification of Calibration Results 308 5.7 Applications 309 5.7.1 Applications with Simultaneous Calibration 309 5.7.2 Applications with Precalibrated Cameras 311 5.7.2.1 Tube Measurement within a Measurement Cell 311 5.7.2.2 Online Measurements in the Field of Car Safety 312 5.7.2.3 High Resolution 3D Scanning with White Light Scanners 312 5.7.2.4 Other Applications 313 References 314 6 Camera Systems in Machine Vision 317 Horst Mattfeldt 6.1 Camera Technology 317 6.1.1 History in Brief 317 6.1.2 Machine Vision versus Closed Circuit TeleVision (CCTV) 317 6.2 Sensor Technologies 319 6.2.1 Spatial Differentiation: 1D and 2D 319 6.2.2 CCD Technology 320 6.2.2.1 Interline Transfer 321 6.2.2.2 Progressive Scan Interline Transfer 321 6.2.2.3 Interlaced Scan Readout 322 6.2.2.4 Enhancing Frame Rate by Multitap Sensors 324 6.2.2.5 SONY HAD Technology 325 6.2.2.6 SONY SuperHAD (II) and ExViewHAD (II) Technology 325 6.2.2.7 CCD Image Artifacts 326 6.2.2.8 Blooming 326 6.2.2.9 Smear 326 6.2.3 CMOS Image Sensor 328 6.2.3.1 Advantages of CMOS Sensor 328 6.2.3.2 CMOS Sensor Shutter Concepts 331 6.2.3.3 Performance Comparison of CMOS versus CCD 336 6.2.3.4 Integration Complexity of CCD versus CMOS Camera Technology 336 6.2.3.5 CMOS Sensor Sensitivity Enhancements 337 6.2.4 MATRIX VISION Available Cameras 338 6.2.4.1 Why So Many Different Models? How to Choose Among These? 338 6.2.4.2 Resolution and Video Standards 338 6.2.4.3 Sensor Sizes and Dimensions 344 6.3 Block Diagrams and Their Description 344 6.3.1 Block Diagram of SONY Progressive Scan Analog Camera 345 6.3.1.1 CCD Read Out Clocks 345 6.3.1.2 CCD Binning Mode 345 6.3.1.3 Spectral Sensitivity 348 6.3.1.4 Analog Signal Processing 348 6.3.1.5 Camera and Frame Grabber 350 6.3.2 Block Diagram of Color Camera with Digital Image Processing 350 6.3.2.1 Bayer TM Complementary Color Filter Array 351 6.3.2.2 Complementary Color Filters Spectral Sensitivity 351 6.3.2.3 Generation of Color Signals 351 6.4 mvBlueCOUGAR-X Line of Cameras 354 6.4.1 Black and White Digital Camera mvBlueCOUGAR-X Camera Series 355 6.4.1.1 Gray Level Sensor and Processing 355 6.4.2 Color Camera mvBlueCOUGAR-X Family 356 6.4.2.1 Analog Processing 356 6.4.2.2 Analog Front End (AFE) 357 6.4.2.3 A/D Conversion 357 6.4.2.4 One-Chip Color Processing 359 6.4.2.5 Inputting Time Stamp Data into Data Stream 361 6.4.2.6 Statistics Engine for White Balance and Auto Features 361 6.4.2.7 Image Memory 361 6.4.2.8 Lookup Table (LUT) and Gamma Function 362 6.4.2.9 Shading Correction 365 6.4.2.10 Reducing Noise by Adaptive Recursive Frame Averaging 366 6.4.2.11 Color Interpolation 367 6.4.2.12 Color Correction 368 6.4.2.13 RGB → YUV Conversion 370 6.4.3 Controlling Image Capture 371 6.4.4 Acquisition and Trigger Modes 371 6.4.4.1 Sequencer 374 6.4.4.2 Latency and Jitter Aspects 375 6.4.4.3 Action Commands 375 6.4.4.4 Scheduled Action Command 377 6.4.5 Data Transmission 377 6.4.5.1 GigE Vision and GVSP 378 6.4.5.2 USB3 Vision 380 6.4.6 Pixel Data 380 6.4.7 Camera Connection 381 6.4.8 Operating the Camera 381 6.4.9 HiRose Jack Pin Assignment 382 6.4.10 Sensor Frame Rates and Bandwidth 382 6.5 Configuration of a GigE Vision Camera 384 6.6 Qualifying Cameras and Noise Measurement (Dr. Gert Ferrano Mv) 386 6.6.1 Explanation of the Most Important Measurements 388 6.6.1.1 Linearity Curve 388 6.6.1.2 Photon Transfer Curve 388 6.7 Camera Noise (by Henning Haider AVT, Updated by Author) 391 6.7.1 Photon Noise 391 6.7.2 Dark Current Noise 391 6.7.3 Fixed Pattern Noise (FPN) 392 6.7.4 Photo Response Non Uniformity (PRNU) 392 6.7.5 Reset Noise 392 6.7.6 1/f Noise (Amplifier Noise) 392 6.7.7 Quantization Noise 392 6.7.8 Noise Floor 393 6.7.9 Dynamic Range 393 6.7.10 Signal to Noise Ratio 393 6.7.11 Example 1: SONY IMX-174 Sensor (mvBlueFOX3-2024) 394 6.7.12 Example 2: CMOSIS CMV2000 (mvBlueCOUGAR-X104) 394 6.8 Useful Links and Literature 394 6.9 Digital Interfaces 395 7 Smart Camera and Vision Systems Design 399 Howard D. Gray and Nate Holmes 7.1 Introduction to Vision System Design 399 7.2 Definitions 400 7.3 Smart Cameras 403 7.3.1 Applications 403 7.3.2 Component Parts 404 7.3.2.1 Processors 404 7.3.2.2 FPGA Processing 406 7.3.2.3 Memory and Storage 407 7.3.2.4 Operating Systems 408 7.3.2.5 Image Sensors 409 7.3.2.6 Inputs and Outputs 410 7.3.2.7 Other Interfaces 412 7.3.2.8 Timers and Counters 413 7.3.3 Programming and Configuring 413 7.3.3.1 Scripting 413 7.3.3.2 High-Level Languages 414 7.3.3.3 Third-Party Tools 416 7.3.4 Environment 416 7.3.4.1 Power Dissipation 416 7.3.4.2 Ingress Protection 417 7.4 Vision Sensors 418 7.4.1 Applications 419 7.4.2 Component Parts 420 7.4.3 Programming and Configuring 420 7.4.4 Environment 421 7.5 Embedded Vision Systems 421 7.5.1 Applications 424 7.5.1.1 Multi-Camera Applications 424 7.5.1.2 Closed Loop Control Applications 424 7.5.2 Component Parts 425 7.5.3 Programming and Configuring 425 7.5.4 Environment 425 7.6 Conclusion 425 References 426 Further Reading 429 8 Camera Computer Interfaces 431 Nate Holmes 8.1 Overview 431 8.2 Camera Buses 432 8.2.1 Software Standards 433 8.2.1.1 GenICam 433 8.2.1.2 Iidc 2 434 8.2.2 Analog Camera Buses (Legacy) 435 8.2.2.1 Analog Video Signal 436 8.2.2.2 Interlaced Video 436 8.2.2.3 Progressive Scan Video 436 8.2.2.4 Timing Signals 437 8.2.2.5 Analog Image Acquisition 437 8.2.2.6 S-Video 438 8.2.2.7 Rgb 438 8.2.2.8 Analog Connectors 439 8.2.3 Parallel Digital Camera Buses (Legacy) 439 8.2.3.1 Digital Video Transmission 439 8.2.3.2 Taps 440 8.2.3.3 Differential Signaling 441 8.2.3.4 Line Scan 441 8.2.3.5 Parallel Digital Connectors 441 8.2.4 IEEE 1394 (FireWire) (Legacy) 442 8.2.4.1 IEEE 1394 for Machine Vision 445 8.2.5 Camera Link 449 8.2.5.1 Camera Link Signals 450 8.2.5.2 Camera Link Connectors 451 8.2.6 Camera Link HS 451 8.2.7 CoaXPress 452 8.2.8 USB (USB3 Vision) 452 8.2.8.1 USB for Machine Vision 454 8.2.9 Gigabit Ethernet (GigE Vision) 455 8.2.9.1 Gigabit Ethernet for Machine Vision 456 8.2.9.2 GigE Vision Device Discovery 456 8.2.9.3 GigE Vision Control Protocol (GVCP) 456 8.2.9.4 GenICam 457 8.2.9.5 GigE Vision Stream Protocol (GVSP) 457 8.2.9.6 Packet Loss and Resends 457 8.2.10 Future Standards Development 458 8.3 Choosing a Camera Bus 459 8.3.1 Bandwidth 459 8.3.2 Resolution 459 8.3.3 Frame Rate 460 8.3.4 Cables 460 8.3.5 Line Scan 460 8.3.6 Reliability 460 8.3.7 Summary of Camera Bus Specifications 461 8.3.8 Sample Use Cases 461 8.3.8.1 Manufacturing Inspection 461 8.3.8.2 LCD Inspection 462 8.3.8.3 Security 463 8.4 Computer Buses 463 8.4.1 Isa/eisa 463 8.4.2 PCI/CompactPCI/PXI 464 8.4.3 Pci-x 466 8.4.4 PCI Express/CompactPCI Express/PXI Express 467 8.4.5 Throughput 469 8.4.6 Prevalence and Lifetime 471 8.4.6.1 Cost 471 8.5 Choosing a Computer Bus 471 8.5.1 Determine Throughput Requirements 471 8.5.2 Applying the Throughput Requirements 473 8.6 Driver Software 473 8.6.1 Application Programming Interface 475 8.6.2 Supported Platforms 477 8.6.3 Performance 477 8.6.4 Utility Functions 478 8.6.5 Acquisition Mode 479 8.6.5.1 Snap 479 8.6.5.2 Grab 479 8.6.5.3 Sequence 480 8.6.5.4 Ring 481 8.6.6 Image Representation 482 8.6.6.1 Image Representation in Memory 482 8.6.7 Bayer Color Encoding 485 8.6.7.1 Image Representation on Disk 487 8.6.8 Image Display 487 8.6.8.1 Understanding Display Modes 488 8.6.8.2 Palettes 489 8.6.8.3 Nondestructive Overlays 490 8.7 Features of a Machine Vision System 491 8.7.1 Image Reconstruction 491 8.7.2 Timing and Triggering 492 8.7.3 Memory Handling 494 8.7.4 Additional Features 496 8.7.4.1 Look-Up Tables 497 8.7.4.2 Region of Interest 499 8.7.4.3 Color Space Conversion 499 8.7.4.4 Shading Correction 501 8.8 Summary 501 References 502 9 Machine Vision Algorithms 505 Carsten Steger 9.1 Fundamental Data Structures 505 9.1.1 Images 505 9.1.2 Regions 506 9.1.3 Subpixel-Precise Contours 508 9.2 Image Enhancement 509 9.2.1 Gray Value Transformations 509 9.2.2 Radiometric Calibration 512 9.2.3 Image Smoothing 517 9.2.4 Fourier Transform 528 9.3 Geometric Transformations 532 9.3.1 Affine Transformations 532 9.3.2 Projective Transformations 533 9.3.3 Image Transformations 534 9.3.4 Polar Transformations 538 9.4 Image Segmentation 540 9.4.1 Thresholding 540 9.4.2 Extraction of Connected Components 548 9.4.3 Subpixel-Precise Thresholding 550 9.5 Feature Extraction 552 9.5.1 Region Features 552 9.5.2 Gray Value Features 556 9.5.3 Contour Features 559 9.6 Morphology 560 9.6.1 Region Morphology 561 9.6.2 Gray Value Morphology 575 9.7 Edge Extraction 579 9.7.1 Definition of Edges in One and Two Dimensions 579 9.7.2 1D Edge Extraction 583 9.7.3 2D Edge Extraction 589 9.7.4 Accuracy of Edges 596 9.8 Segmentation and Fitting of Geometric Primitives 602 9.8.1 Fitting Lines 603 9.8.2 Fitting Circles 607 9.8.3 Fitting Ellipses 608 9.8.4 Segmentation of Contours into Lines, Circles, and Ellipses 609 9.9 Camera Calibration 613 9.9.1 Camera Models for Area Scan Cameras 614 9.9.2 Camera Model for Line Scan Cameras 618 9.9.3 Calibration Process 622 9.9.4 World Coordinates from Single Images 626 9.9.5 Accuracy of the Camera Parameters 629 9.10 Stereo Reconstruction 631 9.10.1 Stereo Geometry 632 9.10.2 Stereo Matching 639 9.11 Template Matching 643 9.11.1 Gray-Value-Based Template Matching 644 9.11.2 Matching Using Image Pyramids 649 9.11.3 Subpixel-Accurate Gray-Value-Based Matching 652 9.11.4 Template Matching with Rotations and Scalings 653 9.11.5 Robust Template Matching 654 9.12 Optical Character Recognition 672 9.12.1 Character Segmentation 672 9.12.2 Feature Extraction 674 9.12.3 Classification 676 References 690 10 Machine Vision in Manufacturing 699 Peter Waszkewitz 10.1 Introduction 699 10.1.1 The Machine Vision Market 699 10.2 Application Categories 701 10.2.1 Types of Tasks 701 10.2.2 Types of Production 703 10.2.2.1 Discrete Unit Production Versus Continuous Flow 703 10.2.2.2 Job-Shop Production Versus Mass Production 704 10.2.3 Types of Evaluations 704 10.2.4 Value-Adding Machine Vision 705 10.3 System Categories 706 10.3.1 Common Types of Systems 707 10.3.2 Sensors 707 10.3.3 Vision Sensors 708 10.3.4 Compact Systems 709 10.3.5 Vision Controllers 710 10.3.6 PC-Based Systems 710 10.3.6.1 Library-Based Systems 711 10.3.6.2 Application-Package-Based Systems 712 10.3.6.3 Library-Based Application Packages 713 10.3.7 Excursion: Embedded Image Processing 713 10.3.8 Summary 714 10.4 Integration and Interfaces 715 10.4.1 Standardization 715 10.4.2 Interfaces 716 10.5 Mechanical Interfaces 716 10.5.1 Dimensions and Fixation 717 10.5.2 Working Distances 718 10.5.3 Position Tolerances 718 10.5.4 Forced Constraints 719 10.5.5 Additional Sensor Requirements 719 10.5.6 Additional Motion Requirements 720 10.5.7 Environmental Conditions 721 10.5.8 Reproducibility 722 10.5.9 Gauge Capability 723 10.6 Electrical Interfaces 725 10.6.1 Wiring and Movement 726 10.6.2 Power Supply 726 10.6.3 Internal Data Connections 727 10.6.4 External Data Connections 729 10.7 Information Interfaces 729 10.7.1 Interfaces and Standardization 730 10.7.2 Traceability 730 10.7.3 Types of Data and Data Transport 731 10.7.4 Control Signals 731 10.7.5 Result and Parameter Data 732 10.7.6 Mass Data 733 10.7.7 Digital I/O 733 10.7.8 Field Bus 733 10.7.9 Serial Interfaces 734 10.7.10 Network 734 10.7.10.1 Standard Ethernet–TCP/IP 734 10.7.10.2 OPC UA and Industry 4.0 735 10.7.10.3 Ethernet-Based Field Bus/Real-Time Ethernet 735 10.7.11 Files 736 10.7.12 Time and Integrity Considerations 736 10.8 Temporal Interfaces 738 10.8.1 Discrete Motion Production 738 10.8.2 Continuous Motion Production 740 10.8.3 Line-Scan Processing 743 10.9 Human–Machine Interfaces 745 10.9.1 Interfaces for Engineering Vision Systems 746 10.9.2 Runtime Interface 747 10.9.2.1 Using the PLC HMI for Machine Vision 749 10.9.3 Remote Maintenance 750 10.9.3.1 Safety Precaution: No Movements 751 10.9.4 Offline Setup 751 10.10 3D Systems 753 10.10.1 Dimensionality and Representation 753 10.10.1.1 Dimensionality 753 10.10.1.2 2.5D and 3D 754 10.10.1.3 Point Clouds and Registration 755 10.10.1.4 Representation 757 10.10.2 3D Data Acquisition 757 10.10.2.1 Passive Methods 758 10.10.2.2 Active Methods 759 10.10.3 Applications 764 10.10.3.1 Identification 765 10.10.3.2 Completeness Check 765 10.10.3.3 Object and Pose Recognition 766 10.10.3.4 Shape and Dimension Applications 767 10.10.3.5 Surface Inspection 769 10.10.3.6 Robotics 770 10.10.4 Conclusion 771 10.11 Industrial Case Studies 772 10.11.1 Glue Check Under UV Light 772 10.11.1.1 Task 772 10.11.1.2 Solution 773 10.11.1.3 Equipment 773 10.11.1.4 Algorithms 774 10.11.1.5 Key Points 774 10.11.2 Completeness Check 774 10.11.2.1 Task 774 10.11.2.2 Solution 774 10.11.2.3 Key Point: Mechanical Setup 775 10.11.2.4 Equipment 775 10.11.2.5 Algorithms 775 10.11.3 Multiple Position and Completeness Check 776 10.11.3.1 Task 776 10.11.3.2 Solution 776 10.11.3.3 Key Point: Cycle Time 778 10.11.3.4 Equipment 778 10.11.3.5 Algorithms 779 10.11.4 Pin-Type Verification 779 10.11.4.1 Task 779 10.11.4.2 Solution 779 10.11.4.3 Key Point: Self-Test 781 10.11.4.4 Equipment 781 10.11.4.5 Algorithms 781 10.11.5 Robot Guidance 781 10.11.5.1 Task 781 10.11.5.2 Solution 782 10.11.5.3 Key Point: Calibration 782 10.11.5.4 Key Point: Communication 783 10.11.5.5 Equipment 784 10.11.5.6 Algorithms 784 10.11.6 Type and Result Data Management 784 10.11.6.1 Task 784 10.11.6.2 Solution 785 10.11.6.3 Key Point: Type Data 785 10.11.6.4 Key Point: Result Data 785 10.11.6.5 Equipment 786 10.11.7 Dimensional Check for Process Control 786 10.11.7.1 Task 786 10.11.7.2 Solution 787 10.11.7.3 Equipment 787 10.11.7.4 Algorithms 788 10.11.8 Ceramic Surface Check 788 10.11.8.1 Task 788 10.11.8.2 Solution 788 10.11.8.3 Equipment 789 10.12 Constraints and Conditions 789 10.12.1 Inspection Task Requirements 789 10.12.2 Circumstantial Requirements 790 10.12.2.1 Cost 791 10.12.2.2 Automation Environment 791 10.12.2.3 Organizational Environment 792 10.12.3 Refinements 793 10.12.4 Limits and Prospects 794 References 796 Appendix 801 Index 805
£189.86
BPB Publications Mastering New Age Computer Vision
Book SynopsisThis book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks.
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Book SynopsisChapter 1. Background Introduction.- Chapter 2. Datasets.- Chapter 3. Algorithms.- Chapter 4. Applications.- Chapter 5. Conclusion and Future Directions.
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Springer Deep Learning for 3D Point Clouds
Book SynopsisChapter 1. Introduction to 3D Point Clouds: Datasets and Perception.- Chapter 2. Learning Basics for 3D Point Clouds.- Chapter 3. Deep Learning-based Point Cloud Enhancement I.- Chapter 4. Deep Learning-based Point Cloud Enhancement II.- Chapter 5. Deep Learning-based Point Cloud Analysis I.- Chapter 6. Deep Learning-based Point Cloud Analysis II.- Chapter 7. Point Cloud Pre-trained Models and Large Models.- Chapter 8. Point Cloud-Language Multi-modal Learning.- Chapter 9. Open Source Projects for 3D Point Clouds.- Chapter 10. Typical Engineering Applications of 3D Point Clouds.- Chapter 11. FutureWork on Deep Learning-based Point Cloud Technologies.
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Springer Computer Vision Metrics
Book SynopsisChapter 1. 2D/3D Image Capture and Representation.- Chapter 2. Image Pre-Processing Taxonomy, Colorimetry.- Chapter 3. Global and Regional Feature Descriptors.- Chapter 4. Local Feature Descriptors.- Chapter 5. Feature Descriptor Attribute Taxonomy.- Chapter 6. Feature Detector and Descriptor Survey.- Chapter 7. Ground Truth Data Topics, Benchmarks, Analysis.- Chapter 8. Vision Pipelines and HW/SW Optimizations.- Chapter 9. Feature Learning Taxonomy and Neuroscience Background.-Chapter 10. Feature Learning and Deep Learning Survey.- Chapter 11. Attention, Transformers, Hybrids, DDN's.- Chapter 12. Applied And Future Visual Computing Topics.
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Independently Published Advanced Computer Vision with OpenCV 4
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Packt Publishing Limited OpenCV 4 for Secret Agents: Use OpenCV 4 in secret projects to classify cats, reveal the unseen, and react to rogue drivers, 2nd Edition
Book SynopsisTurn futuristic ideas about computer vision and machine learning into demonstrations that are both functional and entertainingKey Features Build OpenCV 4 apps with Python 2 and 3 on desktops and Raspberry Pi, Java on Android, and C# in Unity Detect, classify, recognize, and measure real-world objects in real-time Work with images from diverse sources, including the web, research datasets, and various cameras Book DescriptionOpenCV 4 is a collection of image processing functions and computer vision algorithms. It is open source, supports many programming languages and platforms, and is fast enough for many real-time applications. With this handy library, you’ll be able to build a variety of impressive gadgets.OpenCV 4 for Secret Agents features a broad selection of projects based on computer vision, machine learning, and several application frameworks. To enable you to build apps for diverse desktop systems and Raspberry Pi, the book supports multiple Python versions, from 2.7 to 3.7. For Android app development, the book also supports Java in Android Studio, and C# in the Unity game engine. Taking inspiration from the world of James Bond, this book will add a touch of adventure and computer vision to your daily routine. You’ll be able to protect your home and car with intelligent camera systems that analyze obstacles, people, and even cats. In addition to this, you’ll also learn how to train a search engine to praise or criticize the images that it finds, and build a mobile app that speaks to you and responds to your body language.By the end of this book, you will be equipped with the knowledge you need to advance your skills as an app developer and a computer vision specialist.What you will learn Detect motion and recognize gestures to control a smartphone game Detect car headlights and estimate their distance Detect and recognize human and cat faces to trigger an alarm Amplify motion in a real-time video to show heartbeats and breaths Make a physics simulation that detects shapes in a real-world drawing Build OpenCV 4 projects in Python 3 for desktops and Raspberry Pi Develop OpenCV 4 Android applications in Android Studio and Unity Who this book is forIf you are an experienced software developer who is new to computer vision or machine learning, and wants to study these topics through creative projects, then this book is for you. The book will also help existing OpenCV users who want upgrade their projects to OpenCV 4 and new versions of other libraries, languages, tools, and operating systems. General familiarity with object-oriented programming, application development, and usage of operating systems (OS), developer tools, and the command line is required.Table of ContentsTable of Contents Preparing for the Mission Searching for Luxury Accommodations Worldwide Training a Smart Alarm to Recognize the Villain and His Cat Controlling a Phone App with Your Suave Gestures Equipping Your Car with a Rearview Camera and Hazard Detection Creating a Physics Simulation Based on a Pen and Paper Sketch Seeing a Heartbeat with a Motion-Amplifying Camera Stopping Time and Seeing like a Bee
£29.44
Springer London Ltd Advanced Algorithmic Approaches to Medical Image
Book SynopsisMedical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.Table of Contents1. Principles of Image Generation.- 1.1 Introduction.- 1.2 Ultrasound Image Generation.- 1.2.1 The Principle of Pulse-Echo Ultrasound Imaging.- 1.2.2 B-Scan Quality and the Ultimate Limits.- 1.2.3 Propagation-Related Artifacts and Resolution Limits.- 1.2.3 Attenuation-Related Artifacts.- 1.3 X-Ray Cardiac Image Generation.- 1.3.1 LV Data Acquisition System Using X-Rays.- 1.3.2 Drawbacks of Cardiac Catheterization.- 1.4 Magnetic Resonance Image Generation.- 1.4.1 Physical Principles of Nuclear Magnetic Resonance.- 1.4.2 Basics of Magnetic Resonance Imaging.- 1.4.3 Gradient-Echo (GRE).- 1.4.4 The Latest Techniques for MR Image Generation.- 1.4.5 3-D Turbo FLASH (MP-RAGE) Technique.- 1.4.6 Non-Rectilinear k-Space Trajectory: Spiral.- 1.4.7 Fat Suppression.- 1.4.8 High Speed MRI: Perfusion-Weighted.- 1.4.9 Time of Flight (TOF) MR Angiography.- 1.4.10 Fast Spectroscopic Imaging.- 1.4.11 Recent MR Imaging Techniques.- 1.5 Computer Tomography Image Generation.- 1.5.1 Fourier Reconstruction Method.- 1.6 Positron-Emission Tomography Image Generation.- 1.6.1 Underlying Principles of.- 1.6.2 Usage of PET in Diagnosis.- 1.6.3 Fourier Slice Theorem.- 1.6.4 The Reconstruction Algorithm in PET.- 1.6.5 Image Reconstruction Using Filtered Back-Projection.- 1.7 Comparison of Imaging Modalities: A Summary.- 1.7.1 Acknowledgements.- 2. Segmentation in Echocardiographic Images.- 2.1 Introduction.- 2.2 Heart Physiology and Anatomy.- 2.2.1 Cardiac Function.- 2.2.2 Standard LV Views in 2-DEs.- 2.2.3 LV Function Assessment Using 2-DEs.- 2.3 Review of LV Boundary Extraction Techniques Applied to Echocardiographic Data.- 2.3.1 Acoustic Quantification Techniques.- 2.3.2 Image-Based Techniques.- 2.3.3 2-DE Image Processing Techniques.- 2.4Automatic Fuzzy Reasoning-Based Left Ventricular Center Point Extraction.- 2.4.1 LVCP Extraction System Overview.- 2.4.2 Stage 1: Pre-Processing.- 2.4.3 Stage 2: LVCP Features Fuzzification.- 2.4.4 Template Matching.- 2.4.5 Experimental Results.- 2.4.6 Conclusion.- 2.5 A New Edge Detection in the Wavelet Transform Domain.- 2.5.1 Multiscale Edge Detection and the Wavelet Transform.- 2.5.2 Edge Detection Based on the Global Maximum of Wavelet Transform (GMWT).- 2.5.3 GMWT Performance Analysis and Comparison.- 2.6 LV Segmentation System.- 2.6.1 Overall Reference.- 2.6.2 3D Non-Uniform Radial Intensity Sampling.- 2.6.3 LV Boundary Edge Detection on 3D Radial Intensity Matrix.- 2.6.4 Post-Processing of the Edges and Closed LVE Approximation.- 2.6.5 Automatic LV Volume Assessment.- 2.7 Conclusions.- 2.8 Acknowledgments.- 3. Cardiac Boundary Segmentation.- 3.1 Introduction.- 3.2 Cardiac Anatomy and Data Acquisitions for MR, CT, Ul-trasound and X-Rays.- 3.2.1 Cardiac Anatomy.- 3.2.2 Cardiac MR, CT, Ultrasound and X-Ray Acquisitions.- 3.3 Low- and Medium-Level LV Segmentation Techniques.- 3.3.1 Smoothing Image Data.- 3.3.2 Manual and Semi-Automatic LV Thresholding.- 3.3.3 LV Dynamic Thresholding.- 3.3.4 Edge-Based Techniques.- 3.3.5 Mathematical Morphology-Based Techniques.- 3.3.6 Drawbacks of Low-Level LV Segmentation Techniques.- 3.4 Model-Based Pattern Recognition Methods for LV Modeling.- 3.4.1 LV Active Contour Models in the Spatial and Temporal Domains.- 3.4.2 Model-Based Pattern Recognition Learning Methods.- 3.4.3 Polyline Distance Measure and Performance Terms.- 3.4.4 Data Analysis Using IdCM, InCM and the Greedy Method.- 3.5 Left Ventricle Apex Modeling: A Model-Based Approach.- 3.5.1 Longitudinal Axis and Apex Modeling.- 3.5.2 Ruled Surface Model.- 3.5.3 Ruled Surface sr and its Coefficients.- 3.5.4 Estimation of Robust Coefficients and Coordinates of the Ruled Surface.- 3.5.5 Experiment Design.- 3.5.6 Analytical Error Measure, AQin for Inlier Data.- 3.5.7 Experiments, Results and Discussions.- 3.5.8 Conclusions on LV Apex Modeling.- 3.6 Integration of Low-Level Features in LV Model-Based Cardiac Imaging: Fusion of Two Computer Vision Systems.- 3.7 General Purpose LV Validation Technique.- 3.8 LV Convex Hulling: Quadratic Training-Based Point Modeling.- 3.8.1 Quadratic Vs. Linear Optimization for Convex Hulling.- 3.9 LV Eigen Shape Modeling.- 3.9.1 Procrustes Superposition.- 3.9.2 Dimensionality Reduction Using Constraints for Joint.- 3.10 LV Neural Network Models.- 3.11 Comparative Study and Summary of the Characteristics of Model-Based Techniques.- 3.11.1 Characteristics of Model-Based LV Imaging.- 3.12 LV Quantification: Wall Motion and Tracking.- 3.12.1 LV Wall Motion Measurements.- 3.12.2 LV Volume Measurements.- 3.12.3 LV Wall Motion Tracking.- 3.13 Conclusions.- 3.13.1 Cardiac Hardware.- 3.13.2 Cardiac Software.- 3.13.3 Summary.- 3.13.4 Acknowledgments.- 4. Brain Segmentation Techniques.- 4.1 Introduction.- 4.1.1 Human Brain Anatomy and the MRI System.- 4.1.2 Applications of Brain Segmentation.- 4.2 Brain Scanning and its Clinical Significance.- 4.3 Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.3.1 Atlas-Based and Threshold-Based Techniques.- 4.3.2 Cortical Segmentation Using Probability-Based Techniques.- 4.3.3 Clustering-Based Cortical Segmentation Techniques.- 4.3.4 Mathematical Morphology-Based Cortical Segmentation Techniques.- 4.3.5 Prior Knowledge-Based Techniques.- 4.3.6 Texture-Based Techniques.- 4.3.7 Neural Network-Based Techniques.- 4.3.8 Regional Hyperstack: Fusion of Edge-Diffusion with Region-Linking.- 4.3.9 Fusion of Probability-Based with Edge Detectors, Connectivity and Region-Growing.- 4.3.10 Summary of Region-Based Techniques: Pros and Cons.- 4.4 Boundary/Surface-Based 2-D and 3-D Cortical Segmentation Techniques: Edge, Reconstruction, Parametric and Geometric Snakes/Surfaces.- 4.4.1 Edge-Based Cortical-Boundary Estimation Techniques.- 4.4.2 3-D Cortical Reconstruction From 2-D Serial Cross-Sections (Bourke/Victoria).- 4.4.3 2-D and 3-D Parametric Deformable Models for Cortical Boundary Estimation: Snakes, Fitting, Constrained, Ribbon, T-Surface, Connectedness.- 4.4.4 2-D and 3-D Geometric Deformable Models.- 4.4.5 A Note on Isosurface Extraction (Lorensen/GE).- 4.4.6 Summary of Boundary/Surface-Based Techniques: Pros and Cons.- 4.5 Fusion of Boundary/Surface with Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.5.1 2-D/3-D Regional Parametric Boundary: Fusion of Boundary with Classification (Kapur/MIT).- 4.5.2 Regional Parametric Surfaces: Fusion of Surface with Clustering (Xu/JHU).- 4.5.3 2-D Regional Geometric Boundary: Fusion of Boundary with Clustering for Cortical Boundary Estimation (Suri/Marconi).- 4.5.34 3-D Regional Geometric Surfaces: Fusion of Geometric Surface with Probability-Based Voxel Classification (Zeng/Yale).- 4.5.5 2-D/3-D Regional Geometric Surface: Fusion of Geometric Boundary/Surface with Global Shape Information (Leventon/MIT).- 4.5.6 2-D/3-D Regional Geometric Surface: Fusion of Boundary/Surface with Bayesian-Based Pixel Classification (Barillot/IRISA).- 4.5.7 Similarities/Differences Between Different Cortical Segmentation Techniques.- 4.6 3-D Visualization Using Volume Rendering and Texture Mapping.- 4.6.1 Volume Rendering Algorithm for Brain Segmentation.- 4.6.2 Texture Mapping Algorithm for Segmented Brain Visualization.- 4.7 A Note on fMRI: Algorithmic Approach for Establishing the Relationship Between Cognitive Functions and Brain Cortical Anatomy.- 4.7.1 Superiority of fMRI over PET/SPECT Imaging.- 4.7.2 Applications of fMRI.- 4.7.3 Algorithm for Superimposition of Functional and Anatomical Cortex.- 4.7.4 A Short Note on fMRI Time Course Data Analysis.- 4.7.5 Measure of Cortex Geometry.- 4.8 Discussions: Advantages, Validation and New Challenges i 2-D.- 4.8.1 Advantages of Regional Geometric Boundary/Surfaces.- 4.8.2 Validation of 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.3 Challenges in 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.4 Challenges in fMRI.- 4.9 Conclusions and the Future.- 4.9.1 Acknowledgements.- 5. Segmentation for Multiple Sclerosis Lesion.- 5.1 Introduction.- 5.2 Segmentation Techniques.- 5.2.1 Multi-Spectral Techniques.- 5.2.2 Feature Space Classification.- 5.2.3 Supervised Segmentation.- 5.2.4 Unsupervised Segmentation.- 5.2.5 Automatic Segmentation.- 5.3 AFFIRMATIVE Images.- 5.4 Image Pre-Processing.- 5.4.1 RF Inhomogeneity Correction.- 5.4.2 Image Stripping.- 5.4.3 Three Dimensional MR Image Registration.- 5.4.4 Segmentation.- 5.4.5 Flow Correction.- 5.4.6 Evaluation and Validation.- 5.5 Quantification of Enhancing Multiple Sclerosis Lesions.- 5.6 Quadruple Contrast Imaging.- 5.7 Discussion.- 5.7.1 Acknowledgements.- 6. Finite Mixture Models.- 6.1 Introduction.- 6.2 Pixel Labeling Using the Classical Mixture Model.- 6.3 Pixel Labeling Using the Spatially Variant Mixture Model.- 6.4 Comparison of CMM and SVMM for Pixel Labeling.- 6.5 Bayesian Pixel Labeling Using the SVMM.- 6.6 Segmentation Results.- 6.6.1 Computer Simulations.- 6.6.2 Application to Magnetic Resonance Images.- 6.7 Practical Aspects.- 6.8 Summary.- 6.9 Acknowledgements.- 7. MR Spectroscopy.- 7.1 Introduction.- 7.2 A Short History of Neurospectroscopic Imaging and Segmentation in Alzheimer’s Disease and Multiple Sclerosis.- 7.2.1 Alzheimer’s Disease.- 7.2.2 Multiple Sclerosis.- 7.3 Data Acquisition and Image Segmentation.- 7.3.1 Image Pre-Processing for Segmentation.- 7.3.2 Image Post-Processing for Segmentation.- 7.4 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation in Multiple Sclerosis.- 7.4.1 Automatic MRSI Segmentation and Image Processing Algorithm.- 7.4.2 Relative Metabolite Concentrations and Contribution of Gray Matter and White Matter in the Normal Human Brain.- 7.4.3 MRSI and Gadolinium-Enhanced (Gd).- 7.4.4 Lesion Load and Metabolite Concentrations by Segmentation and MRSI.- 7.4.5 MR Spectroscopic Imaging and Localization for Segmentation.- 7.4.6 Lesion Segmentation and Quantification.- 7.4.7 Magnetic Resonance Spectroscopic Imaging and Segmentation Data Processing.- 7.4.8 Statistical Analysis.- 7.5 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation of Alzheimer’s Disease.- 7.5.1 MRSI Data Acquisition Methods.- 7.5.2 H-1 MR Spectra Analysis.- 7.6 Applications of Magnetic Resonance Spectroscopic Imaging and Segmentation.- 7.6.1 Multiple Sclerosis Lesion Metabolite Characteristics and Serial Changes.- 7.6.2 zheimer’s Disease Plaque Metabolite Characteristics.- 7.7 Discussion.- 7.8 Conclusion.- 7.8.1 Acknowledgements.- 8. Fast WM/GM Boundary Estimation.- 8.1 Introduction.- 8.2 Derivation of the Regional Geometric Active Contour Model from the Classical Parametric Deformable Model.- 8.3 Numerical Implementation of the Three Speed Functions in the Level Set Framework for Geometric Snake Propagation.- 8.3.1 Regional Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.2 Gradient Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.3 Curvature Speed Term Expressed in Terms of the Level Set Function (ø).- 8.4 Fast Brain Segmentation System Based on Regional Level Sets.- 8.4.1 Overall System and Its Components.- 8.4.2 Fuzzy Membership Computation/Pixel Classification.- 8.4.3 Eikonal Equation and its Mathematical Solution.- 8.4.4 Fast Marching Method for Solving the Eikonal Equation.- 8.4.5 A Note on the Heap Sorting Algorithm.- 8.4.6 Segmentation Engine: Running the Level Set Method in the Narrow Band.- 8.5 MR Segmentation Results on Synthetic and Real Data.- 8.5.1 Input Data Set and Input Level Set Parameters.- 8.5.2 Results: Synthetic and Real.- 8.5.3 Numerical Stability, Signed Distance Transformation Computation, Sensitivity of Parameters and Speed Issues.- 8.6 Advantages of the Regional Level Set Technique.- 8.7 Discussions: Comparison with Previous Techniques.- 8.8 Conclusions and Further Directions.- 8.8.1 Acknowledgements.- 9. Digital Mammography Segmentation.- 9.1 Introduction.- 9.2 Image Segmentation in Mammography.- 9.3 Anatomy of the Breast.- 9.4 Image Acquisition and Formats.- 9.4.1 Digitization of X-Ray Mammograms.- 9.4.2 Image Formats.- 9.4.3 Image Quantization and Tree-Pyramids.- 9.5 Mammogram Enhancement Methods.- 9.6 Quantifying Mammogram Enhancement.- 9.7 Segmentation of Breast Profile.- 9.8 Segmentation of Microcalcifications.- 9.9 Segmentation of Masses.- 9.9.1 Global Methods.- 9.9.2 Edge-Based Methods.- 9.9.3 Region-Based Segmentation.- 9.9.4 ROI Detection Techniques Using a Single Breast.- 9.9.5 ROI Detection Techniques Using Breast Symmetry.- 9.9.6 Detection of Spicules.- 9.9.7 Breast Alignment for Segmentation.- 9.10 Measures of Segmentation and Abnormality Detection.- 9.11 Feature Extraction From Segmented Regions.- 9.11.1 Morphological Features.- 9.11.2 Texture Features.- 9.11.3 Other Features.- 9.12 Public Domain Databases in Mammography.- 9.12.1 The Digital Database for Screening Mammography (DDSM).- 9.12.2 LLNL/UCSF Database.- 9.12.3 Washington University Digital Mammography Database.- 9.12.4 The Mammographic Image Analysis Society (MIAS) Database.- 9.13 Classification and Measures of Performance.- 9.13.1 Classification Techniques.- 9.13.2 The Receiver Operating Characteristic Curve.- 9.14 Conclusions.- 9.15 Acknowledgements.- 10. Cell Image Segmentation for Diagnostic Pathology.- 10.1 Introduction.- 10.2 Segmentation.- 10.2.1 Feature Space Analysis.- 10.2.2 Mean Shift Procedure.- 10.2.3 Cell Segmentation.- 10.2.4 Segmentation Examples.- 10.3 Decision Support System for Pathology.- 10.3.1 Problem Domain.- 10.3.2 System Overview.- 10.3.3 Current Database.- 10.3.4 Analysis of Visual Attributes.- 10.3.5 Overall Dissimilarity Metric.- 10.3.6 Performance Evaluation and Comparisons.- 10.4 Conclusion.- 11. The Future in Segmentation.- 11.1 Future Research in Medical Image Segmentation.- 11.1.1 The Future of MR Image Generation and Physical Principles.- 11.1.2 The Future of Cardiac Imaging.- 11.2.3 The Future of Neurological Segmentation.- 11.2.4 The Future in Digital Mammography.- 11.2.5 The Future of Pathology Image Segmentation.
£179.99
Springer Fachmedien Wiesbaden 3D-Bildsegmentierung mittels statistischer Formmodelle: Korrespondenzfindung, Modellierung, Segmentierung und ihre wechselseitigen Abhängigkeiten
Book SynopsisSebastian T. Gollmer entwickelt neue Methoden und Algorithmen für die Erstellung statistischer Formmodelle, die Formmodellierung und die formmodellbasierte Bildsegmentierung. Der Autor diskutiert ihre Vorteile gegenüber den jeweils etablierten Verfahren aus der Literatur und evaluiert den generellen Einfluss dieser drei Aspekte auf die erzielbare Segmentierungsgenauigkeit. Letzteres erfolgt sowohl unter Verwendung neu entwickelter und etablierter Evaluierungsverfahren als auch im Rahmen realer Anwendungen. Von besonderer praktischer Relevanz zeigen sich dabei die exzellenten, mit einem neuen vollautomatischen Algorithmus erzielten Ergebnisse für die Unterkiefersegmentierung.Table of ContentsStatistische Formmodelle.- Evaluierung der Korrespondenzgüte.- Untersuchung der Normalverteilungsannahme.- Kernbasierte Formmodellierung.- Relaxiertes aktives Formmodell.- Unterkiefer- und Abdomensegmentierung.
£47.49
Atlantis Press (Zeger Karssen) Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding
Book SynopsisHuman action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attention from many researchers in the computer vision field due to their better robustness and performance. This book will cover gap of information and materials on comprehensive outlook – through various strategies from the scratch to the state-of-the-art on computer vision regarding action recognition approaches. This book will target the students and researchers who have knowledge on image processing at a basic level and would like to explore more on this area and do research. The step by step methodologies will encourage one to move forward for a comprehensive knowledge on computer vision for recognizing various human actions.Table of ContentsIntroduction.- Low-level Image Processing for Action Representations.- Action Representation Approaches.- MHI – A Global-based Generic Approach.- Shape Representation and Feature Vector Analysis .- Action Datasets.-Challenges Ahead.
£999.99
Springer Verlag, Singapore Artificial Intelligence in Construction
Book SynopsisThis book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally.Table of Contents1. Introduction to Artificial Intelligence 2. Fuzzy logic and reasoning 3. Knowledge representation 4. Expert system 5. Information fusion 6. Time series analysis 7. Process mining 8. Simulation and optimization 9. Natural language processing 10. Computer vision 11. Conclusions
£85.49
Springer Verlag, Singapore Quantum Image Processing
Book SynopsisThis book provides a comprehensive introduction to quantum image processing, which focuses on extending conventional image processing tasks to the quantum computing frameworks. It summarizes the available quantum image representations and their operations, reviews the possible quantum image applications and their implementation, and discusses the open questions and future development trends. It offers a valuable reference resource for graduate students and researchers interested in this emerging interdisciplinary field.Table of Contents1.Introduction and Overview.- 2. Quantum Image Representations.- 3. Quantum Image Operations.- 4. Quantum Image Security.- 5. Quantum Image Understanding.- 6. Quantum Multimedia Techniques.- 7. Summary and Discussion.
£98.99
John Wiley & Sons Inc An Introduction to 3D Computer Vision Techniques
Book SynopsisComputer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene.Trade Review“This text is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.” (Zentralblatt MATH, 2012) Table of ContentsPreface xv Acknowledgements xvii Notation and Abbreviations xix Part I 1 1 Introduction 3 1.1 Stereo-pair Images and Depth Perception 4 1.2 3D Vision Systems 4 1.3 3D Vision Applications 5 1.4 Contents Overview: The 3D Vision Task in Stages 6 2 Brief History of Research on Vision 9 2.1 Abstract 9 2.2 Retrospective of Vision Research 9 2.3 Closure 14 2.3.1 Further Reading 14 Part II 15 3 2D and 3D Vision Formation 17 3.1 Abstract 17 3.2 Human Visual System 18 3.3 Geometry and Acquisition of a Single Image 23 3.3.1 Projective Transformation 24 3.3.2 Simple Camera System: the Pin-hole Model 24 3.3.3 Projective Transformation of the Pin-hole Camera 28 3.3.4 Special Camera Setups 29 3.3.5 Parameters of Real Camera Systems 30 3.4 Stereoscopic Acquisition Systems 31 3.4.1 Epipolar Geometry 31 3.4.2 Canonical Stereoscopic System 36 3.4.3 Disparity in the General Case 38 3.4.4 Bifocal, Trifocal and Multifocal Tensors 39 3.4.5 Finding the Essential and Fundamental Matrices 41 3.4.6 Dealing with Outliers 49 3.4.7 Catadioptric Stereo Systems 54 3.4.8 Image Rectification 55 3.4.9 Depth Resolution in Stereo Setups 59 3.4.10 Stereo Images and Reference Data 61 3.5 Stereo Matching Constraints 66 3.6 Calibration of Cameras 70 3.6.1 Standard Calibration Methods 71 3.6.2 Photometric Calibration 73 3.6.3 Self-calibration 73 3.6.4 Calibration of the Stereo Setup 74 3.7 Practical Examples 75 3.7.1 Image Representation and Basic Structures 75 3.8 Appendix: Derivation of the Pin-hole Camera Transformation 91 3.9 Closure 93 3.9.1 Further Reading 93 3.9.2 Problems and Exercises 94 4 Low-level Image Processing for Image Matching 95 4.1 Abstract 95 4.2 Basic Concepts 95 4.2.1 Convolution and Filtering 95 4.2.2 Filter Separability 97 4.3 Discrete Averaging 99 4.3.1 Gaussian Filter 100 4.3.2 Binomial Filter 101 4.4 Discrete Differentiation 105 4.4.1 Optimized Differentiating Filters 105 4.4.2 Savitzky–Golay Filters 108 4.5 Edge Detection 115 4.5.1 Edges from Signal Gradient 117 4.5.2 Edges from the Savitzky–Golay Filter 119 4.5.3 Laplacian of Gaussian 120 4.5.4 Difference of Gaussians 126 4.5.5 Morphological Edge Detector 127 4.6 Structural Tensor 127 4.6.1 Locally Oriented Neighbourhoods in Images 128 4.6.2 Tensor Representation of Local Neighbourhoods 133 4.6.3 Multichannel Image Processing with Structural Tensor 143 4.7 Corner Detection 144 4.7.1 The Most Common Corner Detectors 144 4.7.2 Corner Detection with the Structural Tensor 149 4.8 Practical Examples 151 4.8.1 C++ Implementations 151 4.8.2 Implementation of the Morphological Operators 157 4.8.3 Examples in Matlab: Computation of the SVD 161 4.9 Closure 162 4.9.1 Further Reading 163 4.9.2 Problems and Exercises 163 5 Scale-space Vision 165 5.1 Abstract 165 5.2 Basic Concepts 165 5.2.1 Context 165 5.2.2 Image Scale 166 5.2.3 Image Matching Over Scale 166 5.3 Constructing a Scale-space 168 5.3.1 Gaussian Scale-space 168 5.3.2 Differential Scale-space 170 5.4 Multi-resolution Pyramids 172 5.4.1 Introducing Multi-resolution Pyramids 172 5.4.2 How to Build Pyramids 175 5.4.3 Constructing Regular Gaussian Pyramids 175 5.4.4 Laplacian of Gaussian Pyramids 177 5.4.5 Expanding Pyramid Levels 178 5.4.6 Semi-pyramids 179 5.5 Practical Examples 181 5.5.1 C++ Examples 181 5.5.2 Matlab Examples 186 5.6 Closure 191 5.6.1 Chapter Summary 191 5.6.2 Further Reading 191 5.6.3 Problems and Exercises 192 6 Image Matching Algorithms 193 6.1 Abstract 193 6.2 Basic Concepts 193 6.3 Match Measures 194 6.3.1 Distances of Image Regions 194 6.3.2 Matching Distances for Bit Strings 198 6.3.3 Matching Distances for Multichannel Images 199 6.3.4 Measures Based on Theory of Information 202 6.3.5 Histogram Matching 205 6.3.6 Efficient Computations of Distances 206 6.3.7 Nonparametric Image Transformations 209 6.3.8 Log-polar Transformation for Image Matching 218 6.4 Computational Aspects of Matching 222 6.4.1 Occlusions 222 6.4.2 Disparity Estimation with Subpixel Accuracy 224 6.4.3 Evaluation Methods for Stereo Algorithms 226 6.5 Diversity of Stereo Matching Methods 229 6.5.1 Structure of Stereo Matching Algorithms 233 6.6 Area-based Matching 238 6.6.1 Basic Search Approach 239 6.6.2 Interpreting Match Cost 241 6.6.3 Point-oriented Implementation 245 6.6.4 Disparity-oriented Implementation 250 6.6.5 Complexity of Area-based Matching 256 6.6.6 Disparity Map Cross-checking 257 6.6.7 Area-based Matching in Practice 259 6.7 Area-based Elastic Matching 273 6.7.1 Elastic Matching at a Single Scale 273 6.7.2 Elastic Matching Concept 278 6.7.3 Scale-based Search 280 6.7.4 Coarse-to-fine Matching Over Scale 283 6.7.5 Scale Subdivision 284 6.7.6 Confidence Over Scale 285 6.7.7 Final Multi-resolution Matcher 286 6.8 Feature-based Image Matching 288 6.8.1 Zero-crossing Matching 289 6.8.2 Corner-based Matching 292 6.8.3 Edge-based Matching: The Shirai Method 295 6.9 Gradient-based Matching 296 6.10 Method of Dynamic Programming 298 6.10.1 Dynamic Programming Formulation of the Stereo Problem 301 6.11 Graph Cut Approach 306 6.11.1 Graph Cut Algorithm 306 6.11.2 Stereo as a Voxel Labelling Problem 311 6.11.3 Stereo as a Pixel Labelling Problem 312 6.12 Optical Flow 314 6.13 Practical Examples 318 6.13.1 Stereo Matching Hierarchy in C++ 318 6.13.2 Log-polar Transformation 319 6.14 Closure 321 6.14.1 Further Reading 321 6.14.2 Problems and Exercises 322 7 Space Reconstruction and Multiview Integration 323 7.1 Abstract 323 7.2 General 3D Reconstruction 323 7.2.1 Triangulation 324 7.2.2 Reconstruction up to a Scale 325 7.2.3 Reconstruction up to a Projective Transformation 327 7.3 Multiview Integration 329 7.3.1 Implicit Surfaces and Marching Cubes 330 7.3.2 Direct Mesh Integration 338 7.4 Closure 342 7.4.1 Further Reading 342 8 Case Examples 343 8.1 Abstract 343 8.2 3D System for Vision-Impaired Persons 343 8.3 Face and Body Modelling 345 8.3.1 Development of Face and Body Capture Systems 345 8.3.2 Imaging Resolution, 3D Resolution and Implications for Applications 346 8.3.3 3D Capture and Analysis Pipeline for Constructing Virtual Humans 350 8.4 Clinical and Veterinary Applications 352 8.4.1 Development of 3D Clinical Photography 352 8.4.2 Clinical Requirements for 3D Imaging 353 8.4.3 Clinical Assessment Based on 3D Surface Anatomy 353 8.4.4 Extraction of Basic 3D Anatomic Measurements 354 8.4.5 Vector Field Surface Analysis by Means of Dense Correspondences 357 8.4.6 Eigenspace Methods 359 8.4.7 Clinical and Veterinary Examples 362 8.4.8 Multimodal 3D Imaging 367 8.5 Movie Restoration 370 8.6 Closure 374 8.6.1 Further Reading 374 Part III 375 9 Basics of the Projective Geometry 377 9.1 Abstract 377 9.2 Homogeneous Coordinates 377 9.3 Point, Line and the Rule of Duality 379 9.4 Point and Line at Infinity 380 9.5 Basics on Conics 382 9.5.1 Conics in ℘2 382 9.5.2 Conics in ℘2 384 9.6 Group of Projective Transformations 385 9.6.1 Projective Base 385 9.6.2 Hyperplanes 386 9.6.3 Projective Homographies 386 9.7 Projective Invariants 387 9.8 Closure 388 9.8.1 Further Reading 389 10 Basics of Tensor Calculus for Image Processing 391 10.1 Abstract 391 10.2 Basic Concepts 391 10.2.1 Linear Operators 392 10.2.2 Change of Coordinate Systems: Jacobians 393 10.3 Change of a Base 394 10.4 Laws of Tensor Transformations 396 10.5 The Metric Tensor 397 10.5.1 Covariant and Contravariant Components in a Curvilinear Coordinate System 397 10.5.2 The First Fundamental Form 399 10.6 Simple Tensor Algebra 399 10.6.1 Tensor Summation 399 10.6.2 Tensor Product 400 10.6.3 Contraction and Tensor Inner Product 400 10.6.4 Reduction to Principal Axes 400 10.6.5 Tensor Invariants 401 10.7 Closure 401 10.7.1 Further Reading 401 11 Distortions and Noise in Images 403 11.1 Abstract 403 11.2 Types and Models of Noise 403 11.3 Generating Noisy Test Images 405 11.4 Generating Random Numbers with Normal Distributions 407 11.5 Closure 408 11.5.1 Further Reading 408 12 Image Warping Procedures 409 12.1 Abstract 409 12.2 Architecture of the Warping System 409 12.3 Coordinate Transformation Module 410 12.3.1 Projective and Affine Transformations of a Plane 410 12.3.2 Polynomial Transformations 411 12.3.3 Generic Coordinates Mapping 412 12.4 Interpolation of Pixel Values 412 12.4.1 Bilinear Interpolation 412 12.4.2 Interpolation of Nonscalar-Valued Pixels 414 12.5 The Warp Engine 414 12.6 Software Model of the Warping Schemes 415 12.6.1 Coordinate Transformation Hierarchy 415 12.6.2 Interpolation Hierarchy 416 12.6.3 Image Warp Hierarchy 416 12.7 Warp Examples 419 12.8 Finding the Linear Transformation from Point Correspondences 420 12.8.1 Linear Algebra on Images 424 12.9 Closure 427 12.9.1 Further Reading 428 13 Programming Techniques for Image Processing and Computer Vision 429 13.1 Abstract 429 13.2 Useful Techniques and Methodology 430 13.2.1 Design and Implementation 430 13.2.2 Template Classes 436 13.2.3 Asserting Code Correctness 438 13.2.4 Debugging Issues 440 13.3 Design Patterns 441 13.3.1 Template Function Objects 441 13.3.2 Handle-body or Bridge 442 13.3.3 Composite 445 13.3.4 Strategy 447 13.3.5 Class Policies and Traits 448 13.3.6 Singleton 450 13.3.7 Proxy 450 13.3.8 Factory Method 451 13.3.9 Prototype 452 13.4 Object Lifetime and Memory Management 453 13.5 Image Processing Platforms 455 13.5.1 Image Processing Libraries 455 13.5.2 Writing Software for Different Platforms 455 13.6 Closure 456 13.6.1 Further Reading 456 14 Image Processing Library 457 References 459 Index 475
£99.86
John Wiley & Sons Inc Pixels Paintings
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
£119.70
John Wiley & Sons Inc Image Processing 27 Adaptive and Cognitive
Book SynopsisIntelligent Image Processing describes the EyeTap technology that allows non-invasive tapping into the human eye through devices built into eyeglass frames. This isn't merely about a computer screen inside eyeglasses, but rather the ability to have a shared telepathic experience among viewers.Table of ContentsPreface 1 Humanistic Intelligence as a Basis for Intelligent Image Processing 1.1 Humanistic Intelligence/ 1.2 "WearComp" as Means of Realizing Humanistic Intelligence 1.3 Practical Embodiments of Humanistic Intelligence 2 Where on the Body is the Best Place for a Personal Imaging System? 2.1 Portable Imaging Systems 2.2 Personal Handheld Systems 2.3 Concomitant Cover Activities and the Videoclips Camera System 2.4 The Wristwatch Videophone: A Fully Functional "Always Ready" Prototype 2.5 Telepointer: Wearable Hands-Free Completely Self-Contained Visual Augmented Reality 2.6 Portable Personal Pulse Doppler Radar Vision System Based on Time-Frequency Analysis and q-Chirplet Transform 2.7 When Both Camera and Display are Headworn: Personal Imaging and Mediated Reality 2.8 Partially Mediated Reality 2.9 Seeing "Eye-to-Eye" 2.10 Exercises, Problem Sets, and Homework 3 The EyeTap Principle: Effectively Locating the Camera Inside the Eye as an Alternative to Wearable Camera Systems 3.1 A Personal Imaging System for Lifelong Video Capture 3.2 The EyeTap Principle 3.3 Practical Embodiments of EyeTap 3.4 Problems with Previously Known Camera Viewfinders 3.5 The Aremac 3.6 The Foveated Personal Imaging System 3.7 Teaching the EyeTap Principle 3.8 Calibration of EyeTap Systems 3.9 Using the Device as a Reality Mediator 3.10 User Studies 3.11 Summary and Conclusions 3.12 Exercises, Problem Sets, and Homework 4 Comparametric Equations, Quantigraphic Image Processing, and Comparagraphic Rendering 4.1 Historical Background 4.2 The Wyckoff Principle and the Range of Light 4.3 Comparametric Image Processing: Comparing Differently Exposed Images of the Same Subject Matter 4.4 The Comparagram: Practical Implementations of Comparanalysis 4.5 Spatiotonal Photoquantigraphic Filters 4.6 Glossary of Functions 4.7 Exercises, Problem Sets, and Homework 5 Lightspace and Antihomomorphic Vector Spaces 5.1 Lightspace 5.2 The Lightspace Analysis Function 5.3 The "Spotflash" Primitive 5.4 LAF×LSF Imaging ("Lightspace") 5.5 Lightspace Subspaces 5.6 "Lightvector" Subspace 5.7 Painting with Lightvectors: Photographic/Videographic Origins and Applications of WearComp-Based Mediated Reality 5.8 Collaborative Mediated Reality Field Trials 5.9 Conclusions 5.10 Exercises, Problem Sets, and Homework 6 VideoOrbits: The Projective Geometry Renaissance 6.1 VideoOrbits 6.2 Background 6.3 Framework: Motion Parameter Estimation and Optical Flow 6.4 Multiscale Implementations in 2-D 6.5 Performance and Applications 6.6 AGC and the Range of Light 6.7 Joint Estimation of Both Domain and Range Coordinate Transformations 6.8 The Big Picture 6.9 Reality Window Manager 6.10 Application of Orbits: The Photonic Firewall 6.11 All the World's a Skinner Box 6.12 Blocking Spam with a Photonic Filter 6.13 Exercises, Problem Sets, and Homework Appendix A: Safety First! Appendix B: Multiambic Keyer for Use While Engaged in Other Activities B.1 Introduction B.2 Background and Terminology on Keyers B.3 Optimal Keyer Design: The Conformal Keyer B.4 The Seven Stages of a Keypress B.5 The Pentakeyer B.6 Redundancy B.7 Ordinally Conditional Modifiers B.8 Rollover B.8.1 Example of Rollover on a Cybernetic Keyer B.9 Further Increasing the Chordic Redundancy Factor: A More Expressive Keyer B.10 Including One Time Constant B.11 Making a Conformal Multiambic Keyer B.12 Comparison to Related Work B.13 Conclusion B.14 Acknowledgments Appendix C: WearCam GNUX Howto C.1 Installing GNUX on WearComps C.2 Getting Started C.3 Stop the Virus from Running C.4 Making Room for an Operating System C.5 Other Needed Files C.6 Defrag / 323 C.7 Fips C.8 Starting Up in GNUX with Ramdisk Appendix D: How to Build a Covert Computer Imaging System into Ordinary Looking Sunglasses D.1 The Move from Sixth-Generation WearComp to Seventh-Generation D.2 Label the Wires! D.3 Soldering Wires Directly to the Kopin CyberDisplay D.4 Completing the Computershades Bibliography Index
£127.76
John Wiley and Sons Ltd Understanding Vision
Book SynopsisIn recent years there have been major advances in understanding visual processing. This work brings together experts from various disciplines, ranging from computer science to neuropsychology, to discuss how the work carried out in their field fits into the broader context of vision research.Table of ContentsContructing the perception of surfaces from multiple cues, Kent A. Stevens' visual analysis and representation of spatial relations, Roger J. Watt; modern theories of Gestalt perception, Stephen J. Palmer; thinking visually, Kris N. Kirby and Stephen M. Kosslyn; perceiving and recognizing faces, Vicki Bruce; the breakdown approach to visual perception - neuropsychological studies of object recognition, Glyn W. Humphreys et al; mechanisms which mediate discrimination of 2-D spatial patterns in distributed images, Keith H. Ruddock; the analysis of 3-D shape - psychological principles and neural mechanisms, Andrew J. Parker et al; identification of disoriented objects - a dual-systems theory, Pierre Jolicoeur; surface layout from retinal flow, Mike Harris et al; neural facades - visual representations of static and moving form-and-colour-and-depth, Stephen Grossberg.
£37.00
O'Reilly Media Explainable AI for Practitioners
Book SynopsisExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.
£47.99
John Wiley & Sons Inc A Practical Introduction to Computer Vision with
Book SynopsisExplains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games Trade Review“Although there are many computer vision books on the market that offer a more comprehensive approach to explaining the computer vision concepts, extremely few offer such comprehensive practical examples. In this context, the book would be very welcome by beginner code developers." (Computing Reviews, 8 August 2014) Table of ContentsPreface xiii 1 Introduction 1 1.1 A Difficult Problem 1 1.2 The Human Vision System 2 1.3 Practical Applications of Computer Vision 3 1.4 The Future of Computer Vision 5 1.5 Material in This Textbook 6 1.6 Going Further with Computer Vision 7 2 Images 9 2.1 Cameras 9 2.1.1 The Simple Pinhole Camera Model 9 2.2 Images 10 2.2.1 Sampling 11 2.2.2 Quantisation 11 2.3 Colour Images 13 2.3.1 Red–Green–Blue (RGB) Images 14 2.3.2 Cyan–Magenta–Yellow (CMY) Images 17 2.3.3 YUV Images 17 2.3.4 Hue Luminance Saturation (HLS) Images 18 2.3.5 Other Colour Spaces 20 2.3.6 Some Colour Applications 20 2.4 Noise 22 2.4.1 Types of Noise 23 2.4.2 Noise Models 25 2.4.3 Noise Generation 26 2.4.4 Noise Evaluation 26 2.5 Smoothing 27 2.5.1 Image Averaging 27 2.5.2 Local Averaging and Gaussian Smoothing 28 2.5.3 Rotating Mask 30 2.5.4 Median Filter 31 3 Histograms 35 3.1 1D Histograms 35 3.1.1 Histogram Smoothing 36 3.1.2 Colour Histograms 37 3.2 3D Histograms 39 3.3 Histogram/Image Equalisation 40 3.4 Histogram Comparison 41 3.5 Back-projection 43 3.6 k-means Clustering 44 4 Binary Vision 49 4.1 Thresholding 49 4.1.1 Thresholding Problems 50 4.2 Threshold Detection Methods 51 4.2.1 Bimodal Histogram Analysis 52 4.2.2 Optimal Thresholding 52 4.2.3 Otsu Thresholding 54 4.3 Variations on Thresholding 56 4.3.1 Adaptive Thresholding 56 4.3.2 Band Thresholding 57 4.3.3 Semi-thresholding 58 4.3.4 Multispectral Thresholding 58 4.4 Mathematical Morphology 59 4.4.1 Dilation 60 4.4.2 Erosion 62 4.4.3 Opening and Closing 63 4.4.4 Grey-scale and Colour Morphology 65 4.5 Connectivity 66 4.5.1 Connectedness: Paradoxes and Solutions 66 4.5.2 Connected Components Analysis 67 5 Geometric Transformations 71 5.1 Problem Specification and Algorithm 71 5.2 Affine Transformations 73 5.2.1 Known Affine Transformations 74 5.2.2 Unknown Affine Transformations 75 5.3 Perspective Transformations 76 5.4 Specification of More Complex Transformations 78 5.5 Interpolation 78 5.5.1 Nearest Neighbour Interpolation 79 5.5.2 Bilinear Interpolation 79 5.5.3 Bi-Cubic Interpolation 80 5.6 Modelling and Removing Distortion from Cameras 80 5.6.1 Camera Distortions 81 5.6.2 Camera Calibration and Removing Distortion 82 6 Edges 83 6.1 Edge Detection 83 6.1.1 First Derivative Edge Detectors 85 6.1.2 Second Derivative Edge Detectors 92 6.1.3 Multispectral Edge Detection 97 6.1.4 Image Sharpening 98 6.2 Contour Segmentation 99 6.2.1 Basic Representations of Edge Data 99 6.2.2 Border Detection 102 6.2.3 Extracting Line Segment Representations of Edge Contours 105 6.3 Hough Transform 108 6.3.1 Hough for Lines 109 6.3.2 Hough for Circles 111 6.3.3 Generalised Hough 112 7 Features 115 7.1 Moravec Corner Detection 117 7.2 Harris Corner Detection 118 7.3 FAST Corner Detection 121 7.4 SIFT 122 7.4.1 Scale Space Extrema Detection 123 7.4.2 Accurate Keypoint Location 124 7.4.3 Keypoint Orientation Assignment 126 7.4.4 Keypoint Descriptor 127 7.4.5 Matching Keypoints 127 7.4.6 Recognition 127 7.5 Other Detectors 129 7.5.1 Minimum Eigenvalues 130 7.5.2 SURF 130 8 Recognition 131 8.1 Template Matching 131 8.1.1 Applications 131 8.1.2 Template Matching Algorithm 133 8.1.3 Matching Metrics 134 8.1.4 Finding Local Maxima or Minima 135 8.1.5 Control Strategies for Matching 137 8.2 Chamfer Matching 137 8.2.1 Chamfering Algorithm 137 8.2.2 Chamfer Matching Algorithm 139 8.3 Statistical Pattern Recognition 140 8.3.1 Probability Review 142 8.3.2 Sample Features 143 8.3.3 Statistical Pattern Recognition Technique 149 8.4 Cascade of Haar Classifiers 152 8.4.1 Features 154 8.4.2 Training 156 8.4.3 Classifiers 156 8.4.4 Recognition 158 8.5 Other Recognition Techniques 158 8.5.1 Support Vector Machines (SVM) 158 8.5.2 Histogram of Oriented Gradients (HoG) 159 8.6 Performance 160 8.6.1 Image and Video Datasets 160 8.6.2 Ground Truth 161 8.6.3 Metrics for Assessing Classification Performance 162 8.6.4 Improving Computation Time 165 9 Video 167 9.1 Moving Object Detection 167 9.1.1 Object of Interest 168 9.1.2 Common Problems 168 9.1.3 Difference Images 169 9.1.4 Background Models 171 9.1.5 Shadow Detection 179 9.2 Tracking 180 9.2.1 Exhaustive Search 181 9.2.2 Mean Shift 181 9.2.3 Dense Optical Flow 182 9.2.4 Feature Based Optical Flow 185 9.3 Performance 186 9.3.1 Video Datasets (and Formats) 186 9.3.2 Metrics for Assessing Video Tracking Performance 187 10 Vision Problems 189 10.1 Baby Food 189 10.2 Labels on Glue 190 10.3 O-rings 191 10.4 Staying in Lane 192 10.5 Reading Notices 193 10.6 Mailboxes 194 10.7 Abandoned and Removed Object Detection 195 10.8 Surveillance 196 10.9 Traffic Lights 197 10.10 Real Time Face Tracking 198 10.11 Playing Pool 199 10.12 Open Windows 200 10.13 Modelling Doors 201 10.14 Determining the Time from Analogue Clocks 202 10.15 Which Page 203 10.16 Nut/Bolt/Washer Classification 204 10.17 Road Sign Recognition 205 10.18 License Plates 206 10.19 Counting Bicycles 207 10.20 Recognise Paintings 208 References 209 Index 213
£44.60
John Wiley & Sons Inc Computer Vision in Vehicle Technology
Book SynopsisComputer Vision in Vehicle Technology: Land, Sea & Air Antonio M. Lopez, Universitat Autonoma de Barcelona, Spain Atsushi Imiya, Chiba University, Japan Tomas Pajdla, Czech Technical University, Prague Jose M.Table of ContentsList of Contributors ix Preface xi Abbreviations and Acronyms xiii 1 Computer Vision in Vehicles 1Reinhard Klette 1.1 Adaptive Computer Vision for Vehicles 1 1.1.1 Applications 1 1.1.2 Traffic Safety and Comfort 2 1.1.3 Strengths of (Computer) Vision 2 1.1.4 Generic and Specific Tasks 3 1.1.5 Multi-module Solutions 4 1.1.6 Accuracy, Precision, and Robustness 5 1.1.7 Comparative Performance Evaluation 5 1.1.8 There Are Many Winners 6 1.2 Notation and Basic Definitions 6 1.2.1 Images and Videos 6 1.2.2 Cameras 8 1.2.3 Optimization 10 1.3 Visual Tasks 12 1.3.1 Distance 12 1.3.2 Motion 16 1.3.3 Object Detection and Tracking 18 1.3.4 Semantic Segmentation 21 1.4 Concluding Remarks 23 Acknowledgments 23 2 Autonomous Driving 24Uwe Franke 2.1 Introduction 24 2.1.1 The Dream 24 2.1.2 Applications 25 2.1.3 Level of Automation 26 2.1.4 Important Research Projects 27 2.1.5 Outdoor Vision Challenges 30 2.2 Autonomous Driving in Cities 31 2.2.1 Localization 33 2.2.2 Stereo Vision-Based Perception in 3D 36 2.2.3 Object Recognition 43 2.3 Challenges 49 2.3.1 Increasing Robustness 49 2.3.2 Scene Labeling 50 2.3.3 Intention Recognition 52 2.4 Summary 52 Acknowledgments 54 3 Computer Vision for MAVs 55Friedrich Fraundorfer 3.1 Introduction 55 3.2 System and Sensors 57 3.3 Ego-Motion Estimation 58 3.3.1 State Estimation Using Inertial and Vision Measurements 58 3.3.2 MAV Pose from Monocular Vision 62 3.3.3 MAV Pose from Stereo Vision 63 3.3.4 MAV Pose from Optical Flow Measurements 65 3.4 3D Mapping 67 3.5 Autonomous Navigation 71 3.6 Scene Interpretation 72 3.7 Concluding Remarks 73 4 Exploring the Seafloor with Underwater Robots 75Rafael Garcia, Nuno Gracias, Tudor Nicosevici, Ricard Prados, Natalia Hurtos, Ricard Campos, Javier Escartin, Armagan Elibol, Ramon Hegedus and Laszlo Neumann 4.1 Introduction 75 4.2 Challenges of Underwater Imaging 77 4.3 Online Computer Vision Techniques 79 4.3.1 Dehazing 79 4.3.2 Visual Odometry 84 4.3.3 SLAM 87 4.3.4 Laser Scanning 91 4.4 Acoustic Imaging Techniques 92 4.4.1 Image Formation 92 4.4.2 Online Techniques for Acoustic Processing 95 4.5 Concluding Remarks 98 Acknowledgments 99 5 Vision-Based Advanced Driver Assistance Systems 100David Gerónimo, David Vázquez and Arturo de la Escalera 5.1 Introduction 100 5.2 Forward Assistance 101 5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA) 101 5.2.2 Traffic Sign Recognition (TSR) 103 5.2.3 Traffic Jam Assist (TJA) 105 5.2.4 Vulnerable Road User Protection 106 5.2.5 Intelligent Headlamp Control 109 5.2.6 Enhanced Night Vision (Dynamic Light Spot) 110 5.2.7 Intelligent Active Suspension 111 5.3 Lateral Assistance 112 5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS) 112 5.3.2 Lane Change Assistance (LCA) 115 5.3.3 Parking Assistance 116 5.4 Inside Assistance 117 5.4.1 Driver Monitoring and Drowsiness Detection 117 5.5 Conclusions and Future Challenges 119 5.5.1 Robustness 119 5.5.2 Cost 121 Acknowledgments 121 6 Application Challenges from a Bird’s-Eye View 122Davide Scaramuzza 6.1 Introduction to Micro Aerial Vehicles (MAVs) 122 6.1.1 Micro Aerial Vehicles (MAVs) 122 6.1.2 Rotorcraft MAVs 123 6.2 GPS-Denied Navigation 124 6.2.1 Autonomous Navigation with Range Sensors 124 6.2.2 Autonomous Navigation with Vision Sensors 125 6.2.3 SFLY: Swarm of Micro Flying Robots 126 6.2.4 SVO, a Visual-Odometry Algorithm for MAVs 126 6.3 Applications and Challenges 127 6.3.1 Applications 127 6.3.2 Safety and Robustness 128 6.4 Conclusions 132 7 Application Challenges of Underwater Vision 133Nuno Gracias, Rafael Garcia, Ricard Campos, Natalia Hurtos, Ricard Prados, ASM Shihavuddin, Tudor Nicosevici, Armagan Elibol, Laszlo Neumann and Javier Escartin 7.1 Introduction 133 7.2 Offline Computer Vision Techniques for Underwater Mapping and Inspection 134 7.2.1 2D Mosaicing 134 7.2.2 2.5D Mapping 144 7.2.3 3D Mapping 146 7.2.4 Machine Learning for Seafloor Classification 154 7.3 Acoustic Mapping Techniques 157 7.4 Concluding Remarks 159 8 Closing Notes 161Antonio M. López References 164 Index 195
£67.46
John Wiley & Sons Inc Computer Vision for Structural Dynamics and
Book SynopsisProvides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice. Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tTable of ContentsList of Figures ix List of Tables xv Series Preface xvii Preface xix About the Companion Website xxi 1 Introduction 1 1.1 Structural Health Monitoring: A Quick Review 1 1.2 Computer Vision Sensors for Structural Health Monitoring 3 1.3 Organization of the Book 7 2 Development of a Computer Vision Sensor for Structural Displacement Measurement 11 2.1 Vision Sensor System Hardware 11 2.2 Vision Sensor System Software: Template-Matching Techniques 15 2.2.1 Area-Based Template Matching 16 2.2.2 Feature-Based Template Matching 20 2.3 Coordinate Conversion and Scaling Factors 22 2.3.1 Camera Calibration Method 23 2.3.2 Practical Calibration Method 25 2.4 Representative Template Matching Algorithms 28 2.4.1 Intensity-Based UCC Technique 28 2.4.2 Gradient-Based Robust OCM Technique 33 2.4.3 Vision Sensor Software Package and Operation 39 2.5 Summary 40 3 Performance Evaluation Through Laboratory and Field Tests 43 3.1 Seismic Shaking Table Test 43 3.2 Shaking Table Test of Frame Structure 1 46 3.2.1 Test Description 46 3.2.2 Subpixel Resolution 47 3.2.3 Performance When Tracking Artificial Targets 48 3.2.4 Performance When Tracking Natural Targets 49 3.2.5 Error Quantification 51 3.2.6 Evaluation of OCM and UCC Robustness 51 3.3 Seismic Shaking Table Test of Frame Structure 2 56 3.4 Free Vibration Test of a Beam Structure 59 3.4.1 Test Description 59 3.4.2 Evaluation of the Practical Calibration Method 60 3.5 Field Test of a Pedestrian Bridge 63 3.6 Field Test of a Highway Bridge 66 3.7 Field Test of Two Railway Bridges 67 3.7.1 Test Description 69 3.7.2 Daytime Measurements 72 3.7.3 Nighttime Measurements 72 3.7.4 Field Performance Evaluation 75 3.8 Remote Measurement of the Vincent Thomas Bridge 81 3.9 Remote Measurement of the Manhattan Bridge 82 3.10 Summary 87 4 Application in Modal Analysis, Model Updating, and Damage Detection 89 4.1 Experimental Modal Analysis 91 4.1.1 Modal Analysis of a Frame 91 4.1.2 Modal Analysis of a Beam 97 4.2 Model Updating as a Frequency-Domain Optimization Problem 101 4.3 Damage Detection 108 4.3.1 Mode Shape Curvature-Based Damage Index 108 4.3.2 Test Description 109 4.3.3 Damage Detection Results 110 4.4 Summary 112 5 Application in Model Updating of Railway Bridges under Trainloads 115 5.1 Field Measurement of Bridge Displacement under Trainloads 116 5.2 Formulation of the Finite Element Model 118 5.2.1 Modeling the Train-Track-Bridge Interaction 118 5.2.2 Finite Element Model of the Railway Bridge 120 5.3 Sensitivity Analysis and Finite Element Model Updating 121 5.3.1 Model Updating as a Time-Domain Optimization Problem 122 5.3.2 Sensitivity Analysis of Displacement and Acceleration Responses 123 5.3.3 Finite Element Model Updating 127 5.4 Dynamic Characteristics of Short-Span Bridges under Trainloads 130 5.5 Summary 136 6 Application in Simultaneously Identifying Structural Parameters and Excitation Forces 139 6.1 Simultaneous Identification Using Vision-Based Displacement Measurements 140 6.1.1 Structural Parameter Identification as a Time-Domain Optimization Problem 141 6.1.2 Force Identification Based on Structural Displacement Measurements 142 6.1.3 Simultaneous Identification Procedure 144 6.2 Numerical Example 146 6.2.1 Robustness to Noise and Number of Sensors 147 6.2.2 Robustness to Initial Stiffness Values 150 6.2.3 Robustness to Damping Ratio Values 150 6.3 Experimental Validation 154 6.3.1 Test Description 154 6.3.2 Identification Results 155 6.4 Summary 157 7 Application in Estimating Cable Force 171 7.1 Vision Sensor for Estimating Cable Force 172 7.1.1 Vibration Method 172 7.1.2 Procedure for Vision-Based Cable Tension Estimation 173 7.2 Implementation in the Hard Rock Stadium Renovation Project 174 7.2.1 Hard Rock Stadium 175 7.2.2 Test Description 176 7.2.3 Estimating and Validating Cable Force 178 7.3 Implementation in the Bronx-Whitestone Bridge Suspender Replacement Project 184 7.3.1 Bronx-Whitestone Bridge 184 7.3.2 Estimating Suspender Tension 185 7.4 Summary 187 8 Achievements, Challenges, and Opportunities 191 8.1 Capabilities of Vision-Based Displacement Sensors: A Summary 191 8.1.1 Artificial vs. Natural Targets 192 8.1.2 Single-Point vs. Multipoint Measurements 192 8.1.3 Pixel vs. Subpixel Resolution 193 8.1.4 2D vs. 3D Measurements 194 8.1.5 Real Time vs. Post Processing 194 8.2 Sources of Error in Vision-Based Displacement Sensors 195 8.2.1 Camera Motion 196 8.2.2 Coordinate Conversion 197 8.2.3 Hardware Limitations 198 8.2.4 Environmental Sources 198 8.3 Vision-Based Displacement Sensors for Structural Health Monitoring 199 8.3.1 Dynamic Displacement Measurement 199 8.3.2 Modal Property Identification 201 8.3.3 Model Updating and Damage Detection 202 8.3.4 Cable Force Estimation 203 8.4 Other Civil and Structural Engineering Applications 204 8.4.1 Automated Machine Visual Inspection 204 8.4.2 Onsite Construction Tracking and Safety Monitoring 206 8.4.3 Vehicle Load Estimation 206 8.4.4 Other Applications 207 8.5 Future Research Directions 208 Appendix: Fundamentals of Digital Image Processing Using MATLAB 211 A.1 Digital Image Representation 211 A.2 Noise Removal 214 A.3 Edge Detection 216 A.4 Discrete Fourier Transform 217 References 221 Index 229
£100.76
John Wiley & Sons Inc Image Segmentation Principles Techniques and
Book SynopsisImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authorssuch as convolutional neural networks, graph convolutional networks, deformable convolution, and model compressionto assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.Table of ContentsPreface About the Authors List of Abbreviations Part One: Principle 1 Introduction to Image Segmentation 2 Principles of Clustering 3 Principles of Mathematical Morphology 4 Principles of Neural Network Part Two: Methods 5 Fast and Robust Image Segmentation Using Clustering 6 Fast Image Segmentation Using Watershed Transform 7 Superpixel-based Fast Image Segmentation Part Three: Application 8 Image Segmentation for Traffic Scene Analysis 9 Image Segmentation for Medical Analysis 10 Image Segmentation for Remote Sensing Analysis 11 Image Segmentation for Material Analysis
£99.00
John Wiley & Sons Inc Machine Learning Applications
Book SynopsisMachine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space explor
£88.65
