Image processing Books

Book SynopsisThis text provides an exceptional introduction to the multidisciplinary field of mobile robotics using hands-on examples in ROS 2 enabling students to explore concepts either in a simulation or using their own robot hardware. The new edition includes coverage of HRI, robot ethics, and AI techniques for end-to-end robot control.Trade Review'This book is an indispensable tool for any - both pre-university and university - course on mobile robotics. In relation to the first edition, this current one has been sufficiently updated. I recommend this book to researchers - particularly those who study localization or mapping - and doctoral students who are interested in investigating the latest approaches and techniques in the mobile robotics field.' Ramon Gonzalez Sanchez, Computing Reviews'… a great resource for an intermediate or advanced course on mobile robotics.' R. S. Stansbury, ChoiceTable of ContentsAcknowledgments; Preface; 1. Overview and motivation; 2. Fundamental problems; Part I. Locomotion and Perception: 3. Mobile robot hardware; 4. Non-visual sensors and algorithms; 5. Visual sensors and algorithms; Part II. Representation and Planning: 6. Deep learning for robots; 7. Planning in, representing and reasoning about space; 8. System control; 9. Pose maintenance and localization; 10. Mapping and related tasks; 11. Robot collectives; 12. Human-robot interaction; 13. Robot ethics; 14. Robots in practice; 15. The future of mobile robotics; Appendix A. Fictional robots; Appendix B. Probability and statistics; Appendix C. Linear systems, matrices and filtering; Appendix D. Markov models; Bibliography; Index.

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

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

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Book SynopsisTaking a critical look at the realities that have shaped the first stage of Barack Obama's presidency, Morton Keller offers a history-focused examination of Obama's developing style of governing, with particular attention to his signature policies of the stimulus, financial, and health care reforms.Table of Contents Foreword Acknowledgments Introduction Chapter One: Governing Chapter Two: Fixing the Economy Chapter Three: Into the Maze: Health Care Chapter Four: Contexts: Analogy and Ideology Chapter Five: Unfinished Business: Policy and Politics Notes About the Author Index

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Book SynopsisData registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment.Table of ContentsPreface xi Acknowledgements xiii 1 Introduction 1 1.1 The History of Image Registration 1 1.2 Definition of Registration 2 1.3 What is Motion Estimation 3 1.4 Video Quality Assessment 5 1.5 Applications 5 1.5.1 Video Processing 5 1.5.2 Medical Applications 7 1.5.3 Security Applications 8 1.5.4 Military and Satellite Applications 10 1.5.5 Reconstruction Applications 11 1.6 Organization of the Book 12 References 13 2 Registration for Video Coding 15 2.1 Introduction 15 2.2 Motion Estimation Technique 16 2.2.1 Block-Based Motion Estimation Techniques 16 2.3 Registration and Standards for Video Coding 30 2.3.1 H.264 30 2.3.2 H.265 34 2.4 Evaluation Criteria 35 2.4.1 Dataset 35 2.4.2 Motion-Compensated Prediction Error (MCPE) in dB 38 2.4.3 Entropy in bpp 39 2.4.4 Angular Error in Degrees 40 2.5 Objective Quality Assessment 41 2.5.1 Full-Reference Quality Assessment 41 2.5.2 No-Reference and Reduced-Reference Quality Metrics 44 2.5.3 Temporal Masking in Video Quality Assessment 46 2.6 Conclusion 48 2.7 Exercises 49 References 49 3 Registration for Motion Estimation and Object Tracking 53 3.1 Introduction 53 3.1.1 Mathematical Notation 54 3.2 Optical Flow 55 3.2.1 Horn–Schunk Method 56 3.2.2 Lukas–Kanade Method 56 3.2.3 Applications of Optical Flow for Motion Estimation 57 3.3 Efficient Discriminative Features for Motion Estimation 61 3.3.1 Invariant Features 62 3.3.2 Optimization Stage 64 3.4 Object Tracking 64 3.4.1 KLT Tracking 64 3.4.2 Motion Filtering 66 3.4.3 Multiple Object Tracking 67 3.5 Evaluating Motion Estimation and Tracking 68 3.5.1 Metrics for Motion Detection 68 3.5.2 Metrics for Motion Tracking 69 3.5.3 Metrics for Efficiency 70 3.5.4 Datasets 70 3.6 Conclusion 70 3.7 Exercise 75 References 75 4 Face Alignment and Recognition Using Registration 79 4.1 Introduction 79 4.2 Unsupervised Alignment Methods 80 4.2.1 Natural Features: Gradient Features 81 4.2.2 Dense Grids: Non-rigid Non-affine Transformations 81 4.3 Supervised Alignment Methods 83 4.3.1 Generative Models 84 4.3.2 Discriminative Approaches 86 4.4 3D Alignment 88 4.4.1 Hausdorff Distance Matching 88 4.4.2 Iterative Closest Point (ICP) 89 4.4.3 Multistage Alignment 89 4.5 Metrics for Evaluation 90 4.5.1 Evaluating Face Recognition 90 4.5.2 Evaluating Face Alignment 90 4.5.3 Testing Protocols and Benchmarks 91 4.5.4 Datasets 92 4.6 Conclusion 94 4.7 Exercise 94 References 94 5 Remote Sensing Image Registration in the Frequency Domain 97 5.1 Introduction 97 5.2 Challenges in Remote Sensing Imaging 100 5.3 Satellite Image Registration in the Fourier Domain 102 5.3.1 Translation Estimation Using Correlation 102 5.4 Correlation Methods 103 5.5 Subpixel Shift Estimation in the Fourier Domain 107 5.6 FFT-Based Scale-Invariant Image Registration 111 5.7 Motion Estimation in the Frequency Domain for Remote Sensing Image Sequences 115 5.7.1 Quad-Tree Phase Correlation 116 5.7.2 Shape Adaptive Motion Estimation in the Frequency Domain 119 5.7.3 Optical Flow in the Fourier Domain 120 5.8 Evaluation Process and Related Datasets 122 5.8.1 Remote Sensing Image Datasets 123 5.9 Conclusion 123 5.10 Exercise – Practice 124 References 124 6 Structure from Motion 129 6.1 Introduction 129 6.2 Pinhole Model 131 6.3 Camera Calibration 133 6.4 Correspondence Problem 135 6.5 Epipolar Geometry 136 6.6 Projection Matrix Recovery 140 6.6.1 Triangulation 141 6.7 Feature Detection and Registration 141 6.7.1 Auto-correlation 143 6.7.2 Harris Detector 143 6.7.3 SIFT Feature Detector 146 6.8 Reconstruction of 3D Structure and Motion 148 6.8.1 Simultaneous Localization and Mapping 149 6.8.2 Registration for Panoramic View 150 6.9 Metrics and Datasets 152 6.9.1 Datasets for Performance Evaluation 154 6.10 Conclusion 155 6.11 Exercise – Practice 155 References 155 7 Medical Image Registration Measures 162 7.1 Introduction 162 7.2 Feature-Based Registration 163 7.2.1 Generalized Iterative Closest Point Algorithm 164 7.2.2 Hierarchical Maximization 165 7.3 Intensity-Based Registration 165 7.3.1 Voxels as Features 166 7.3.2 Special Case: Spatially Determined Correspondences 168 7.3.3 Intensity Difference Measures 169 7.3.4 Correlation Coefficient 170 7.3.5 Pseudo-likelihood Measures 171 7.3.6 General Implementation Using Joint Histograms 181 7.4 Transformation Spaces and Optimization 184 7.4.1 Rigid Transformations 185 7.4.2 Similarity Transformations 186 7.4.3 Affine Transformations 186 7.4.4 Projective Transformations 187 7.4.5 Polyaffine Transformations 187 7.4.6 Free-Form Transformations: ‘Small Deformation’ Model 188 7.4.7 Free-Form Transformations: ‘Large Deformation’ Models 189 7.5 Conclusion 193 7.6 Exercise 193 7.6.1 Implementation Guidelines 195 References 196 8 Video Restoration Using Motion Information 201 8.1 Introduction 201 8.2 History of Video and Film Restoration 203 8.3 Restoration of Video Noise and Grain 206 8.4 Restoration Algorithms for Video Noise 208 8.5 Instability Correction Using Registration 211 8.6 Estimating and Removing Flickering 214 8.7 Dirt Removal in Video Sequences 217 8.8 Metrics in Video Restoration 221 8.9 Conclusions 225 8.10 Exercise – Practice 225 References 225 Index 229

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Book SynopsisComputer Vision and Imaging in Intelligent Transportation Systems Robert P.Table of ContentsList of Contributors xiii Preface xvii Acknowledgments xxi About the Companion Website xxiii 1 Introduction 1 Raja Bala and Robert P. Loce 1.1 Law Enforcement and Security 1 1.2 Efficiency 4 1.3 Driver Safety and Comfort 5 1.4 A Computer Vision Framework for Transportation Applications 7 1.4.1 Image and Video Capture 8 1.4.2 Data Preprocessing 8 1.4.3 Feature Extraction 9 1.4.4 Inference Engine 10 1.4.5 Data Presentation and Feedback 11 Part I Imaging from the Roadway Infrastructure 15 2 Automated License Plate Recognition 17 Aaron Burry and Vladimir Kozitsky 2.1 Introduction 17 2.2 Core ALPR Technologies 18 2.2.1 License Plate Localization 19 2.2.2 Character Segmentation 24 2.2.3 Character Recognition 28 2.2.4 State Identification 38 3 Vehicle Classification 47 Shashank Deshpande, Wiktor Muron and Yang Cai 3.1 Introduction 47 3.2 Overview of the Algorithms 48 3.3 Existing AVC Methods 48 3.4 LiDAR Imaging-Based 49 3.4.1 LiDAR Sensors 49 3.4.2 Fusion of LiDAR and Vision Sensors 50 3.5 Thermal Imaging-Based 53 3.5.1 Thermal Signatures 53 3.5.2 Intensity Shape-Based 56 3.6 Shape- and Profile-Based 58 3.6.1 Silhouette Measurements 60 3.6.2 Edge-Based Classification 65 3.6.3 Histogram of Oriented Gradients 67 3.6.4 Haar Features 68 3.6.5 Principal Component Analysis 69 3.7 Intrinsic Proportion Model 72 3.8 3D Model-Based Classification 74 3.9 SIFT-Based Classification 74 3.10 Summary 75 4 Detection of Passenger Compartment Violations 81 Orhan Bulan, Beilei Xu, Robert P. Loce and Peter Paul 4.1 Introduction 81 4.2 Sensing within the Passenger Compartment 82 4.2.1 Seat Belt Usage Detection 82 4.2.2 Cell Phone Usage Detection 83 4.2.3 Occupancy Detection 83 4.3 Roadside Imaging 84 4.3.1 Image Acquisition Setup 84 4.3.2 Image Classification Methods 85 4.3.3 Detection-Based Methods 94 5 Detection of Moving Violations 101 Wencheng Wu, Orhan Bulan, Edgar A. Bernal and Robert P. Loce 5.1 Introduction 101 5.2 Detection of Speed Violations 101 5.2.1 Speed Estimation from Monocular Cameras 102 5.2.2 Speed Estimation from Stereo Cameras 108 5.2.3 Discussion 115 5.3 Stop Violations 115 5.3.1 Red Light Cameras 115 5.4 Other Violations 125 5.4.1 Wrong-Way Driver Detection 125 5.4.2 Crossing Solid Lines 126 6 Traffic Flow Analysis 131 Rodrigo Fernandez, Muhammad Haroon Yousaf, Timothy J. Ellis, Zezhi Chen and Sergio A. Velastin 6.1 What is Traffic Flow Analysis? 131 6.1.1 Traffic Conflicts and Traffic Analysis 131 6.1.2 Time Observation 132 6.1.3 Space Observation 133 6.1.4 The Fundamental Equation 133 6.1.5 The Fundamental Diagram 133 6.1.6 Measuring Traffic Variables 134 6.1.7 Road Counts 135 6.1.8 Junction Counts 135 6.1.9 Passenger Counts 136 6.1.10 Pedestrian Counts 136 6.1.11 Speed Measurement 136 6.2 The Use of Video Analysis in Intelligent Transportation Systems 137 6.2.1 Introduction 137 6.2.2 General Framework for Traffic Flow Analysis 137 6.2.3 Application Domains 143 6.3 Measuring Traffic Flow from Roadside CCTV Video 144 6.3.1 Video Analysis Framework 144 6.3.2 Vehicle Detection 146 6.3.3 Background Model 146 6.3.4 Counting Vehicles 149 6.3.5 Tracking 150 6.3.6 Camera Calibration 150 6.3.7 Feature Extraction and Vehicle Classification 152 6.3.8 Lane Detection 153 6.3.9 Results 155 6.4 Some Challenges 156 7 Intersection Monitoring Using Computer Vision Techniques for Capacity, Delay, and Safety Analysis 163 Brendan Tran Morris and Mohammad Shokrolah Shirazi 7.1 Vision-Based Intersection Analysis: Capacity, Delay, and Safety 163 7.1.1 Intersection Monitoring 163 7.1.2 Computer Vision Application 164 7.2 System Overview 165 7.2.1 Tracking Road Users 166 7.2.2 Camera Calibration 169 7.3 Count Analysis 171 7.3.1 Vehicular Counts 171 7.3.2 Nonvehicular Counts 173 7.4 Queue Length Estimation 173 7.4.1 Detection-Based Methods 174 7.4.2 Tracking-Based Methods 175 7.5 Safety Analysis 177 7.5.1 Behaviors 178 7.5.2 Accidents 182 7.5.3 Conflicts 185 7.6 Challenging Problems and Perspectives 187 7.6.1 Robust Detection and Tracking 187 7.6.2 Validity of Prediction Models for Conflict and Collisions 188 7.6.3 Cooperating Sensing Modalities 189 7.6.4 Networked Traffic Monitoring Systems 189 7.7 Conclusion 189 8 Video-Based Parking Management 195 Oliver Sidla and Yuriy Lipetski 8.1 Introduction 195 8.2 Overview of Parking Sensors 197 8.3 Introduction to Vehicle Occupancy Detection Methods 200 8.4 Monocular Vehicle Detection 200 8.4.1 Advantages of Simple 2D Vehicle Detection 200 8.4.2 Background Model–Based Approaches 200 8.4.3 Vehicle Detection Using Local Feature Descriptors 202 8.4.4 Appearance-Based Vehicle Detection 203 8.4.5 Histograms of Oriented Gradients 204 8.4.6 LBP Features and LBP Histograms 207 8.4.7 Combining Detectors into Cascades and Complex Descriptors 208 8.4.8 Case Study: Parking Space Monitoring Using a Combined Feature Detector 208 8.4.9 Detection Using Artificial Neural Networks 211 8.5 Introduction to Vehicle Detection with 3D Methods 213 8.6 Stereo Vision Methods 215 8.6.1 Introduction to Stereo Methods 215 8.6.2 Limits on the Accuracy of Stereo Reconstruction 216 8.6.3 Computing the Stereo Correspondence 217 8.6.4 Simple Stereo for Volume Occupation Measurement 218 8.6.5 A Practical System for Parking Space Monitoring Using a Stereo System 218 8.6.6 Detection Methods Using Sparse 3D Reconstruction 220 9 Video Anomaly Detection 227 Raja Bala and Vishal Monga 9.1 Introduction 227 9.2 Event Encoding 228 9.2.1 Trajectory Descriptors 229 9.2.2 Spatiotemporal Descriptors 231 9.3 Anomaly Detection Models 233 9.3.1 Classification Methods 233 9.3.2 Hidden Markov Models 234 9.3.3 Contextual Methods 234 9.4 Sparse Representation Methods for Robust Video Anomaly Detection 236 9.4.1 Structured Anomaly Detection 237 9.4.2 Unstructured Video Anomaly Detection 243 9.4.3 Experimental Setup and Results 245 9.5 Conclusion and Future Research 253 Part II Imaging from and within the Vehicle 257 10 Pedestrian Detection 259 Shashank Deshpande and Yang Cai 10.1 Introduction 259 10.2 Overview of the Algorithms 259 10.3 Thermal Imaging 260 10.4 Background Subtraction Methods 261 10.4.1 Frame Subtraction 261 10.4.2 Approximate Median 262 10.4.3 Gaussian Mixture Model 263 10.5 Polar Coordinate Profile 263 10.6 Image-Based Features 265 10.6.1 Histogram of Oriented Gradients 265 10.6.2 Deformable Parts Model 266 10.6.3 LiDAR and Camera Fusion–Based Detection 266 10.7 LiDAR Features 268 10.7.1 Preprocessing Module 268 10.7.2 Feature Extraction Module 268 10.7.3 Fusion Module 268 10.7.4 LIPD Dataset 270 10.7.5 Overview of the Algorithm 270 10.7.6 LiDAR Module 272 10.7.7 Vision Module 275 10.7.8 Results and Discussion 276 10.7.8.1 LiDAR Module 276 10.7.8.2 Vision Module 276 10.8 Summary 280 11 Lane Detection and Tracking Problems in Lane Departure Warning Systems 283 Gianni Cario, Alessandro Casavola and Marco Lupia 11.1 Introduction 283 11.2 LD: Algorithms for a Single Frame 285 11.2.1 Image Preprocessing 285 11.2.2 Edge Extraction 287 11.2.3 Stripe Identification 291 11.2.4 Line Fitting 294 11.3 LT Algorithms 297 11.3.1 Recursive Filters on Subsequent N frames 298 11.3.2 Kalman Filter 298 11.4 Implementation of an LD and LT Algorithm 299 11.4.1 Simulations 300 11.4.2 Test Driving Scenario 300 11.4.3 Driving Scenario: Lane Departures at Increasing Longitudinal Speed 300 11.4.4 The Proposed Algorithm 302 11.4.5 Conclusions 303 12 Vision-Based Integrated Techniques for Collision Avoidance Systems 305 Ravi Satzoda and Mohan Trivedi 12.1 Introduction 305 12.2 Related Work 307 12.3 Context Definition for Integrated Approach 307 12.4 ELVIS: Proposed Integrated Approach 308 12.4.1 Vehicle Detection Using Lane Information 309 12.4.2 Improving Lane Detection using On-Road Vehicle Information 312 12.5 Performance Evaluation 313 12.5.1 Vehicle Detection in ELVIS 313 12.5.2 Lane Detection in ELVIS 316 12.6 Concluding Remarks 319 13 Driver Monitoring 321 Raja Bala and Edgar A. Bernal 13.1 Introduction 321 13.2 Video Acquisition 322 13.3 Face Detection and Alignment 323 13.4 Eye Detection and Analysis 325 13.5 Head Pose and Gaze Estimation 326 13.5.1 Head Pose Estimation 326 13.5.2 Gaze Estimation 328 13.6 Facial Expression Analysis 332 13.7 Multimodal Sensing and Fusion 334 13.8 Conclusions and Future Directions 336 14 Traffic Sign Detection and Recognition 343 Hasan Fleyeh 14.1 Introduction 343 14.2 Traffic Signs 344 14.2.1 The European Road and Traffic Signs 344 14.2.2 The American Road and Traffic Signs 347 14.3 Traffic Sign Recognition 347 14.4 Traffic Sign Recognition Applications 348 14.5 Potential Challenges 349 14.6 Traffic Sign Recognition System Design 349 14.6.1 Traffic Signs Datasets 352 14.6.2 Colour Segmentation 354 14.6.3 Traffic Sign's Rim Analysis 359 14.6.4 Pictogram Extraction 364 14.6.5 Pictogram Classification Using Features 365 14.7 Working Systems 369 15 Road Condition Monitoring 375 Matti Kutila, Pasi Pyykonen, Johan Casselgren and Patrik Jonsson 15.1 Introduction 375 15.2 Measurement Principles 376 15.3 Sensor Solutions 377 15.3.1 Camera-Based Friction Estimation Systems 377 15.3.2 Pavement Sensors 379 15.3.3 Spectroscopy 380 15.3.4 Roadside Fog Sensing 382 15.3.5 In-Vehicle Sensors 383 15.4 Classification and Sensor Fusion 386 15.5 Field Studies 390 15.6 Cooperative Road Weather Services 394 15.7 Discussion and Future Work 395 Index 399

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Book SynopsisComputer-aided automatic processing of images requires the control of a series of operations, which this book analyzes. Knowing the statistical properties of images, sampling them to reduce the observable world to a series of discrete values, restoring images in order to correct degradations – all these operations are explained here, together with the mathematical tools they require. Topics covered include fractal representation, mathematical morphology, wavelet representations and the detection and description of contours and shapes.Table of ContentsChapter 1. Statistical properties of images (Henri Maître). Chapter 2. Image sampling and fractal representation (Henri Maître). Chapter 3. Discrete representations (Isabelle Bloch). Chapter 4. Restoration of images (Henri Maître). Chapter 5. Mathematical morphology (Isabelle Bloch). Chapter 6. Markov fields (Florence Tupin and Marc Sigelle). Chapter 7. Wavelets and image processing (Béatrice Pesquet-Popescu and Jean-Christophe Pesquet). Chapter 8. Partial differential equations (Yann Gousseau). Chapter 9. Preprocessing (Henri Maître). Chapter 10. Detection of contours in images (Henri Maître). Chapter 11. Segmentation by regions (Henri Maître). Chapter 12. Textures (Henri Maître). Chapter 13. Description of contours and shapes (Henri Maître). List of Authors. Index.

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Book SynopsisDuring the last decade, image and signal compression for storage and transmission purpose has seen a great expansion. But what about medical data compression? Should a medical image or a physiological signal be processed and compressed like any other data? The progress made in imaging systems, storing systems and telemedicine makes compression in this field particularly interesting. However, this compression has to be adapted to the specificities of biomedical data which contain diagnosis information. As such, this book offers an overview of compression techniques applied to medical data, including: physiological signals, MRI, X-ray, ultrasound images, static and dynamic volumetric images. Researchers, clinicians, engineers and professionals in this area, along with postgraduate students in the signal and image processing field, will find this book to be of great interest.Table of ContentsPreface xiii Chapter 1. Relevance of Biomedical Data Compression 1 Jean-Yves TANGUY, Pierre JALLET, Christel LE BOZEC and Guy FRIJA 1.1. Introduction 1 1.2. The management of digital data using PACS 2 1.2.1. Usefulness of PACS 2 1.2.2. The limitations of installing a PACS 3 1.3. The increasing quantities of digital data 4 1.3.1. An example from radiology 4 1.3.2. An example from anatomic pathology 6 1.3.3. An example from cardiology with ECG 7 1.3.4. Increases in the number of explorative examinations 8 1.4. Legal and practical matters 8 1.5. The role of data compression. 9 1.6. Diagnostic quality 10 1.6.1. Evaluation 10 1.6.2. Reticence 11 1.7. Conclusion 12 1.8. Bibliography 12 Chapter 2. State of the Art of Compression Methods 15 Atilla BASKURT 2.1. Introduction 15 2.2. Outline of a generic compression technique 16 2.2.1. Reducing redundancy 17 2.2.2. Quantizing the decorrelated information 18 2.2.3. Coding the quantized values 18 2.2.4. Compression ratio, quality evaluation 20 2.3. Compression of still images 21 2.3.1. JPEG standard 22 2.3.1.1. Why use DCT? 22 2.3.1.2. Quantization 24 2.3.1.3. Coding 24 2.3.1.4. Compression of still color images with JPEG 25 2.3.1.5. JPEG standard: conclusion 26 2.3.2. JPEG 2000 standard 27 2.3.2.1. Wavelet transform 27 2.3.2.2. Decomposition of images with the wavelet transform 27 2.3.2.3. Quantization and coding of subbands 29 2.3.2.4. Wavelet-based compression methods, serving as references 30 2.3.2.5. JPEG 2000 standard 31 2.4. The compression of image sequences 33 2.4.1. DCT-based video compression scheme 34 2.4.2. A history of and comparison between video standards 36 2.4.3. Recent developments in video compression 38 2.5. Compressing 1D signals 38 2.6. The compression of 3D objects 39 2.7. Conclusion and future developments 39 2.8. Bibliography 40 Chapter 3. Specificities of Physiological Signals and Medical Images 43 Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Jean-Yves TANGUY, Elsa ANGELINI, Christel LE BOZEC and Jean-Jacques LE JEUNE 3.1. Introduction 43 3.2. Characteristics of physiological signals 44 3.2.1. Main physiological signals 44 3.2.1.1. Electroencephalogram (EEG) 44 3.2.1.2. Evoked potential (EP) 45 3.2.1.3. Electromyogram (EMG) 45 3.2.1.4. Electrocardiogram (ECG) 46 3.2.2. Physiological signal acquisition 46 3.2.3. Properties of physiological signals 46 3.2.3.1. Properties of EEG signals 46 3.2.3.2. Properties of ECG signals 48 3.3. Specificities of medical images 50 3.3.1. The different features of medical imaging formation processes 50 3.3.1.1. Radiology 51 3.3.1.2. Magnetic resonance imaging (MRI) 54 3.3.1.3. Ultrasound 58 3.3.1.4. Nuclear medicine 62 3.3.1.5. Anatomopathological imaging 66 3.3.1.6. Conclusion 68 3.3.2. Properties of medical images 69 3.3.2.1. The size of images 70 3.3.2.2. Spatial and temporal resolution 71 3.3.2.3. Noise in medical images 72 3.4. Conclusion 73 3.5. Bibliography 74 Chapter 4. Standards in Medical Image Compression 77 Bernard GIBAUD and Joël CHABRIAIS 4.1. Introduction 77 4.2. Standards for communicating medical data 79 4.2.1. Who creates the standards, and how? 79 4.2.2. Standards in the healthcare sector 80 4.2.2.1. Technical committee 251 of CEN 80 4.2.2.2. Technical committee 215 of the ISO 80 4.2.2.3. DICOM Committee 80 4.2.2.4.Health Level Seven (HL7) 85 4.2.2.5. Synergy between the standards bodies 86 4.3. Existing standards for image compression 87 4.3.1. Image compression 87 4.3.2. Image compression in the DICOM standard 89 4.3.2.1. The coding of compressed images in DICOM 89 4.3.2.2. The types of compression available 92 4.3.2.3. Modes of access to compressed data 95 4.4. Conclusion 99 4.5. Bibliography 99 Chapter 5. Quality Assessment of Lossy Compressed Medical Images 101 Christine CAVARO-MÉNARD, Patrick LE CALLET, Dominique BARBA and Jean-Yves TANGUY 5.1. Introduction 101 5.2. Degradations generated by compression norms and their consequences in medical imaging 102 5.2.1. The block effect 102 5.2.2. Fading contrast in high spatial frequencies 103 5.3. Subjective quality assessment 105 5.3.1. Protocol evaluation 105 5.3.2. Analyzing the diagnosis reliability 106 5.3.2.1. ROC analysis 108 5.3.2.2. Analyses that are not based on the ROC method 111 5.3.3. Analyzing the quality of diagnostic criteria 111 5.3.4. Conclusion 114 5.4. Objective quality assessment 114 5.4.1. Simple signal-based metrics 115 5.4.2. Metrics based on texture analysis 115 5.4.3. Metrics based on a model version of the HVS 117 5.4.3.1. Luminance adaptation 117 5.4.3.2. Contrast sensivity 118 5.4.3.3. Spatio-frequency decomposition 118 5.4.3.4. Masking effect 119 5.4.3.5. Visual distortion measures 120 5.4.4. Analysis of the modification of quantitative clinical parameters 123 5.5. Conclusion 125 5.6. Bibliography 125 Chapter 6. Compression of Physiological Signals 129 Amine NAÏT-ALI 6.1. Introduction 129 6.2. Standards for coding physiological signals 130 6.2.1. CEN/ENV 1064 Norm 130 6.2.2. ASTM 1467 Norm 130 6.2.3. EDF norm 130 6.2.4. Other norms 131 6.3. EEG compression 131 6.3.1. Time-domain EEG compression 131 6.3.2. Frequency-domain EEG compression 132 6.3.3. Time-frequency EEG compression 132 6.3.4. Spatio-temporal compression of the EEG 132 6.3.5. Compression of the EEG by parameter extraction 132 6.4. ECG compression 133 6.4.1. State of the art 133 6.4.2. Evaluation of the performances of ECG compression methods 134 6.4.3. ECG pre-processing 135 6.4.4. ECG compression for real-time transmission 136 6.4.4.1. Time domain ECG compression 136 6.4.4.2. Compression of the ECG in the frequency domain 141 6.4.5. ECG compression for storage 144 6.4.5.1. Synchronization and polynomial modeling 145 6.4.5.2. Synchronization and interleaving 149 6.4.5.3. Compression of the ECG signal using the JPEG 2000 standard 150 6.5. Conclusion 150 6.6. Bibliography 151 Chapter 7. Compression of 2D Biomedical Images 155 Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Olivier DEFORGES and Marie BABEL 7.1. Introduction 155 7.2. Reversible compression of medical images 156 7.2.1. Lossless compression by standard methods 156 7.2.2. Specific methods of lossless compression 157 7.2.3. Compression based on the region of interest 158 7.2.4. Conclusion 160 7.3. Lossy compression of medical images 160 7.3.1. Quantization of medical images 160 7.3.1.1. Principles of vector quantization 161 7.3.1.2. A few illustrations 161 7.3.1.3. Balanced tree-structured vector quantization 163 7.3.1.4. Pruned tree-structured vector quantization 163 7.3.1.5. Other vector quantization methods applied to medical images 163 7.3.2. DCT-based compression of medical images 164 7.3.3. JPEG 2000 lossy compression of medical images 167 7.3.3.1. Optimizing the JPEG 2000 parameters for the compression of medical images 167 7.3.4. Fractal compression 170 7.3.5. Some specific compression methods 171 7.3.5.1. Compression of mammography images 171 7.3.5.2. Compression of ultrasound images 172 7.4. Progressive compression of medical images 173 7.4.1. State-of-the-art progressive medical image compression techniques 173 7.4.2. LAR progressive compression of medical images 174 7.4.2.1. Characteristics of the LAR encoding method 174 7.4.2.2. Progressive LAR encoding 176 7.4.2.3. Hierarchical region encoding 178 7.5. Conclusion 181 7.6. Bibliography 182 Chapter 8. Compression of Dynamic and Volumetric Medical Sequences 187 Azza OULED ZAID, Christian OLIVIER and Amine NAÏT-ALI 8.1. Introduction 187 8.2. Reversible compression of (2D+t) and 3D medical data sets 190 8.3. Irreversible compression of (2D+t) medical sequences 192 8.3.1. Intra-frame lossy coding 192 8.3.2. Inter-frame lossy coding 194 8.3.2.1. Conventional video coding techniques 194 8.3.2.2. Modified video coders 195 8.3.2.3. 2D+t wavelet-based coding systems limits 195 8.4. Irreversible compression of volumetric medical data sets 196 8.4.1. Wavelet-based intra coding 196 8.4.2. Extension of 2D transform-based coders to 3D data 197 8.4.2.1. 3D DCT coding 197 8.4.2.2. 3D wavelet-based coding based on scalar or vector quantization 198 8.4.2.3. Embedded 3D wavelet-based coding 199 8.4.2.4. Object-based 3D embedded coding 204 8.4.2.5. Performance assessment of 3D embedded coders 205 8.5. Conclusion 207 8.6. Bibliography 208 Chapter 9. Compression of Static and Dynamic 3D Surface Meshes 211 Khaled MAMOU, Françoise PRÊTEUX, Rémy PROST and Sébastien VALETTE 9.1. Introduction 211 9.2. Definitions and properties of triangular meshes 213 9.3. Compression of static meshes 216 9.3.1. Single resolution mesh compression 217 9.3.1.1. Connectivity coding 217 9.3.1.2. Geometry coding 218 9.3.2. Multi-resolution compression 219 9.3.2.1. Mesh simplification methods 219 9.3.2.2. Spectral methods 219 9.3.2.3. Wavelet-based approaches 220 9.4. Compression of dynamic meshes 229 9.4.1. State of the art 230 9.4.1.1. Prediction-based techniques 230 9.4.1.2. Wavelet-based techniques 231 9.4.1.3. Clustering-based techniques 233 9.4.1.4. PCA-based techniques 234 9.4.1.5. Discussion 234 9.4.2. Application to dynamic 3D pulmonary data in computed tomography 236 9.4.2.1. Data 236 9.4.2.2. Proposed approach 237 9.4.2.3. Results 238 9.5. Conclusion 239 9.6. Appendices 240 9.6.1. Appendix A: mesh via the MC algorithm 240 9.7. Bibliography 241 Chapter 10. Hybrid Coding: Encryption-Watermarking-Compression for Medical Information Security 247 William PUECH and Gouenou COATRIEUX 10.1. Introduction 247 10.2. Protection of medical imagery and data 248 10.2.1. Legislation and patient rights 248 10.2.2. A wide range of protection measures 249 10.3. Basics of encryption algorithms 251 10.3.1. Encryption algorithm classification 251 10.3.2. The DES encryption algorithm 252 10.3.3. The AES encryption algorithm 253 10.3.4. Asymmetric block system: RSA 254 10.3.5. Algorithms for stream ciphering 255 10.4. Medical image encryption 257 10.4.1. Image block encryption 258 10.4.2. Coding images by asynchronous stream cipher 258 10.4.3. Applying encryption to medical images 259 10.4.4. Selective encryption of medical images 261 10.5. Medical image watermarking and encryption 265 10.5.1. Image watermarking and health uses 265 10.5.2. Watermarking techniques and medical imagery 266 10.5.2.1. Characteristics. 266 10.5.2.2. The methods 267 10.5.3. Confidentiality and integrity of medical images by data encryption and data hiding 269 10.6. Conclusion. 272 10.7. Bibliography 273 Chapter 11. Transmission of Compressed Medical Data on Fixed and Mobile Networks 277 Christian OLIVIER, Benoît PARREIN and Rodolphe VAUZELLE 11.1. Introduction 277 11.2. Brief overview of the existing applications 278 11.3. The fixed and mobile networks 279 11.3.1. The network principles 279 11.3.1.1. Presentation, definitions and characteristics 279 11.3.1.2. The different structures and protocols 281 11.3.1.3. Improving the Quality of Service 281 11.3.2. Wireless communication systems 282 11.3.2.1. Presentation of these systems 282 11.3.2.2. Wireless specificities 284 11.4. Transmission of medical images 287 11.4.1. Contexts 287 11.4.1.1. Transmission inside a hospital 287 11.4.1.2. Transmission outside hospital on fixed networks 287 11.4.1.3. Transmission outside hospital on mobile networks 288 11.4.2. Encountered problems 288 11.4.2.1. Inside fixed networks 288 11.4.2.2. Inside mobile networks 289 11.4.3. Presentation of some solutions and directions 293 11.4.3.1. Use of error correcting codes 294 11.4.3.2. Unequal protection using the Mojette transform 297 11.5. Conclusion 299 11.6. Bibliography 300 Conclusion 303 List of Authors 305 Index 309

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Book SynopsisMany scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems. The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three chapters bring the theoretical notions that make it possible to cast inverse problems within a mathematical framework. The next three chapters address the fundamental inverse problem of deconvolution in a comprehensive manner. Chapters 7 and 8 deal with advanced statistical questions linked to image estimation. In the last five chapters, the main tools introduced in the previous chapters are put into a practical context in important applicative areas, such as astronomy or medical imaging.Table of ContentsIntroduction 15 Jérôme IDIER PART I. FUNDAMENTAL PROBLEMS AND TOOLS 23 Chapter 1. Inverse Problems, Ill-posed Problems 25 Guy DEMOMENT, Jérôme IDIER 1.1. Introduction 25 1.2. Basic example 26 1.3. Ill-posed problem 30 1.3.1. Case of discrete data 31 1.3.2. Continuous case 32 1.4. Generalized inversion 34 1.4.1. Pseudo-solutions 35 1.4.2. Generalized solutions 35 1.4.3. Example 35 1.5. Discretization and conditioning 36 1.6. Conclusion 38 1.7. Bibliography 39 Chapter 2. Main Approaches to the Regularization of Ill-posed Problems 41 Guy DEMOMENT, Jérôme IDIER 2.1. Regularization 41 2.1.1. Dimensionality control 42 2.1.2. Minimization of a composite criterion 44 2.2. Criterion descent methods 48 2.2.1.Criterion minimization for inversion 48 2.2.2. The quadratic case 49 2.2.3. The convex case 51 2.2.4. General case 52 2.3. Choice of regularization coefficient 53 2.3.1. Residual error energy control 53 2.3.2. “L-curve” method 53 2.3.3. Cross-validation 54 2.4. Bibliography 56 Chapter 3. Inversion within the Probabilistic Framework 59 Guy DEMOMENT, Yves GOUSSARD 3.1. Inversion and inference 59 3.2. Statistical inference 60 3.2.1. Noise law and direct distribution for data 61 3.2.2. Maximum likelihood estimation 63 3.3. Bayesian approach to inversion 64 3.4. Links with deterministic methods 66 3.5. Choice of hyperparameters 67 3.6. A priori model68 3.7. Choice of criteria 70 3.8. The linear, Gaussian case 71 3.8.1. Statistical properties of the solution 71 3.8.2. Calculation of marginal likelihood 73 3.8.3. Wiener filtering 74 3.9. Bibliography 76 PART II. DECONVOLUTION 79 Chapter 4. Inverse Filtering and Other Linear Methods 81 Guy LE BESNERAIS, Jean-François GIOVANNELLI, Guy DEMOMENT 4.1. Introduction 81 4.2. Continuous-time deconvolution 82 4.2.1. Inverse filtering 82 4.2.2. Wiener filtering 84 4.3. Discretization of the problem 85 4.3.1. Choice of a quadrature method 85 4.3.2. Structure of observation matrix H 87 4.3.3. Usual boundary conditions 89 4.3.4. Problem conditioning 89 4.3.5.Generalized inversion 91 4.4. Batch deconvolution 92 4.4.1. Preliminary choices 92 4.4.2. Matrix form of the estimate 93 4.4.3. Hunt’s method (periodic boundary hypothesis) 94 4.4.4. Exact inversion methods in the stationary case 96 4.4.5. Case of non-stationary signals 98 4.4.6. Results and discussion on examples 98 4.5. Recursive deconvolution 102 4.5.1. Kalman filtering 102 4.5.2. Degenerate state model and recursive least squares 104 4.5.3. Autoregressive state model 105 4.5.4. Fast Kalman filtering 108 4.5.5. Asymptotic techniques in the stationary case 110 4.5.6. ARMA model and non-standard Kalman filtering 111 4.5.7. Case of non-stationary signals 111 4.5.8. On-lineprocessing: 2Dcase 112 4.6. Conclusion 112 4.7. Bibliography 113 Chapter 5. Deconvolution of Spike Trains 117 Frédéric CHAMPAGNAT, Yves GOUSSARD, Stéphane GAUTIER, Jérôme IDIER 5.1. Introduction 117 5.2. Penalization of reflectivities, L2LP/L2Hy deconvolutions 119 5.2.1. Quadratic regularization 121 5.2.2. Non-quadratic regularization 122 5.2.3. L2LPorL2Hy deconvolution 123 5.3. Bernoulli-Gaussian deconvolution 124 5.3.1. Compound BG model 124 5.3.2. Various strategies for estimation 124 5.3.3. General expression for marginal likelihood 125 5.3.4. An iterative method for BG deconvolution 126 5.3.5. Other methods 128 5.4. Examples of processing and discussion 130 5.4.1. Nature of the solutions 130 5.4.2. Setting the parameters 132 5.4.3. Numerical complexity 133 5.5. Extensions 133 5.5.1. Generalization of structures of R and H 134 5.5.2. Estimation of the impulse response . . . 134 5.6. Conclusion 136 5.7. Bibliography 137 Chapter 6. Deconvolution of Images 141 Jérôme IDIER, Laure BLANC-FÉRAUD 6.1. Introduction 141 6.2. Regularization in the Tikhonov sense 142 6.2.1. Principle 142 6.2.2. Connection with image processing by linear PDE 144 6.2.3. Limits of Tikhonov’s approach 145 6.3. Detection-estimation 148 6.3.1. Principle 148 6.3.2. Disadvantages 149 6.4. Non-quadratic approach 150 6.4.1. Detection-estimation and non-convex penalization 154 6.4.2. Anisotropic diffusion by PDE 155 6.5. Half-quadratic augmented criteria 156 6.5.1. Duality between non-quadratic criteria and HQ criteria 157 6.5.2. Minimization of HQ criteria 158 6.6. Application in image deconvolution 159 6.6.1. Calculation of the solution 159 6.6.2. Example 161 6.7. Conclusion 164 6.8. Bibliography 165 PART III. ADVANCED PROBLEMS AND TOOLS 169 Chapter 7. Gibbs-Markov Image Models 171 Jérôme IDIER 7.1. Introduction 171 7.2. Bayesian statistical framework 172 7.3. Gibbs-Markov fields 173 7.3.1. Gibbs fields 174 7.3.2. Gibbs-Markov equivalence 177 7.3.3. Posterior law of a GMRF 180 7.3.4. Gibbs-Markov models for images 181 7.4. Statistical tools, stochastic sampling 185 7.4.1. Statistical tools 185 7.4.2. Stochastic sampling 188 7.5. Conclusion 194 7.6. Bibliography 195 Chapter 8. Unsupervised Problems 197 Xavier DESCOMBES, Yves GOUSSARD 8.1. Introduction and statement of problem 197 8.2. Directly observed field 199 8.2.1. Likelihood properties 199 8.2.2. Optimization 200 8.2.3. Approximations 202 8.3. Indirectly observed field 205 8.3.1. Statement of problem 205 8.3.2. EM algorithm 206 8.3.3. Application to estimation of the parameters of a GMRF 207 8.3.4. EM algorithm and gradient 208 8.3.5. Linear GMRF relative to hyperparameters 210 8.3.6. Extensions and approximations 212 8.4. Conclusion 215 8.5. Bibliography 216 PART IV. SOME APPLICATIONS 219 Chapter 9. Deconvolution Applied to Ultrasonic Non-destructive Evaluation 221 Stéphane GAUTIER, Frédéric CHAMPAGNAT, Jérôme IDIER 9.1. Introduction 221 9.2. Example of evaluation and difficulties of interpretation 222 9.2.1. Description of the part to be inspected 222 9.2.2. Evaluation principle 222 9.2.3. Evaluation results and interpretation 223 9.2.4. Help with interpretation by restoration of discontinuities 224 9.3. Definition of direct convolution model 225 9.4. Blind deconvolution 226 9.4.1. Overview of approaches for blind deconvolution 226 9.4.2. DL2Hy/DBGd econvolution 230 9.4.3. Blind DL2Hy/DBG deconvolution 232 9.5. Processing real data 232 9.5.1. Processing by blind deconvolution 233 9.5.2. Deconvolution with a measured wave 234 9.5.3. Comparison between DL2Hy and DBG 237 9.5.4. Summary 240 9.6. Conclusion 240 9.7. Bibliography 241 Chapter 10. Inversion in Optical Imaging through Atmospheric Turbulence 243 Laurent MUGNIER, Guy LE BESNERAIS, Serge MEIMON 10.1. Optical imaging through turbulence 243 10.1.1. Introduction 243 10.1.2. Image formation 244 10.1.4. Imaging techniques 249 10.2. Inversion approach and regularization criteria used 253 10.3. Measurement of aberrations 254 10.3.1. Introduction 254 10.3.2. Hartmann-Shack sensor 255 10.3.3. Phase retrieval and phase diversity 257 10.4. Myopic restoration in imaging 258 10.4.1. Motivation and noise statistic 258 10.4.2. Data processing in deconvolution from wavefront sensing 259 10.4.3. Restoration of images corrected by adaptive optics 263 10.4.4. Conclusion 267 10.5. Image reconstruction in optical interferometry (OI) 268 10.5.1. Observation model 268 10.5.2. Traditional Bayesian approach 271 10.5.3. Myopic modeling 272 10.5.4. Results 274 10.6. Bibliography 277 Chapter 11. Spectral Characterization in Ultrasonic Doppler Velocimetry 285 Jean-François GIOVANNELLI, Alain HERMENT 11.1. Velocity measurement in medical imaging 285 11.1.1. Principle of velocity measurement in ultrasound imaging 286 11.1.2. Information carried by Doppler signals 286 11.1.3.Some characteristics and limitations 288 11.1.4. Data and problems treated 288 11.2. Adaptive spectral analysis 290 11.2.1. Least squares and traditional extensions 290 11.2.2. Long AR models – spectral smoothness – spatial continuity 291 11.2.3. Kalman smoothing 293 11.2.4. Estimation of hyperparameters 294 11.2.5. Processing results and comparisons 296 11.3. Tracking spectral moments 297 11.3.1. Proposed method 298 11.3.2. Likelihood of the hyperparameters 302 11.3.3. Processing results and comparisons 304 11.4. Conclusion 306 11.5. Bibliography 307 Chapter 12. Tomographic Reconstruction from Few Projections 311 Ali MOHAMMAD-DJAFARI, Jean-Marc DINTEN 12.1. Introduction 311 12.2. Projection generation model 312 12.3. 2D analytical methods 313 12.4. 3D analytical methods 317 12.5. Limitations of analytical methods 317 12.6. Discrete approach to reconstruction 319 12.7. Choice of criterion and reconstruction methods 321 12.8. Reconstruction algorithms 323 12.8.1. Optimization algorithms for convex criteria 323 12.8.2. Optimization or integration algorithms 327 12.9. Specific models for binary objects 328 12.10. Illustrations 328 12.10.1.2D reconstruction 328 12.10.2.3Dreconstruction 329 12.11. Conclusions 331 12.12. Bibliography 332 Chapter 13. Diffraction Tomography 335 Hervé CARFANTAN, Ali MOHAMMAD-DJAFARI 13.1. Introduction 335 13.2. Modeling the problem 336 13.2.1. Examples of diffraction tomography applications 336 13.2.2. Modeling the direct problem 338 13.3. Discretization of the direct problem 340 13.3.1. Choice of algebraic framework 340 13.3.2. Method of moments 341 13.3.3. Discretization by the method of moments 342 13.4. Construction of criteria for solving the inverse problem 343 13.4.1. First formulation: estimation of x 344 13.4.2. Second formulation: simultaneous estimation of x and φ 345 13.4.3. Properties of the criteria 347 13.5. Solving the inverse problem 347 13.5.1. Successive linearizations 348 13.5.2. Joint minimization 350 13.5.3. Minimizing MAP criterion 351 13.6. Conclusion 353 13.7. Bibliography 354 Chapter 14. Imaging from Low-intensity Data 357 Ken SAUER, Jean-Baptiste THIBAULT 14.1. Introduction 357 14.2. Statistical properties of common low-intensity image data 359 14.2.1. Likelihood functions and limiting behavior 359 14.2.2. Purely Poisson measurements 360 14.2.3. Inclusion of background counting noise 362 14.2.4. Compound noise models with Poisson information 362 14.3. Quantum-limited measurements in inverse problems 363 14.3.1. Maximum likelihood properties 363 14.3.2. Bayesian estimation 366 14.4. Implementation and calculation of Bayesian estimates 368 14.4.1. Implementation for pure Poisson model 368 14.4.2. Bayesian implementation for a compound data model 370 14.5. Conclusion 372 14.6. Bibliography 372 List of Authors 375 Index 377

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Book SynopsisThe concept of an inverse problem is a familiar one to most scientists and engineers, particularly in the field of signal and image processing, imaging systems (medical, geophysical, industrial non-destructive testing, etc.), and computer vision. In imaging systems, the aim is not just to estimate unobserved images but also their geometric characteristics from observed quantities that are linked to these unobserved quantities by a known physical or mathematical relationship. In this manner techniques such as image enhancement or addition of hidden detail can be delivered. This book focuses on imaging and vision problems that can be clearly described in terms of an inverse problem where an estimate for the image and its geometrical attributes (contours and regions) is sought. The book uses a consistent methodology to examine inverse problems such as: noise removal; restoration by deconvolution; 2D or 3D reconstruction in X-ray, tomography or microwave imaging; reconstruction of the surface of a 3D object using X-ray tomography or making use of its shading; reconstruction of the surface of a 3D landscape based on several satellite photos; super-resolution; motion estimation in a sequence of images; separation of several images mixed using instruments with different sensitivities or transfer functions; and much more.Trade Review"Apart from the high price I can recommend this book if you are interested in imaging or artificial vision." (I Programmer, 3 February 2011)Table of ContentsPreface 13 Chapter 1. Introduction to Inverse Problems in Imaging and Vision 15 Ali MOHAMMAD-DJAFARI 1.1. Inverse problems 16 1.2. Specific vision problems 21 1.3. Models for time-dependent quantities 26 1.4. Inverse problems with multiple inputs and multiple outputs (MIMO) 27 1.5. Non-linear inverse problems 30 1.6. 3D reconstructions 33 1.7. Inverse problems with multimodal observations 33 1.8. Classification of inversion methods: analytical or algebraic 34 1.9. Standard deterministic methods 40 1.10. Probabilistic methods 44 1.11. Problems specific to vision 50 1.12. Introduction to the various chapters of the book 52 1.13. Bibliography 55 Chapter 2. Noise Removal and Contour Detection 59 Pierre CHARBONNIER and Christophe COLLET 2.1. Introduction 61 2.2. Statistical segmentation of noisy images 72 2.3. Multi-band multi-scale Markovian regularization 79 2.4. Bibliography 88 Chapter 3. Blind Image Deconvolution 97 Laure BLANC-FÉRAUD, Laurent MUGNIER and André JALOBEANU 3.1. Introduction 97 3.2. The blind deconvolution problem 98 3.3. Joint estimation of the PSF and the object 103 3.4. Marginalized estimation of the impulse response 107 3.5. Various other approaches 112 3.6. Multi-image methods and phase diversity 114 3.7. Conclusion 115 3.8. Bibliography 116 Chapter 4. Triplet Markov Chains and Image Segmentation 123 Wojciech PIECZYNSKI 4.1. Introduction 124 4.2. Pairwise Markov chains (PMCs) 127 4.3. Copulas in PMCs 130 4.4. Parameter estimation 132 4.5. Triplet Markov chains (TMCs) 136 4.6. TMCs and non-stationarity 139 4.7. Hidden Semi-Markov chains (HSMCs) and TMCs 140 4.8. Auxiliary multivariate chains 144 4.9. Conclusions and outlook 148 4.10. Bibliography 149 Chapter 5. Detection and Recognition of a Collection of Objects in a Scene 155 Xavier DESCOMBES, Ian JERMYN and Josiane ZERUBIA 5.1. Introduction 155 5.2. Stochastic approaches 156 5.3. Variational approaches 167 5.4. Bibliography 184 Chapter 6. Apparent Motion Estimation and Visual Tracking 191 Etienne MÉMIN and Patrick PÉREZ 6.1. Introduction: from motion estimation to visual tracking 191 6.2. Instantaneous estimation of apparent motion 193 6.3. Visual tracking 219 6.4. Conclusions 240 6.5. Bibliography 241 Chapter 7. Super-resolution 251 Ali MOHAMMAD-DJAFARI and Fabrice HUMBLOT 7.1. Introduction 251 7.2. Modeling the direct problem 252 7.3. Classical SR methods 257 7.4. SR inversion methods 261 7.5. Methods based on a Bayesian approach 265 7.6. Simulation results 271 7.7. Conclusion 272 7.8. Bibliography 274 Chapter 8. Surface Reconstruction from Tomography Data 277 Charles SOUSSEN and Ali MOHAMMAD-DJAFARI 8.1. Introduction 277 8.2. Reconstruction of localized objects 280 8.3. Use of deformable contours for 3D reconstruction 284 8.4. Appropriate surface models and algorithmic considerations 293 8.5. Reconstruction of a polyhedric active contour 298 8.6. Conclusion 303 8.7. Bibliography 305 Chapter 9. Gauss-Markov-Potts Prior for Bayesian Inversion in Microwave Imaging 309 Olivier FÉRON, Bernard DUCHÊNE and Ali MOHAMMAD-DJAFARI 9.1. Introduction 310 9.2. Experimental configuration and modeling of the direct problem 311 9.3. Inversion in the linear case 315 9.4. Inversion in the non-linear case 325 9.5. Conclusion 335 9.6. Bibliography 336 Chapter 10. Shape from Shading 339 Jean-Denis DUROU 10.1. Introduction 339 10.2. Modeling of shape from shading 340 10.3. Resolution of shape from shading 353 10.4. Conclusion 371 10.5. Bibliography 372 Chapter 11. Image Separation 377 Hichem SNOUSSI and Ali MOHAMMAD-DJAFARI 11.1. General introduction 377 11.2. Blind image separation 378 11.3. Bayesian formulation 384 11.4. Stochastic algorithms 390 11.5. Simulation results 398 11.6. Conclusion 401 11.7. Appendix 1: a posteriori distributions 407 11.8. Bibliography 409 Chapter 12. Stereo Reconstruction in Satellite and Aerial Imaging 411 Julie DELON and Andrés ALMANSA 12.1. Introduction 411 12.2. Principles of satellite stereovision 412 12.3. Matching 415 12.4. Regularization 421 12.5. Numerical considerations 425 12.6. Conclusion 432 12.7. Bibliography 434 Chapter 13. Fusion and Multi-modality 437 Christophe COLLET, Farid FLITTI, Stéphanie BRICQ and André JALOBEANU 13.1. Fusion of optical multi-detector images without loss of information 437 13.2. Fusion of multi-spectral images using hidden Markov trees 438 13.3. Segmentation of multimodal cerebral MRI using an a priori probabilistic map 448 13.4. Bibliography 458 List of Authors 461 Index 463

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Book SynopsisThe authors describe a technique that can visualize the atomic structure of molecules, it is necessary, in terms of the image processing, to consider the reconstruction of sparse images. Many works have leveraged the assumption of sparsity in order to achieve an improved performance that would not otherwise be possible. For nano MRI, the assumption of sparsity is given by default since, at the atomic scale, molecules aresparse structures. This work reviews the latest results on molecular imaging for nano MRI. Sparse image reconstruction methods can be categorized as either non-Bayesian or Bayesian. A comparison of the performance and complexity of several such algorithms is given.Table of ContentsIntroduction ix Chapter 1. Nano MRI 1 Chapter 2. Sparse Image Reconstruction 7 Chapter 3. Iterative Thresholding Methods 15 Chapter 4. Hyperparameter Selection Using the SURE Criterion 43 Chapter 5. Monte Carlo Approach: Gibbs Sampling 53 Chapter 6. Simulation Study 65 Bibliography 73 Index 77

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Book SynopsisThis full-color book will teach you how to use Adobe Illustrator's various tools to create infographics, as well as basic page layouts for them. This is the first of three volumes which will cover all the fundamentals of Illustrator, an industry standard application used by graphic designers and marketing and communication teams. How is an infographic different from a logo or any other illustration? What additional thought processes, skills, or software tools should be utilized to create an infographic? In this first volume of Illustrator Basics, you will learn the answers to all these questions. Author Jennifer Harder will walk you through creating basic infographics in Illustrator using Basic Shape tools, Pen Tools, Type Tools, and Symbols. Upon completing this volume, you will have an appreciation for how easy it is to design an infographic and discover how rudimentary shapes and colors can affect readability while conveying meaning to your audience. You will be able to use this knowledge to create your own infographics using Illustrator’s wide array of tools. Who This Book Is For Discover the tools within Illustrator that are ideal for creating basic infographics Develop a logo based upon a scanned sketch Gain an understanding of different infographic layouts and the process of reviewing them with your client Who This Book Is For Beginner-level designers and others who are interested in learning the process of creating infographics for their company, the classroom, for a visual resume, an article in a magazine, or a webpage.Table of ContentsChapter 1: What are Infographics? Chapter 2: Preparation for Creating a Logo and Infographics Chapter 3: Scanner Basics Chapter 4: Setting Up Your Workspace Chapter 5: Working with Artboards and Saving Files Chapter 6: A Basic Review of Illustrator’s Shape Tools Chapter 7: A Basic Review of Illustrator’s Pen Tools Chapter 8: Working with Illustrator’s Layers and additional Drawing and Type Tools Chapter 9: Creating your first Infographic Projects

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Book Synopsis1: Ready Yourself to Learn From AI.- 2: Reprogram Your Learning Patterns.- 3: Regulate How You Learn.- 4: Re-Learn While Working.-5: Design Your Own Learning.- 6: Re-energize Doing.- 7: Re-Assess with Generative AI.- 8: Re-Adjust with AI.- 9: Prototype Learning.- 10: Re-Iterate How You Learn.- 11:Reconciling Using Generative AI.- 12: Remember the Algorithms.- 13: Continuously Improve and Learn with AI.- 14: Build Your Own Teaching Bots.- 15: Re-Invent Reinforcement.- 16: Learn with Other Bots.- 17: Transform Your Organization.- 18: Reclaim Your Creative Content.- 19: Fill in the Blanks.- 20: An Intelligent Conclusion.

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Book SynopsisPart I: Foundations for Effective Visuals.- Chapter 1: Getting Started.- Part II: No-Cost Software Titles.- Chapter 2: Paint.NET: The Free Image Editor for Windows.- Chapter 3: GIMP: A Powerful Free Alternative to Photoshop.- Chapter 4: FotoSketcher: Turn Photos Into Art.- Chapter 5: Inkscape: The Free Program for Creating Scalable Vector Graphics.- Part III: Using Predesigned Templates, Stock Images and AI, and Resources for Large Format Graphics.- Chapter 6: Affordable Web-Based Solutions.- Chapter 7: No-Cost Stock Image Resources .- Chapter 8: Utilizing Generative AI Resources Chapter.- Chapter 9: Large Format and Vehicle Graphics.- Part IV: Employee Involvement.- Chapter 10: Cultivating a Visual Branding Culture. Part V: Useful Learning Resources.- Chapter 11: Appendix: Useful Learning Resources.

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