Image processing Books

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Book SynopsisBrimming with top articles from experts in signal processing and biomedical engineering, Time Frequency and Wavelets in Biomedical Signal Processing introduces time--frequency, time--scale, wavelet transform methods, and their applications in biomedical signal processing.Table of ContentsList of Contributors. Preface. TIME-FREQUENCY ANALYSIS METHODS WITH BIOMEDICAL APPLICATIONS. Recent Advances in Time-Frequency Representations: SomeTheoretical Foundation (W. Williams). Biological Applications and Interpretations of Time-Frequency Signal Analysis (W. Williams). The Application of Advanced Time-Frequency Analysis Techniques to Doppler Ultrasound (S. Marple, et al.). Analysis of ECG Late Potentials Using Time-Frequency Methods (H. Dickhaus & H. Heinrich). Time-Frequency Distributions Applied to Uterine EMG: Characterization and Assessment (J. Duchene & D. Devedeux). Time-Frequency Analyses of the Electrogastrogram (Z. Lin and J. Chen). Recent Advances in Time-Frequency and Time-Scale Methods (C. Mello & M. Akay). WAVELETS, WAVELET PACKETS, AND MATCHING PURSUITS WITH BIOMEDICAL APPLICATIONS. Fast Algorithms for Wavelet Transform Computation (O. Rioul & P. Duhamel). Analysis of Cellular Vibrations in the Living Cochlea Using the Continuous Wavelet Transform and the Short-Time Fourier Transform (M. Teich, et al.). Alterative Processing Method Using Gabor Wavelets and the Wavelet Transform for the Analysis of Phonocardiogram Signals (M. Matalgah, et al.). Wavelet Feature Extraction from Neurophysiological Signals (M. Sun & R. Sclabassi). Experiments with Adapted Wavelet De-Noising for Medical Signals and Images (R. Coifman & M. Wickerhauser). Speech Enhancement for Hearing Aids (J. Rutledge). From Continuous Wavelet Transform to Wavelet Packets: Application to the Estimation of Pulmonary Microvascular Pressure (M. Karrakchou & M. Kunt). In Pursuit of Time-Frequency Representation of Brain Signals (P. Durka & K. Blinowska). EEG Spike Directors Based on Different Decompositions: A Comparative Study (L. Senhadji, et al.). WAVELETS AND MEDICAL IMAGING. A Discrete Dyadic Wavelet Transform for Multidimensional Feature Analysis (I. Koren & A. Laine). Hexagonal QMF Banks and Wavelets (S. Schuler & A. Laine). Inversion of the Radon Transform under Wavelet Constraints (B. Sahiner & A. Yagle). Wavelets Applied to Mammograms (W. Richardson). Hybrid Wavelet Transform for Image Enhancement forComputer-Assisted Diagnosis and Telemedicine Applications (L. Clarke, et al.). Medical Image Enhancement Using Wavelet Transform and Arithmetic Coding (P. Saipetch, et al.). Adapted Wavelet Encoding in Functional Magnetic Resonance Imaging (D. Healy, et al.). A Tutorial Overview of a Stabilization Algorithm for Limited-Angle Tomography (T. Olson). Wavelet Compression of Medical Images (A. Manduca). WAVELETS, NEURAL NETWORKS, AND FRACTALS. Single Side Scaling Wavelet Frame and Neural Network (Q. Zhang). Analysis of Evoked Potentials Using Wavelet Networks (H. Heinrich & H. Dickhaus). Self-Organizing Wavelet-Based Neural Networks (K. Kobayashi). On Wavelets and Fractal Processes (P. Flandrin). Fractal Analysis of Heart Rate Variability (R. Fischer & M. Akay). Index. Editor's Biography.

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Book SynopsisPrinciples of Magnetic Resonance Imaging Biomedical/Electrical Engineering Principles of Magnetic Resonance Imaging A Signal Processing Perspective A volume in the IEEE Press Series in Biomedical EngineeringMetin Akay. Series Editor Since its inception in 1971. MRI has developed into a premier tool for anatomical and runaional imaging. Prin??ples ofMagne??c Resonance Imaging provides a clear and comprehensive treatment of MR image formation principles from a signal processing perspective. You will find discussion of these essential topics: Mathematical fundamentals Signal generation and detection principles Signal characteristics Signal localization principles Image reconstruction techniques Image contrast mechanisms Image resolution. noise, and artifacts Fast-scan imaging Constrained reconstruction Spatial information encoding Table of ContentsPreface. Acknowledgments. Introduction. Mathematical Fundamentals. Signal Generation and Detection. Signal Characteristics. Signal Localization. Image Reconstruction. Image Contrast. Image Resolution, Noise, and Artifacts. Fast-Scan Imaging. Constrained Reconstruction. Appendix A: Mathematical Formulas. Appendix B: Glossary. Appendix C: Abbreviations. Appendix D: Mathematical Symbols. Appendix E: Physical Constants. Bibliography. Index. About the Authors.

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Book Synopsis

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Book Synopsis

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Book SynopsisAn exploration of random processes for image and signal processing. It seeks to reflect the author's increasing appreciation of the profound differences between deterministic and probabilistic scientific epistemology. Topics include canonical representation and transform coding.

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Book SynopsisThis bible of a whole generation of communications engineers was originally published in 1958. The focus is on the statistical theory underlying the study of signals and noises in communications systems, emphasizing techniques as well s results. End of chapter problems are provided. Sponsored by: IEEE Communications SocietyTable of ContentsPreface to the IEEE Press Edition. Preface. Errata. Introduction. Probability. Random Variables and Probability Distributions. Averages. Sampling. Spectral Analysis. Shot Noise. The Gaussian Process. Linear Systems. Noise Figures. Optimum Linear Systems. Nonlinear Devices: The Direct Method. Nonlinear Devices: The Transform Method. Statistical Detection Signals. Appendix 1: The Impulse Function. Appendix 2: Integral Equations. Bibliography. Index.

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Book SynopsisMachine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systTable of ContentsSection-1 Machine & Deep Learning techniques for Image Processing 1.1 Image Features in Machine Learning 1.2 Image Segmentation and Classification using Deep Learning 1.3 Deep Learning based Synthetic Aperture Radar Image Classification 1.4 Design Perspectives of Multitask Deep Learning Models and Applications 1.5 Image Reconstruction using Deep Learning 1.6 Machine and Deep Learning Techniques for Image Super-Resolution Section-2 Machine & Deep Learning techniques for Text and Speech Processing 2.1 Machine and Deep Learning Techniques for Text and Speech Processing 2.2 Manipuri Handwritten Script Recognition using Machine and Deep Learning 2.3 Comparison of Different Text Extraction Techniques for Complex Color Images 2.4 Smart Text Reader System for Blind Person using Machine and Deep Learning 2.5 Machine Learning Techniques for Deaf People 2.6 Design and Development of Chatbot based on Reinforcement Learning 2.7 DNN based Speech Quality Enhancement and Multi-speaker Separation for Automatic Speech Recognition System 2.8 Design and Development of Real-Time Music Transcription using Digital Signal Processing Section-3 Applications of Signal and Image Processing with Machine & Deep learning techniques 3.1 Role of Machine Learning in Wrist Pulse Analysis 3.2 An Explainable Convolutional Neural Network based Method for Skin Lesion Classification from Dermoscopic Images 3.3 Future of Machine-Learning and Deep-Learning in Health-Care Monitoring System 3.4 Usage of AI & Wearable IoT Devices for Healthcare Data: A Study 3.5 Impact of IoT in Biomedical Applications using Machine and Deep Learning 3.6 Wireless Communications using Machine Learning and Deep Learning 3.7 Applications of Machine Learning and Deep Learning in Smart Agriculture 3.8 Structural Damage Prediction from Earthquakes using Deep Learning 3.9 Machine Learning and Deep Learning Techniques in Social Sciences 3.1O Green Energy using Machine and Deep Learning 3.11 Light Deep CNN Approach for Multi-Label Pathology Classification using Frontal Chest X-Ray Index

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Book Synopsis Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You''ll see the OpenCV algorithms and how to use them for image processing.  The next section looks at advanced machine learning and deep learning methods for image processing and classification. You''ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you''ll explore how models are made in real time and then deployed using various DevOps tools.  All the concepTable of ContentsChapter 1: Installation and Environment Setup Chapter Goal: Making System Ready for Image Processing and Analysis No of pages 20 Sub -Topics (Top 2) 1. Installing Jupyter Notebook 2. Installing OpenCV and other Image Analysis dependencies 3. Installing Neural Network Dependencies Chapter 2: Introduction to Python and Image Processing Chapter Goal: Introduction to different concepts of Python and Image processing Application on it. No of pages: 50 Sub - Topics (Top 2) 1. Essentials of Python 2. Terminologies related to Image Analysis Chapter 3: Advanced Image Processing using OpenCV Chapter Goal: Understanding Algorithms and their applications using Python No of pages: 100 Sub - Topics (Top 2): 1. Operations on Images 2. Image Transformations Chapter 4: Machine Learning Approaches in Image Processing Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario No of pages: 100 Sub - Topics (Top 2): 1. Image Classification and Segmentation 2. Applying Supervised and Unsupervised Learning approaches on Images using Python Chapter 5: Real Time Use Cases Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book No of pages: 100 Sub - Topics (Top 5): 1. Facial Detection 2. Facial Recognition 3. Hand Gesture Movement Recognition 4. Self-Driving Cars Conceptualization: Advanced Lane Finding 5. Self-Driving Cars Conceptualization: Traffic Signs Detection Chapter 6: Appendix A Chapter Goal: Advanced concepts Introduction No of pages: 50 Sub - Topics (Top 2): 1. AdaBoost and XGBoost 2. Pulse Coupled Neural Networks

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Book SynopsisBefore you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data.

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Book SynopsisChange Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.Table of ContentsContents Preface xi Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1 Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU and Xiaohua TONG 1.1. Introduction 1 1.2. Unsupervised change detection in multispectral images 3 1.2.1.Relatedconcepts 3 1.2.2.Openissuesandchallenges 7 1.2.3. Spectral–spatial unsupervised CD techniques 7 1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9 1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9 1.3.2. Multiscale morphological compressed change vector analysis 11 1.3.3. Superpixel-level compressed change vector analysis 15 1.4.Datasetdescriptionandexperimentalsetup 18 1.4.1.Datasetdescription 18 1.4.2.Experimentalsetup 22 1.5.Resultsanddiscussion 24 1.5.1.ResultsontheXuzhoudataset 24 1.5.2. Results on the Indonesia tsunami dataset 24 xv 1.6.Conclusion 28 1.7.Acknowledgements 29 1.8.References 29 Chapter 2 Change Detection in Time Series of Polarimetric SAR Images 35 Knut CONRADSEN, Henning SKRIVER, MortonJ.CANTY andAllanA.NIELSEN 2.1. Introduction 35 2.1.1.Theproblem 36 2.1.2 Important concepts illustrated by means of the gamma distribution 39 2.2.Testtheoryandmatrixordering 45 2.2.1. Test for equality of two complex Wishart distributions 45 2.2.2. Test for equality of k-complex Wishart distributions 47 2.2.3. The block diagonal case 49 2.2.4.TheLoewnerorder 52 2.3.Thebasicchangedetectionalgorithm 53 2.4.Applications 55 2.4.1.Visualizingchanges 58 2.4.2.Fieldwisechangedetection 59 2.4.3. Directional changes using the Loewner ordering 62 2.4.4. Software availability 65 2.5.References 70 Chapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73 Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ, Arnaud BRELOY and Frédéric PASCAL 3.1. Introduction 73 3.2.Datasetdescription 76 3.3.StatisticalmodelingofSARimages 77 3.3.1.Thedata 77 3.3.2.Gaussianmodel 77 3.3.3.Non-Gaussianmodeling 83 3.4.Dissimilaritymeasures 84 3.4.1.Problemformulation 84 3.4.2. Hypothesis testing statistics 85 3.4.3.Information-theoreticmeasures 87 3.4.4.Riemanniangeometrydistances 89 3.4.5.Optimaltransport 90 3.4.6.Summary 91 3.4.7. Results of change detectors on the UAVSAR dataset 91 3.5. Change detection based on structured covariances 94 3.5.1. Low-rank Gaussian change detector 96 3.5.2. Low-rank compound Gaussian change detector 97 3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100 3.6.Conclusion 102 3.7.References 103 Chapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109 Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN and Lionel BOMBRUN 4.1. Introduction 109 4.2.Parametricmodelingofconvnetfeatures 110 4.3.Anomalydetectioninimagetimeseries 113 4.4.Functionalimagetimeseriesclustering 119 4.5.Conclusion 123 4.6.References 123 Chapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127 Fatima KARBOU, Guillaume JAMES, Philippe DURAND and Abdourrahmane M. ATTO 5.1. Introduction 127 5.2.Testareaanddata 129 5.3.WetsnowdetectionusingSentinel-1 129 5.4.Metricstodetectwetsnow 133 5.5.Discussion 138 5.6.Conclusion 143 5.7.Acknowledgements 143 5.8.References 143 Chapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145 Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC and Pedro MORETTIN 6.1. Introduction 145 6.2. Random field model of a cyclone texture 148 6.2.1.Cyclonetexturefeature 149 6.2.2. Wavelet-based power spectral densities and cyclone fields 150 6.2.3. Fractional spectral power decay model 153 6.3.Cyclonefieldeyedetectionandtracking 157 6.3.1.Cycloneeyedetection 157 6.3.2.Dynamicfractalfieldeyetracking 158 6.4. Cyclone field intensity evolution prediction 159 6.5.Discussion 161 6.6.Acknowledgements 163 6.7.References 163 Chapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167 Minh-Tan PHAM and Grégoire MERCIER 7.1. Introduction 167 7.2. Texture representation and characterization using local extrema 169 7.2.1.Motivationandapproach 169 7.2.2. Local extrema keypoints within SAR images 172 7.3.Unsupervisedchangedetection 175 7.3.1. Proposed framework 175 7.3.2. Weighted graph construction from keypoints 176 7.3.3.Changemeasure(CM)generation 178 7.4.Experimentalstudy 179 7.4.1. Data description and evaluation criteria 179 7.4.2.Changedetectionresults 181 7.4.3.Sensitivitytoparameters 185 7.4.4.ComparisonwiththeNLMmodel 188 7.4.5. Analysis of the algorithm complexity 191 7.5.Applicationtoglacierflowmeasurement 192 7.5.1. Proposed method 193 7.5.2.Results 194 7.6.Conclusion 196 7.7.References 197 Chapter 8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201 Andrea GARZELLI and Claudia ZOPPETTI 8.1. Introduction 201 8.2. Proposed method 203 8.2.1.Testsiteanddata 206 8.3.SARprocessing 209 8.4.Opticalprocessing 215 8.5.Combinationlayer 217 8.6.Results 219 8.7.Conclusion 220 8.8.References 221 Chapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223 Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE 9.1. Introduction 223 9.2. Overview of the change detection problem 225 9.2.1. Change detection methods for multispectral images 227 9.2.2. Challenges addressed in this chapter 230 9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231 9.3.1. Magnitude image statistical mixture model 231 9.3.2.Bayesiandecision 233 9.3.3. Numerical approach to parameter estimation 234 9.4. A compound multiclass statistical model of the difference image 239 9.4.1. Difference image statistical mixture model 240 9.4.2. Magnitude image statistical mixture model 245 9.4.3.Bayesiandecision 248 9.4.4. Numerical approach to parameter estimation 249 9.5.Experimentalresults 253 9.5.1.Datasetdescription 253 9.5.2.Experimentalsetup 256 9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256 9.5.4. Test 2: Multiclass Rician mixture model 260 9.6.Conclusion 266 9.7.References 267 List of Authors 275 Index 277 Summary of Volume 2 281

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Book SynopsisChange Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253

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Book SynopsisConventional topographic databases, obtained by capture on aerial or spatial images provide a simplified 3D modeling of our urban environment, answering the needs of numerous applications (development, risk prevention, mobility management, etc.). However, when we have to represent and analyze more complex sites (monuments, civil engineering works, archeological sites, etc.), these models no longer suffice and other acquisition and processing means have to be implemented. This book focuses on the study of adapted lifting means for “notable buildings”. The methods tackled in this book cover lasergrammetry and the current techniques of dense correlation based on images using conventional photogrammetry.Table of Contents1. Specific Requirements for the 3D Digitization of Outstanding Sites. 2. 3D Digitization Using Images. 3. 3D Digitization by Laser Scanner. 4. Complementarity of Techniques. 5. Point Cloud Processing. 6. Management and Use of Surveys.

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Book SynopsisMathematical Imaging is currently a rapidly growing field in applied mathematics, with an increasing need for theoretical mathematics. This book, the second of two volumes, emphasizes the role of mathematics as a rigorous basis for imaging sciences. It provides a comprehensive and convenient overview of the key mathematical concepts, notions, tools and frameworks involved in the various fields of gray-tone and binary image processing and analysis, by proposing a large, but coherent, set of symbols and notations, a complete list of subjects and a detailed bibliography. It establishes a bridge between the pure and applied mathematical disciplines, and the processing and analysis of gray-tone and binary images. It is accessible to readers who have neither extensive mathematical training, nor peer knowledge in Image Processing and Analysis. It is a self-contained book focusing on the mathematical notions, concepts, operations, structures, and frameworks that are beyond or involved in Image Processing and Analysis. The notations are simplified as far as possible in order to be more explicative and consistent throughout the book and the mathematical aspects are systematically discussed in the image processing and analysis context, through practical examples or concrete illustrations. Conversely, the discussed applicative issues allow the role of mathematics to be highlighted. Written for a broad audience – students, mathematicians, image processing and analysis specialists, as well as other scientists and practitioners – the author hopes that readers will find their own way of using the book, thus providing a mathematical companion that can help mathematicians become more familiar with image processing and analysis, and likewise, image processing and image analysis scientists, researchers and engineers gain a deeper understanding of mathematical notions and concepts.Table of ContentsPreface xvii Introduction xxv Part 5 Twelve Main Geometrical Frameworks for Binary Images 1 Chapter 21 The Set-Theoretic Framework 3 Chapter 22 The Topological Framework 9 Chapter 23 The Euclidean Geometric Framework 23 Chapter 24 The Convex Geometric Framework 37 Chapter 25 the Morphological Geometric Framework 47 Chapter 26 The Geometric and Topological Framework 57 Chapter 27 The Measure-Theoretic Geometric Framework 71 Chapter 28 The Integral Geometric Framework 89 Chapter 29 The Differential Geometric Framework 111 Chapter 30 The Variational Geometric Framework 129 Chapter 31 The Stochastic Geometric Framework 135 Chapter 32 The Stereological Framework 159 Part 6 Four Specific Geometrical Framework for Binary Images 177 Chapter 33 The Granulometric Geometric Framework 179 Chapter 34 The Morphometric Geometric Framework 189 Chapter 35 The Fractal Geometric Framework 211 Chapter 36 The Textural Geometric Framework 229 Part 7 Four 'Hybrid' Framework for Gray-Tone and Binary Images 241 Chapter 37 The Interpolative Framework 243 Chapter 38 The Bounded-Variation Framework 253 Chapter 39 The Level Set Framework 269 Chapter 40 The Distance-Map Framework 281 Concluding Discussion and Perspectives 295 Appendices 301 Tables of Notations and Symbols 303 Table of Acronyms 341 Table of Latin Phrases 347 Bibliography 349 Index of Authors 435 Index of Subjects 445

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Book SynopsisVolume 3 of the second edition of the fully revised and updated Digital Signal and Image Processing using MATLAB, after first two volumes on the "Fundamentals" and "Advances and Applications: The Deterministic Case", focuses on the stochastic case. It will be of particular benefit to readers who already possess a good knowledge of MATLAB, a command of the fundamental elements of digital signal processing and who are familiar with both the fundamentals of continuous-spectrum spectral analysis and who have a certain mathematical knowledge concerning Hilbert spaces. This volume is focused on applications, but it also provides a good presentation of the principles. A number of elements closer in nature to statistics than to signal processing itself are widely discussed. This choice comes from a current tendency of signal processing to use techniques from this field. More than 200 programs and functions are provided in the MATLAB language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.Table of ContentsForeword ix Notations and Abbreviations xiii 1 Mathematical Concepts 1 1.1 Basic concepts on probability 1 1.2 Conditional expectation 9 1.3 Projection theorem 10 1.4 Gaussianity 13 1.5 Random variable transformation 18 1.6 Fundamental statistical theorems 21 1.7 Other important probability distributions 23 2 Statistical Inferences 25 2.1 Statistical model 25 2.2 Hypothesis tests 27 2.3 Statistical estimation 41 3 Monte-Carlo Simulation 85 3.1 Fundamental theorems 85 3.2 Stating the problem 86 3.3 Generating random variables 88 3.4 Variance reduction 99 4 Second Order Stationary Process 107 4.1 Statistics for empirical correlation 107 4.2 Linear prediction of WSS processes 111 4.3 Non-parametric spectral estimation of WSS processes 124 5 Inferences on HMM 139 5.1 Hidden Markov Models (HMM) 130 5.2 Inferences on HMM 142 5.3 Gaussian linear case: the Kalman filter 143 5.4 Discrete finite Markov case 152 6 Selected Topics 163 6.1 High resolution methods 163 6.2 Digital Communications 186 6.3 Linear equalization and the Viterbi algorithm 211 6.4 Compression 220 7 Hints and Solutions 235 H1 Mathematical concepts 235 H2 Statistical inferences 237 H3 Monte-Carlo simulation 269 H4 Second order stationary process 283 H5 Inferences on HMM 283 H6 Selected Topics 300 8 Appendices 317 A1 Miscellaneous functions 317 A2 Statistical functions 318 Bibliography 329 Index 333

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Book SynopsisThis is the first book that focuses entirely on the fundamental questions in visualization. Unlike other existing books in the field, it contains discussions that go far beyond individual visual representations and individual visualization algorithms. It offers a collection of investigative discourses that probe these questions from different perspectives, including concepts that help frame these questions and their potential answers, mathematical methods that underpin the scientific reasoning of these questions, empirical methods that facilitate the validation and falsification of potential answers, and case studies that stimulate hypotheses about potential answers while providing practical evidence for such hypotheses. Readers are not instructed to follow a specific theory, but their attention is brought to a broad range of schools of thoughts and different ways of investigating fundamental questions. As such, the book represents the by now most significant collective effort for gathering a large collection of discourses on the foundation of data visualization. Data visualization is a relatively young scientific discipline. Over the last three decades, a large collection of computer-supported visualization techniques have been developed, and the merits and benefits of using these techniques have been evidenced by numerous applications in practice. These technical advancements have given rise to the scientific curiosity about some fundamental questions such as why and how visualization works, when it is useful or effective and when it is not, what are the primary factors affecting its usefulness and effectiveness, and so on. This book signifies timely and exciting opportunities to answer such fundamental questions by building on the wealth of knowledge and experience accumulated in developing and deploying visualization technology in practice.Table of ContentsPart I: Theoretical Underpinnings of Data Visualization.- The Fabric of Visualization.- Visual Abstraction.- Measures in Visualization Space.- Knowledge-Assisted Visualization and Guidance.- Mathematical Foundations in Visualizations.- Transformations, Mappings and Data Summaries.- Part II: Empirical Studies in Visualization.- A Survey of Variables Used in Empirical Studies for Visualization.- Empirical Evaluations with Domain Experts.- Evaluation of Visualization Systems with Long-term Case Studies.- Vis4Vis: Visualization for (Empirical) Visualization Research.- 'Isms' in Visualization.- Open Challenges in Empirical Visualization Research.- Part III: Collaboration with Domain Experts.- Case Studies for Working with Domain Experts.- Collaboration Between Industry and University.- Collaborating Successfully with Domain Experts.- Part IV: Developing Visualizations for Broad Audiences.- Reflections on Visualization for Broad Audiences.- Reaching Broad Audiences from a Research Institute Setting.- Reaching Broad Audiences from a Large Agency Setting.- Reaching Broad Audiences from a Science Center or Museum Setting.- Reaching Broad Audiences in an Educational Setting.- Challenges and Open Issues in Visualization for Broad Audiences

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Book SynopsisThis textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field — What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. “An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, StanfordTable of ContentsIntroduction.- Part I: Foundations- Representing Position and Orientation.- Part II: Computer Vision.- Light and Color.- Images and Image Processing.- Image Feature Extraction.- Part III: The Geometry of Vision.- Image Formation.- Using Multiple Images.- Index.

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Book SynopsisThis four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.The papers are organized into the following topical sections: extracting document semantics, text and symbol recognition, document analysis systems, office automation, signature verification, document forensics and provenance analysis, pen-based document analysis, human document interaction, document synthesis, and graphs recognition.Table of ContentsExtracting Document Semantics.- MiikeMineStamps: A Long-Tailed Dataset of Japanese Stamps via Active Learning.- Deep Learning for Document Layout Generation: A First Reproducible Quantitative Evaluation and a Baseline Model.- Text and Symbol Recognition.- MRD: A Memory Relation Decoder for Online Handwritten Mathematical Expression Recognition.-Full Page Handwriting Recognition via Image to Sequence Extraction.- SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition.- IHR-NomDB: The Old Degraded Vietnamese Handwritten Script Archive Database.- Sequence Learning Model for Syllables Recognition Arranged in Two Dimensions.- Transformer for Handwritten Text Recognition using Bidirectional Post-Decoding.- Zero-Shot Chinese Text Recognition via Matching Class Embedding.- Text-conditioned Character Segmentation for CTC-based Text Recognition.-Towards Fast, Accurate and Compact Online Handwritten Chinese Text Recognition.- HCADecoder: A Hybrid CTC-Attention Decoder for Chinese Text Recognition.-Meta-learning of Pooling Layers for Character Recognition.- Document Analysis Systems.- Text-line-up: Don’t Worry about the Caret.- Multimodal Attention-based Learning for Imbalanced Corporate Documents Classification.- Light-weight Document Image Cleanup using Perceptual Loss.- Office Automation.- A New Semi-Automatic Annotation Model via Semantic Boundary Estimation for Scene Text Detection.- Searching from the Prediction of Visual and Language Model for Handwritten Chinese Text Recognition.- Towards an IMU-based Pen Online Handwriting Recognizer.- Signature Verification.- 2D vs 3D online writer identification: a comparative study.- A Handwritten Signature Segmentation Approach for Multi-resolution and Complex Documents Acquired by Multiple Sources.- Attention based Multiple Siamese Network for Offline Signature Verification.- Attention to Warp: Deep Metric Learning for Multivariate Time Series.- Document Forensics and Provenance Analysis.- Customizable Camera Verification for Media Forensic.- Density Parameters of Handwriting in Schizophrenia and Affective Disorders Assessed Using the Raygraf Computer Software.- Pen-based Document Analysis.- Language-Independent Bimodal System for Early Parkinson’s Disease Detection.-TRACE: A Differentiable Approach to Line-level Stroke Recovery for Offline Handwritten Text.- Segmentation and graph matching for online analysis of student arithmetic operations.- Applying End-to-end Trainable Approach on Stroke Extraction in Handwritten Math Expressions Images.- A Novel Sigma-Lognormal Parameter Extractor for Online Signatures.- Human Document Interaction.- Near-perfect Relation Extraction from Family Books.- Estimating Human Legibility in Historic Manuscript Images - A Baseline.- A Modular and Automated Annotation Platform for Handwritings: Evaluation on Under-resourced Languages.- Reducing the Human Effort in Text Line Segmentation for Historical Documents.- DSCNN: Dimension Separable Convolutional Neural Networks for character recognition based on inertial sensor signal.- Document Synthesis.- DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis.- Font Style that Fits an Image -- Font Generation Based on Image Context.- Bayesian Hyperparameter optimization of Deep Neural Network algorithms based on Ant Colony optimization.- End-to-End Approach for Recognition of Historical Digit Strings.- Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs.- Synthesizing Training Data for Handwritten Music Recognition.- Towards Book Cover Design via Layout Graphs.- Graphics Recognition.- Complete Optical Music Recognition via Agnostic Transcription and Machine Translation.- Improving Machine Understanding of Human Intent in Charts.- DeMatch: Towards Understanding the Panel of Chart Documents.- Sequential Next-Symbol Prediction for Optical Music Recognition.- Which Parts Determine the Impression of the Font?.- Impressions2Font: Generating Fonts by Specifying Impressions.

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Book SynopsisThis four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.The papers are organized into the following topical sections: scene text detection and recognition, document classification, gold-standard benchmarks and data sets, historical document analysis, and handwriting recognition. In addition, the volume contains results of 13 scientific competitions held during ICDAR 2021.Table of ContentsScene Text Detection and Recognition.- HRRegionNet: Chinese Character Segmentation in Historical Documents with Regional Awareness.- Fast Text v. Non-text Classification of Images.- Mask Scene Text Recognizer.- Rotated Box Is Back: An Accurate Box Proposal Network for Scene Text Detection.- Heterogeneous Network Based Semi-supervised Learning For Scene Text Recognition.- Scene Text Detection with Scribble Line.- EEM: An End-to-end Evaluation Metric for Scene Text Detection and Recognition.- SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models.- Fast Recognition for Multidirectional and Multi-Type License Plates with 2D Spatial Attention.- A Multi-level Progressive Rectification Mechanism for Irregular Scene Text Recognition.- Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition.- FEDS - Filtered Edit Distance Surrogate.- Bidirectional Regression for Arbitrary-Shaped Text Detection.- Document Classification.- VML-HP: Hebrew paleography dataset.- Open Set Authorship Attribution toward Demystifying Victorian Periodicals.- A More Effective Sentence-Wise Text Segmentation Approach using BERT.- Data Augmentation for Writer Identification Using a Cognitive Inspired Model.- Key-guided Identity Document Classification Method by Graph Attention Network.- Document Image Quality Assessment via Explicit Blur and Text Size Estimation.- Analyzing the potential of Zero-Shot Recognition for Document Image Classification.- Gender Detection Based on Spatial Pyramid Matching.- EDNets: Deep Feature Learning for Document Image Classification based on Multi-view Encoder-Decoder Neural Networks.- Fast End-to-end Deep Learning Identity Document Detection, Classification and Cropping.- Gold-Standard Benchmarks and Data Sets.- Image Collation: Matching illustrations in manuscripts.- Revisiting the Coco Panoptic Metric to Enable Visual and Qualitative Analysis of Historical Map Instance Segmentation.- A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes.- GNHK: A Dataset for English Handwriting in the Wild.- Personalizing Handwriting Recognition Systems with Limited User-Specific Samples.- An Efficient Local Word Augment Approach for Mongolian Handwritten Script Recognition.- IIIT-INDIC-HW-WORDS: A Dataset for Indic Handwritten Text Recognition.- Historical Document Analysis.- AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions.- TS-Net: OCR Trained to Switch Between Text Transcription Styles.- Handwriting Recognition with Novelty.- Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction.- Data Augmentation Based on CycleGAN for Improving Woodblock-printing Mongolian Words Recognition.- SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization.- Handwriting Recognition.- Recognizing Handwritten Chinese Texts with Insertion and Swapping Using A Structural Attention Network.- Strikethrough Removal From Handwritten Words Using CycleGANs.- Iterative Weighted Transductive Learning for Handwriting Recognition.- Competition Reports.- ICDAR 2021 Competition on Scientific Literature Parsing.- ICDAR 2021 Competition on Historical Document Classification.- ICDAR 2021 Competition on Document Visual Question Answering.- ICDAR 2021 Competition on Scene Video Text Spotting.- ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment.- ICDAR 2021 Competition on Components Segmentation Task of Document Photos.- ICDAR 2021 Competition on Historical Map Segmentation.- ICDAR 2021 Competition on Time-Quality Document Image Binarization.- ICDAR 2021 Competition on On-Line Signature Verification.- ICDAR 2021 Competition on Script Identification in the Wild.- ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.- ICDAR 2021 Competition on Multimodal Emotion Recognition on Comics Scenes.- ICDAR 2021 Competition on Mathematical Formula Detection.

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Book SynopsisThis open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.Table of ContentsPart I - Introduction: 1. Digital Face Manipulation: An Introduction.- 2. Face Manipulation in Biometric Systems.- 3. Face Manipulation in Media Forensics.- Part II - Face Manipulation Detection Methods: 4. DeepFakes Manipulation.- 5. DeepFakes Detection.- 6. Attacking Face Recognition Systems with DeepFakes.- 7. Vulnerability of Face Recognition Systems to Morphing Attacks.- 8. Face Morphing Attack Detection.- 9. Face Synthesis Detection.- 10. Expression Swap Detection.- 11. Audio- and Text-to-Video Detection.- 12. Detection of Facial Retouching.- 13. Face De-Identification Detection.- Part III - Further Topics: 14. All-in-One Face Manipulation Detection: Generalization Analysis.- 15. Reversion of Face Manipulation.- 16. 3D Face Manipulation Detection.- 17. Improving Face Recognition with Face Image Manipulation.- 18. Impact of Post-Processing on Face Manipulation Detection.- 19. Societal and Legal Aspects of Face Manipulation.- 20. Face Manipulation for Privacy Protection.- 21. Privacy-preserving Face Manipulation Detection.- 22. Face Manipulation in Operational Systems.- Part IV - Open Issues, Trends, and Challenges: 23. All: Future trends in face Manipulation and Fake Detection.

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Book SynopsisThis book, geared towards both students and professionals, examines the synthesis of artificial intelligence (AI) and psychology in detecting mis-/disinformation in digital media content, and suggests practical means to intervene and curtail this current global ‘infodemic’. This interdisciplinary book explores technological, psychological, philosophical, and linguistic insights into the nature of truth and deception, trust and credibility, cognitive biases and logical fallacies and how, through AI and human intervention, content users can be alerted to the presence of deception. The author investigates how AI can mimic the procedures and know-hows of humans, showing how AI can help spot fakes and how AI tools can work to debunk rumors and fact-check. The book describes how AI detection systems work and how they fit with broader societal and individual concerns. Each chapter focuses attention on key concepts and their inter-connection. The first part of the book seeks theoretical footing to understand our interactions with new information and reviews relevant empirical findings in behavioral sciences. The second part is about applied knowledge. The author looks at several known practices that guard us against deception, and provides several real-world examples of manipulative persuasive techniques in advertising, political propaganda, and public relations. She provides links to the downloadable executable files to three AI applications (clickbait, satire, and falsehood detectors) via LiT.RL GitHub, an open access repository. The book is useful to students and professionals studying AI and media studies as well as library and information professionals. Examines how artificial intelligence (AI) and psychology can aid in detecting mis-/disinformation and the language of deceit in digital media content; Suggests practical computational means to intervene and curtail the global ‘infodemic’ of fake news; Presents how AI can sift, sort, and shuffle digital content, to reduce the amount of content needed to be reviewed by humans. Table of ContentsIntroduction.- Infodemic in the Digital Media Content.- Part I. Human Nature of Deception and Perceptions of Truth.- Psychology of Mis-/Disinformation and Language of Deceit.- Library and Information Science on Credibility and Trust in Computing.- Philosophies of Truth.- Part II. Applied Practices and Artificial Intelligence (AI).- Investigation in Law Enforcement, Journalism, and Sciences.- Manipulation in Marketing, Advertising, and Public Relations.- Artificially Intelligent Solutions: Detection, Debunking, Fact-Checking.- Lessons for Infodemic Control and Future of Digital Content Verification.- Conclusion.

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Book SynopsisThis book gathers outstanding research papers presented at the International Conference on Intelligent Vision and Computing (ICIVC 2021), held online during October 03–04, 2021. ICIVC 2021 is organised by Sur University, Oman. The book presents novel contributions in intelligent vision and computing and serves as reference material for beginners and advanced research. The topics covered are intelligent systems, intelligent data analytics and computing, intelligent vision and applications collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal natural language processing.Table of ContentsHandwritten Bengali Digit Classification using Deep Learning.- IOT Based COVID Patient Health Monitoring System In Quarantine.- Self-attention Convolution for Sparse to Dense Depth Completion.- Using Algorithm in Parametric Design as an Approach to Inspire Nature in Architectural Design.- Docker Container Orchestration Management in Cloud Computing.- Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index.

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Book SynopsisComputer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.Table of ContentsPreface.- Acknowledgments.- Introduction.- Features and Classifiers.- Neural Networks Basics.- Convolutional Neural Network.- CNN Learning.- Examples of CNN Architectures.- Applications of CNNs in Computer Vision.- Deep Learning Tools and Libraries.- Conclusion.- Bibliography.- Authors' Biographies.

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Book SynopsisThis two-volume set of LNCS 13393 and LNCS 13394 constitutes - in conjunction with the volume LNAI 13395 - the refereed proceedings of the 18th International Conference on Intelligent Computing, ICIC 2022, held in Xi'an, China, in August 2022. The 209 full papers of the three proceedings volumes were carefully reviewed and selected from 449 submissions.This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications”. Papers focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.Table of ContentsEvolutionary Computing and Learning. Neural Networks.- Pattern Recognition.- Image Processing.- Information Security.- Biomedical Informatics Theory and Methods.- Biomedical Data Modeling and Mining.- Intelligent Computing in Computational Biology.- Computational Genomics and Biomarker Discovery.- Intelligent Computing in Drug Design.- Theoretical Computational Intelligence and Applications.- Fuzzy Theory and Algorithms.- Machine Learning and Data Mining.- Intelligent Computing in Computer Vision.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Intelligent Computing and Optimization.

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Book SynopsisThis two-volume set of LNCS 13393 and LNCS 13394 constitutes - in conjunction with the volume LNAI 13395 - the refereed proceedings of the 18th International Conference on Intelligent Computing, ICIC 2022, held in Xi'an, China, in August 2022. The 209 full papers of the three proceedings volumes were carefully reviewed and selected from 449 submissions.This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications”. Papers focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.Table of ContentsEvolutionary Computing and Learning. Neural Networks.- Pattern Recognition.- Image Processing.- Information Security.- Biomedical Informatics Theory and Methods.- Biomedical Data Modeling and Mining.- Intelligent Computing in Computational Biology.- Computational Genomics and Biomarker Discovery.- Intelligent Computing in Drug Design.- Theoretical Computational Intelligence and Applications.- Fuzzy Theory and Algorithms.- Machine Learning and Data Mining.- Intelligent Computing in Computer Vision.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Intelligent Computing and Optimization.

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Book SynopsisThis two volume proceedings, LNCS 13445 and 13446, constitutes the refereed proceedings of the 9th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics, XR Salento 2022, held in Lecce, Italy, July 6–8, 2022. Due to COVID-19 pandemic the conference was held as a hybrid conference.The 42 full and 16 short papers were carefully reviewed and selected from 84 submissions. The papers discuss key issues, approaches, ideas, open problems, innovative applications and trends in virtual reality, augmented reality, mixed reality, applications in cultural heritage, in medicine, in education, and in industry.Table of ContentseXtended Reality for learning and training.- Mixed Reality Agents for Automated Mentoring Processes.- Asynchronous Manual Work in Mixed Reality Remote Collaboration.- A virtual reality serious game for children with dyslexia: DixGame.- Processing Physiological Sensor Data in Near Real-Time as Social Signals for their Use on Social Virtual Reality Platforms.- Developing a tutorial for improving usability and user skills in an immersive virtual reality experience.- Challenges in Virtual Reality Training for CRBN Events.- A Preliminary Study on the Teaching Mode of Interactive VR Painting Ability Cultivation.- eXtended Reality in education.- Factors in the cognitive-emotional impact of Educational Environmental Narrative Videogames.- Instinct-based Decision-making in Interactive Narratives.- The application of immersive virtual reality for children's road education: validation of a pedestrian crossing scenario.- Collaborative VR scene broadcasting for geometry education.- Collaborative Mixed Reality Annotations System for Science and History Education Based on UWB Positioning and Low-cost AR Glasses.- Artificial Intelligence and Machine Learning for eXtended Reality.- Can AI replace conventional markerless tracking? A comparative performance study for mobile augmented reality based on artificial intelligence..- Find, Fuse, Fight: genetic algorithms to provide engaging content for multiplayer augmented reality games.- Synthetic data generation for surface defect detection*.- eXtended Reality in Geo-Information Sciences.- ARtefact: A Conceptual Framework for the Integrated Information Management of Archaeological Excavations.- Geomatics meets XR: a brief overview of the synergy between Geospatial data and augmented visualization..- Utilization of Geographic Data for the Creation of Occlusion Models in the Context of Mixed Reality Applications.- Development of an open-source 3D WebGIS framework to promote cultural heritage dissemination.- Industrial eXtended Reality.- A FRAMEWORK FOR DEVELOPING XR APPLICATIONS INCLUDING MULTIPLE SENSORIAL MEDIA.- Augmented Reality Remote Maintenance in Industry: A Systematic Literature Review.- Virtual Teleoperation Setup for a Bimanual Bartending Robot.- eXtended Reality in the digital transformation of museums.- Virtualization and Vice Versa: A new procedural model of the reverse virtualization for the user behavior tracking in the Virtual Museums.- “You can tell a man by the emotion he feels”: How Emotions Influence Visual Inspection of Abstract Art in Immersive Virtual Reality.- Augmented Reality and 3D Printing for Archaeological Heritage: Evaluation of Visitor Experience.- Building blocks for multi-dimensional WebXR inspection tools targeting Cultural Heritage.- Comparing the impact of low-cost 360° cultural heritage videos displayed in 2D screens versus virtual reality headsets.- eXtended Reality beyond the five senses.- Non-immersive versus immersive extended reality for motor imagery neurofeedback within a brain-computer interfaces.- Virtual Reality enhances EEG-based neurofeedback for emotional self-regulation.- Psychological and Educational Interventions Among Cancer Patients: A Systematic Review to Analyze the Role of Immersive Virtual Reality for Improving Patients’ Well-Being.

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Book SynopsisThis book constitutes the refereed proceedings of the 19th International Conference on Virtual Reality and Mixed Reality, EuroXR 2022, held in Stuttgart, Germany, in September 2022.The 6 full and 2 short papers were carefully reviewed and selected from 37 submissions. The conference presents contributions on results and insights in Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), commonly referred to under the umbrella of Extended Reality (XR), including software systems, immersive rendering technologies, 3D user interfaces, and applications. Table of ContentsDesigning Functional Prototypes Combining BCI and AR for Home Automation.- SightX: A 3D Selection Technique for XR.- Design and Evaluation of Three User Interfaces for Detecting Unmanned Aerial Vehicles Using Virtual Reality.- Evaluating the Acceptability and Usability of a Head-Mounted Augmented Reality Approach for Autistic Children With High Support Needs.- Exploiting Augmented Reality in LEGO Therapy for Children with Autism Spectrum Disorder.- Evaluation of Point Cloud Streaming and Rendering for VR-based Telepresence in the OR.- Fast Intra-Frame Video Splicing for Occlusion Removal in Diminished Reality.- Coupling AR with object detection neural networks for end-user engagement.- A Procedural Building Generator based on Real-World Data Enabling Designers to Create Context for XR Automotive Design Experiences.- Generating VR meeting rooms with non-rectangular floor plans using cost optimization and hard constraints.- Controlling Continuous Locomotion in Virtual Reality With Bare Hands using Hand Gestures.- An Augmented Reality solution for the Positive Behaviour Intervention and Support.- The Reality of Virtual Experiences: Semantic and Epi-sodic Memory Formation in VR.

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Book SynopsisThe eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies. Table of ContentsBrain Development and Atlases.- Progression models for imaging data with Longitudinal Variational Auto Encoders.- Boundary-Enhanced Self-Supervised Learning for Brain Structure Segmentation.- Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline.- 3D Global Fourier Network for Alzheimer’s Disease Diagnosis using Structural MRI.- CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis.- Interpretable differential diagnosis for Alzheimer’s disease and Frontotemporal dementia.- Is a PET all you need? A multi-modal study for Alzheimer’s disease using 3D CNNs.- Unsupervised Representation Learning of Cingulate Cortical Folding Patterns.- Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer’s disease detection.- Extended Electrophysiological Source Imaging with Spatial Graph Filters.- DWI and Tractography.- Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data.- Atlas-powered deep learning (ADL) - application to diffusion weighted MRI.- One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation.- Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner.- An adaptive network with extragradient for diffusion MRI-based microstructure estimation.- Shape-based features of white matter fiber-tracts associated with outcome in Major Depression Disorder.- White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning.- Segmentation of Whole-brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve Points.- TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers.- Multi-site Normative Modeling of Diffusion Tensor Imaging Metrics Using Hierarchical Bayesian Regression.- Functional Brain Networks.- Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification.- Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome.- Decoding Task Sub-type States with Group Deep Bidirectional Recurrent Neural Network.- Hierarchical Brain Networks Decomposition via Prior Knowledge Guided Deep Belief Network.- Interpretable signature of consciousness in resting-state functional network brain activity.- Nonlinear Conditional Time-varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels.- fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits.- Multi-head Attention-based Masked Sequence Model for Mapping Functional Brain Networks.- Dual-HINet: Dual Hierarchical Integration Network of Multigraphs for Connectional Brain Template Learning.- RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis.- Modelling Cycles in Brain Networks with the Hodge Laplacian.- Predicting Spatio-Temporal Human Brain Response Using fMRI.- Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer.- Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior.- Embedding Human Brain Function via Transformer.- How Much to Aggregate: Learning Adaptive Node-wise Scales on Graphs for Brain Networks.- Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport.- The Semi-constrained Network-Based Statistic (scNBS): integrating local and global information for brain network inference.- Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder.- Neuroimaging.- Characterization of brain activity patterns across states of consciousness based on variational auto-encoders.- Conditional VAEs for confound removal and normative modelling of neurodegenerative diseases.- Semi-supervised learning with data harmonisation for biomarker discovery from resting state fMRI.- Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning.- Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease.- Heart and Lung Imaging.- AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images.- Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans.- What Makes for Automatic Reconstruction of Pulmonary Segments.- CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs.- Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation.- Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision.- Diffusion Deformable Model for 4D Temporal Medical Image Generation.- SAPJNet: Sequence-Adaptive Prototype-Joint Network for Small Sample Multi-Sequence MRI Diagnosis.- Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification.- Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View.- CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays.- Reinforcement learning for active modality selection during diagnosis.- Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings.- Attention mechanisms for physiological signal deep learning: which attention should we take?.- Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance.- Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose.- RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment.- A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000.- LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection.- Consistency-based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification.- Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph.- Rib Suppression in Digital Chest Tomosynthesis.- Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network.- Dermatology.- Data-Driven Deep Supervision for Skin Lesion Classification.- Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images.- FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis.- Reliability-aware Contrastive Self-ensembling for Semi-supervised Medical Image Classification.

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Book SynopsisThe eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies. Table of ContentsMachine learning – weakly-supervised learning.- machine learning – model interpretation.- machine learning – uncertainty.- machine learning theory and methodologies.

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Book SynopsisThis book constitutes the refereed proceedings of the first MICCAI Workshop on Resource-Efficient Medical Image Analysis, REMIA 2022, held in conjunction with MICCAI 2022, in September 2022 as a hybrid event. REMIA 2022 accepted 13 papers from the 19 submissions received. The workshop aims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations.Table of ContentsMulti-Task Semi-Supervised Learning for Vascular Network.- Segmentation and Renal Cell Carcinoma Classification.- Self-supervised Antigen Detection Artificial Intelligence (SANDI).- RadTex: Learning Effcient Radiograph Representations from Text Reports.- Single Domain Generalization via Spontaneous Amplitude Spectrum Diversification.- Triple-View Feature Learning for Medical Image Segmentation.- Classification of 4D fMRI Images Using ML, Focusing on Computational and Memory Utilization Effciency.- An Effcient Defending Mechanism Against Image Attacking On Medical Image Segmentation Models.- Leverage Supervised and Self-supervised Pretrain Models for Pathological Survival Analysis via a Simple and Low-cost Joint Representation Tuning.- Pathological Image Contrastive Self-Supervised Learning.- Investigation of Training Multiple Instance Learning Networks with Instance Sampling.- Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound.- A self-attentive meta-learning approach for image-based few-shot disease detection.- Facing Annotation Redundancy: OCT Layer Segmentation with Only 10 Annotated Pixels Per Layer.

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Book SynopsisThis book constitutes the proceedings of the First Workshop on Computational Mathematics Modeling in Cancer Analysis (CMMCA2022), held in conjunction with MICCAI 2022, in Singapore in September 2022. Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually. DALI 2022 accepted 15 papers from the 16 submissions that were reviewed. A major focus of CMMCA2022 is to identify new cutting-edge techniques and their applications in cancer data analysis in response to trends and challenges in theoretical, computational and applied aspects of mathematics in cancer data analysis.Table of ContentsCellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma .- Is More Always Better? Effects of Patch Sampling in Distinguishing Chronic Lymphocytic Leukemia from Transformation to Diffuse Large B-cell Lymphoma.- Repeatability of Radiomic Features against Simulated Scanning Position Stochasticity across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-Institutional Study on Head-and-Neck Cases.- MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism.- NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images.- Self-supervised learning based on a pre-trained method for the subtype classification of spinal tumors.- CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis.- Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns.- Modality-collaborative AI model Ensemble for Lung Cancer Early Diagnosis.- Clustering-based Multi-instance Learning Network for Whole Slide Image Classification.- Multi-task Learning-driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification.- Light Annotation Fine Segmentation: Histology Image Segmentation based on VGG Fusion with Global Normalisation CAM.- Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical Cancer Radiotherapy.- Automatic Computer-aided Histopathologic Segmentation for Nasopharyngeal Carcinoma using Transformer Framework.- Accurate Breast Tumor Identification UsingComputational Ultrasound Image Features.

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Book SynopsisThe 4-volume set LNCS 13534, 13535, 13536 and 13537 constitutes the refereed proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022, held in Shenzhen, China, in November 2022.The 233 full papers presented were carefully reviewed and selected from 564 submissions. The papers have been organized in the following topical sections: Theories and Feature Extraction; Machine learning, Multimedia and Multimodal; Optimization and Neural Network and Deep Learning; Biomedical Image Processing and Analysis; Pattern Classification and Clustering; 3D Computer Vision and Reconstruction, Robots and Autonomous Driving; Recognition, Remote Sensing; Vision Analysis and Understanding; Image Processing and Low-level Vision; Object Detection, Segmentation and Tracking.Table of Contents​Image Processing and Low-level Vision.- Video Deraining via Temporal Discrepancy Learning.- Multi-priors Guided Dehazing Network Based on Knowledge Distillation.- DLMP-Net: a dynamic yet lightweight multi-pyramid network for crowd density estimation.- CHENet: Image to Image Chinese Handwriting Eraser.- Identidication method for rice pests with small sample size problem combining deep learning and metric learning.- Boundary-Aware Polyp Segmentation Network.- SUDANet:A Siamese UNet with Dense Attention Mechanism for Remote Sensing Image Change Detection.- A Local-Global Self-attention Interaction Network for RGB-D Cross-modal Person Re-identification.- A RAW Burst Super-Resolution Method with Enhanced Denoising.- Unpaired and Self-supervised Optical Coherence Tomography Angiography Super-resolution.- Multi-Feature Fusion Network for Single Image Dehazing.- LAGAN: Landmark Aided Text to Face Sketch Generation.- DMF-CL: Dense Multi-scale Feature Contrastive Learning for Semantic segmentation of Remote-sensing images.- Image derain method for generative adversarial network based on wavelet high frequency feature fusion.- GPU-Accelerated Infrared Patch-Image Model for Small Target Detection.- Hyperspectral and Multispectral Image Fusion Based on Unsupervised Feature Mixing and Reconstruction Network.- Information Adversarial Disentanglement for Face Swapping.- A Dense Prediction ViT Network for Single Image Bokeh Rendering.- Multi-scale Coarse-to-fine Network for Demoiring.- Learning Contextual Embedding Deep Networks for Accurate and Efficient Image Deraining.- A Stage-Mutual-Ane Network for Single Remote Sensing Image Super-Resolution.- Style-based Attentive Network for Real-World Face Hallucination.- Cascade Scale-aware Distillation Network for Lightweight RemoteSensing Image Super-Resolution.- Few-Shot Segmentation via Rich Prototype Generation and RecurrentPrediction Enhancement.- Object Detection, Segmentation and Tracking.- TAFDet: A Task Awareness Focal Detector for Ship Detection in SAR Images.- MSDNet:Multi-scale Dense Networks for Salient Object Detection.- WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation.- Infrared Object Detection Algorithm Based on Spatial Feature Enhancement.- Object Detection Based on Embedding Internal and External Knowledge.- ComLoss: A Novel Loss towards More Compact Predictions for Pedestrian Detection.- Remote sensing image detection based on attention mechanism and YOLOv5.- Detection of Pin Defects in Transmission Lines Based on Dynamic Receptive Field.- Identification of bird s nest hazard level of transmission line based on improved yolov5 and location constraints.- Image Magnification Network for Vessel Segmentation in OCTA Images.- CFA-Net: Cross-level Feature Fusion and Aggregation Network for Salient Object Detection.- Disentangled Feature Learning for Semi-supervised Person Re-identification.- Detection Beyond What and Where: A Benchmark for Detecting Occlusion State.- Weakly Supervised Object Localization with Noisy-Label Learning.- Enhanced Spatial Awareness For Deep Interactive Image Segmentation.- Anchor-Free Location Refinement Network for Small License Plate Detection.- Multi-View LiDAR Guided Monocular 3D Object Detection.- Dual Attention-guided Network for Anchor-free Apple Instance Segmentation in Complex Environments.- Attention-Aware Feature Distillation for Object Detection in Decompressed Images.- Cross-Stage Class-Specific Attention for Image Semantic Segmentation.- Defect Detection for High Voltage Transmission Lines Based on Deep Learning.- ORION: Orientation-Sensitive Object Detection.- An Infrared Moving Small Object Detection Method Based on Trajectory Growth.- Two-stage Object Tracking Based on Similarity Measurement for FusedFeatures of Positive and Negative Samples.- PolyTracker: Progressive Contour Regression for Multiple ObjectTracking and Segmentation.- Dual-branch Memory Network for Visual Object Tracking.- Instance-wise Contrastive Learning for Multi-Object Tracking.- Information Lossless Multi-Modal Image Generation for RGB-T Tracking.- JFT: A Robust Visual Tracker Based on Jitter Factor and Global Registration.- Caged Monkey Dataset: A New Benchmark for Caged Monkey Pose Estimation.- WTB-LLL: A Watercraft Tracking Benchmark Derived by Low-light-level Camera.- Dualray: Dual-view X-ray Security Inspection Benchmark and FusionDetection Framework.

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

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