Foundations of computer vision:
"An up-to-date computer vision textbook incorporating the latest deep learning advances that have revolutionized the field over the last decade"--
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| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Massachusetts ; London
The MIT Press
[2024]
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| Series: | Adaptive computation and machine learning series
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | "An up-to-date computer vision textbook incorporating the latest deep learning advances that have revolutionized the field over the last decade"-- |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xxviii, 810 Seiten Illustrationen, Diagramme |
| ISBN: | 9780262048972 0262048973 |
Staff View
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| 100 | 1 | |a Torralba, Antonio |e Verfasser |0 (DE-588)1266670807 |4 aut | |
| 245 | 1 | 0 | |a Foundations of computer vision |c Antonio Torralba, Phillip Isola,and William T. Freeman |
| 264 | 1 | |a Cambridge, Massachusetts ; London |b The MIT Press |c [2024] | |
| 264 | 4 | |c © 2024 | |
| 300 | |a xxviii, 810 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Adaptive computation and machine learning series | |
| 500 | |a Includes bibliographical references and index | ||
| 520 | |a "An up-to-date computer vision textbook incorporating the latest deep learning advances that have revolutionized the field over the last decade"-- | ||
| 650 | 7 | |a Computer vision |2 DLC | |
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| 700 | 1 | |a Freeman, William T. |e Verfasser |0 (DE-588)1330984935 |4 aut | |
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Record in the Search Index
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Contents Preface. xxi Notation. XXV 1 The Challenge of Vision. 1.1 Introduction. 1.2 Vision. 1.3 Theories of Vision. 1.4 What’s Next?. 1.5 Concluding Remarks. 1 1 1 4 25 25 I FOUNDATIONS. 27 2 A Simple Vision System. 2.1
Introduction. 2.2 A Simple World: The Blocks World. 2.3 A Simple Image Formation Model. . 2.4 A Simple Goal . 2.5 From Images to Edges and Useful Features . 2.6 From Edges to Surfaces. 2.7 Generalization. 2.8 Concluding Remarks. 29 29 29 30 31 31 35 40 41 3 Looking at Images. 3.1 Introduction. 3.2 Looking at Individual Pixels. 3.3 The More You Look, the More You See
. 3.4 The Eye of the Artist. 3.5 Tree Shadows and Image Formation . 3.6 Horizontal or Vertical. 3.7 Motion Blur. 3.8 Accidents Happen . 3.9 Cues for Support. 3.10 Looking at Raindrops. 3.11 Plato’s Cave . 43 43 43 45 46 46 48 48 49 50 50 51
Contents viii 3.12 How Do You Know Something Is Wet?. 3.13 Concluding Remarks. 52 52 4 Computer Vision and Society. 4.1 Introduction. 4.2 Fairness. 4.3 Ethics. 4.4 Concluding Remarks. 53 53 53 57 59 II IMAGE FORMATION. 61 5 Imaging. 5.1 Introduction. 5.2 Light Interacting with
Surfaces. 5.3 The Pinhole Camera and Image Formation. 5.4 Concluding Remarks. 63 63 63 65 71 6 Lenses. 6.1 Introduction. 6.2 Lensmaker’s Formula. 6.3 Imaging with Lenses. 6.4 Concluding Remarks. 73 73 74 79 84 7 Cameras as Linear Systems . 7.1 Introduction. 7.2 Flatland. 7.3 Cameras as Linear
Systems. 7.4 More General Imagers. 7.5 Concluding Remarks. 87 87 87 88 89 94 8 Color. 95 8.1 Introduction. 95 8.2 Color Physics. 96 8.3 Color Perception. 101 8.4 Spatial Resolution and Color. 106 8.5 Concluding Remarks. 106 III FOUNDATIONS OF LEARNING. 109 9 Ill Ill Ill 113 113 Introduction to Learning. 9.1
Introduction. 9.2 Learning from Examples. 9.3 Learning without Examples. 9.4 Key Ingredients.
ix Contents Empirical Risk Minimization: A Formalization of Learning from Examples. Learning as Probabilistic Inference. Case Studies. Learning to Learn. Concluding Remarks. 115 115 115 121 122 10 Gradient-Based Learning Algorithms . 10.1 Introduction. 10.2 Technical Setting. 10.3 Basic Gradient Descent. 10.4 Learning Rate Schedules. 10.5 Momentum. 10.6 What Kinds of Functions Can Be Minimizedwith Gradient Descent?. 10.7 Stochastic
Gradient Descent. 10.8 Concluding Remarks. 123 123 123 124 124 125 126 129 131 11 The Problem of Generalization . 11.1 Introduction. 11.2 Underfitting and Overfitting. 11.3 Regularization. 11.4 Rethinking Generalization. 11.5 Three Tools in the Search for Truth: Data, Priors, and Hypotheses . 11.6 Concluding Remarks. 133 133 133 137 138 139 144 12 Neural Networks. 12.1 Introduction. 12.2 The Perceptron: A Simple Model of a
SingleNeuron. 12.3 Multilayer Perceptrons. 12.4 Activations Versus Parameters . 12.5 Deep Nets. 12.6 Deep Learning: Learning with Neural Nets. 12.7 Catalog of Layers. 12.8 Why Are Neural Networks a Good Architecture? . 12.9 Concluding Remarks. 145 145 145 147 148 149 152 155 157 158 13 Neural Networks as Distribution Transformers. 13.1 Introduction. 13.2 A Different Way of Plotting Functions. 13.3 How Deep Nets Remap a Data Distribution. 13.4 Binary
Classifier Example. 13.5 How High-Dimensional Datapoints Get Remapped by Deep Net. 13.6 Concluding Remarks. 159 159 159 160 162 163 164 9.5 9.6 9,7 9.8 9.9
X Contents 165 165 166 166 168 168 169 176 178 178 179 14 Backpropagation. 14.1 Introduction. 14.2 The Trick of Backpropagation: Reuse of Computation. 14.3 Backward for a Generic Layer. 14.4 The Full Algorithm: Forward, Then Backward. 14.5 Backpropagation Over Data Batches. 14.6 Example: Backpropagation for an MLP. 14.7 Backpropagation through DAGs: Branch and Merge. 14.8 Parameter Sharing . 14.9 Backpropagation to the Data. 14.10 Concluding Remarks. IV FOUNDATIONS OF IMAGE
PROCESSING. 181 15 Linear Image Filtering. 183 15.1 Introduction. 183 15.2 Signals and Images. 183 15.3 Systems. 186 15.4 Convolution. 189 15.5 Cross-Correlation Versus Convolution. 195 15.6 System Identification. 198 15.7 Concluding Remarks. 200 16 Fourier Analysis . 201 16.1 Introduction.201 16.2
Image Transforms.201 16.3 Fourier Series. 201 16.4 Continuous and Discrete Waves. 203 16.5 The Discrete Fourier Transform. 206 16.6 Useful Transforms.209 16.7 Discrete Fourier Transform Properties. 213 16.8 A Family of Fourier Transforms. 217 16.9 Fourier Analysis as an Image Representation. 219 16.10 Fourier Analysis of Linear Filters. 222 16.11 Concluding Remarks. 226 V LINEAR FILTERS. 229 17 Blur
Filters. .231 17.1 Introduction.231 17.2 Box Filter . 232 17.3 Gaussian Filter.234
Contents 17.4 17.5 xi Binomial Filters. 237 Concluding Remarks. 239 18 Image Derivatives. 241 18.1 Introduction. 241 18.2 Discretizing Image Derivatives. 242 18.3 Gradient-Based Image Representation.245 18.4 Image Editing in the Gradient Domain . 246 18.5 Gaussian Derivatives. 247 18.6 High-Order Gaussian Derivatives. 248 18.7 Derivatives of Binomial Filters. 251 18.8 Image Gradient and Directional Derivatives.252 18.9 Image
Laplacian. 253 18.10 A Simple Model of the Early Visual System. 256 18.11 Sharpening Filter. 258 18.12 Retinex. 259 18.13 Concluding Remarks. 262 19 Temporal Filters. 263 19.1 Introduction. 263 19.2 Modeling Sequences. 263 19.3 Modeling Sequences in the Fourier Domain. 264 19.4 Temporal Filters. 265 19.5 Concluding Remarks. 270 VI SAMPLING AND
MULTISCALE IMAGE REPRESENTATIONS. 271 20 Image Sampling and Aliasing. 273 20.1 Introduction.273 20.2 Aliasing.273 20.3 Sampling Theorem. 275 20.4 Reconstruction.278 20.5 Ideal Reconstruction . 278 20.6 A Family of 2D Spatial Samplings. 282 20.7 Anti-Aliasing Filter. 284 20.8 Spatiotemporal Sampling . 285 20.9 Concluding Remarks. 286 21 Downsampling and Upsampling Images
. 287 21.1 Introduction. 287 21.2 Example: Aliasing-Based Adversarial Attack. 287 21.3 Downsampling.288
xii Contents 21.4 21.5 Upsampling. 298 Concluding Remarks. 303 22 Filter Banks. 305 22.1 Introduction. 305 22.2 Gabor Filters. 305 22.3 Steerable Filters and Orientation Analysis. 312 22.4 Motion Analysis.318 22.5 Concluding Remarks. 320 23 Image Pyramids. 321 23.1 Introduction. 321 23.2 Image Pyramids and Multiscale Image Analysis . 322 23.3 Linear Image Transforms
.323 23.4 Gaussian Pyramid.323 23.5 Laplacian Pyramid. 325 23.6 Steerable Pyramid . 329 23.7 A Pictorial Summary. 330 23.8 Concluding Remarks. 332 VII NEURAL ARCHITECTURES FOR VISION .333 24 Convolutional Neural Nets . 335 24.1 Introduction. 335 24.2 Convolutional Layers. 336 24.3 Nonlinear Filtering Layers .344 24.4 A Simple CNN Classifier
. 346 24.5 A Worked Example. 347 24.6 Feature Maps in CNNs . 350 24.7 Receptive Fields. 351 24.8 Spatial Outputs. 353 24.9 CNN as a Sliding Filter. 354 24.10 Why Process Images Patch by Patch? . 354 24.11 Popular CNN Architectures. 355 24.12 Concluding Remarks. 358 25 Recurrent Neural Nets. 359 25.1 Introduction. 359 25.2 Recurrent
Layer. 361 25.3 Backpropagation through Time. 361 25.4 Stacking Recurrent Layers . 363 25.5 Long Short-Term Memory. 364 25.6 Concluding Remarks. 365
Contents 26 xiii Transformers. 367 26.1 Introduction. 367 26.2 A Limitation of CNNs: Independence between Far Apart Patches. 367 26.3 The Idea of Attention. 368 26.4 A New Data Type: Tokens. 368 26.5 Token Nets .371 26.6 The Attention Layer.371 26.7 The Full Transformer Architecture. 379 26.8 Permutation Equivariance.381 26.9 CNNs in Disguise.382 26.10 Masked Attention.
384 26.11 Positional Encodings. 385 26.12 Comparing Fully Connected, Convolutional,and Self-Attention Layers. 387 26.13 Concluding Remarks.388 VIII PROBABILISTIC MODELS OF IMAGES. 389 27 Statistical Image Models. 391 27.1 Introduction. 391 27.2 How Do We Tell Noise from Texture?.392 27.3 Independent Pixels. 394 27.4 Dead Leaves Model. 396 27.5 The Gaussian Model . 399 27.6 The Wavelet Marginal Model. 402 27.7 Nonparametric Markov Random Field
ImageModels. 408 27.8 Concluding Remarks. 410 28 Textures. 411 28.1 Introduction. 411 28.2 A Few Notes about Human Perception. 412 28.3 Heeger-Bergen Texture Analysis and Synthesis. 414 28.4 Efros-Leung Texture Analysis and SynthesisModel.417 28.5 Connection to Deep Generative Models. 419 28.6 Concluding Remarks. 420 29 Probabilistic Graphical Models. 421 29.1 Introduction. 421 29.2 Simple
Examples. 421 29.3 Directed Graphical Models. 425 29.4 Inference in Graphical Models. 425 29.5 Simple Example of Inference in a Graphical Model.426 29.6 Belief Propagation. 428 29.7 Loopy Belief Propagation. 434
Contents xiv 29.8 29.9 Relationship of Probabilistic Graphical Models to Neural Networks. 437 Concluding Remarks. 438 IX GENERATIVE IMAGE MODELS AND REPRESENTATION LEARNING. 439 30 Representation Learning. 441 30.1 Introduction. 441 30.2 Problem Setting.441 30.3 What Makes for a Good Representation? . 442 30.4 Autoencoders. 444 30.5 Predictive Encodings. 447 30.6 Self-Supervised Learning . 449 30.7 Imputation. 449 30.8 Abstract Pretext
Tasks. 450 30.9 Clustering. 451 30.10 Contrastive Learning. 455 30.11 Concluding Remarks. 460 31 Perceptual Grouping . 461 31.1 Introduction. 461 31.2 Why Group?.462 31.3 Segments. 463 31.4 Edges, Boundaries, and Contours. 465 31.5 Layers . 466 31.6 Emergent Groups.467 31.7 Concluding
Remarks. 467 32 Generative Models. 469 32.1 Introduction. 469 32.2 Unconditional Generative Models. 470 32.3 Learning Generative Models.472 32.4 Density Models. 474 32.5 Energy-Based Models.475 32.6 Gaussian Density Models. 479 32.7 Autoregressive Density Models . ·. 480 32.8 Diffusion Models. 484 32.9 Generative Adversarial Networks. 487 32.10 Concluding
Remarks. 489 33 Generative Modeling Meets Representation Learning. 491 33.1 Introduction. 491 33.2 Latent Variables as Representations. 492 33.3 Technical Setting.492
Contents 33.4 33.5 33.6 33.7 XV Variational Autoencoders . 493 Do VAEs Learn Good Representations?. 503 Generative Adversarial Networks Are Representation Learners Too. 506 Concluding Remarks. 507 34 Conditional Generative Models. 509 34.1 Introduction.509 34.2 A Motivating Example: Image Colorization. 509 34.3 Conditional Generative Models Solve Multimodal Structured Prediction.513 34.4 A Tour of Popular Conditional Models. 514 34.5 Structured Prediction in Vision . . . 517 34.6 Image-to-Image Translation .517 34.7 Concluding
Remarks. 523 X CHALLENGES IN LEARNING-BASED VISION. 525 35 Data Bias and Shift. 527 35.1 Introduction. 527 35.2 Out-of-DistributionGeneralization. 529 35.3 A Toy Example. 530 35.4 Dataset Bias . 533 35.5 Sources of Bias. 534 35.6 Adversarial Shifts. 537 35.7 Concluding Remarks. 538 36 Training for Robustness and Generality. 539 36.1
Introduction. 539 36.2 Data Augmentation . 539 36.3 Adversarial Training . 542 36.4 Toward General-PurposeVision Models. 543 36.5 Concluding Remarks. 544 37 Transfer Learning and Adaptation. 545 37.1 Introduction. 545 37.2 Problem Setting. 545 37.3 Finetuning. 546 37.4 Learning from a Teacher. 549 37.5
Prompting. 550 37.6 Domain Adaptation. 552 37.7 Generative Data. 553 37.8 Other Kinds of Knowledge that CanBe Transferred. 554 37.9 A Combinatorial Catalogof Transfer Learning Methods. 554
xvi Contents 37.10 Sequence Models from the Lens of Adaptation . 555 37.11 Concluding Remarks.555 XI UNDERSTANDING GEOMETRY. 557 38 Representing Images and Geometry. 559 38.1 Introduction. 559 38.2 Homogeneous and Heteregenous Coordinates. 560 38.3 2D Image Transformations. 561 38.4 Lines and Planes in Homogeneous Coordinates.567 38.5 Image Warping. 568 38.6 Implicit Image Representations .569 38.7 Concluding Remarks. 571 39 Camera Modeling and
Calibration. 573 39.1 Introduction. 573 39.2 3D Camera Projections in Homogeneous Coordinates.574 39.3 Camera-Intrinsic Parameters.576 39.4 Camera-Extrinsic Parameters. 580 39.5 Full Camera Model . 582 39.6 A Few Concrete Examples .583 39.7 Camera Calibration . 588 39.8 Concluding Remarks.594 40 Stereo Vision .595 40.1 Introduction. 595 40.2 Stereo
Cues. 596 40.3 Model-Based Methods. 599 40.4 Learning-Based Methods. 609 40.5 Evaluation. 611 40.6 Concluding Remarks. 611 41 Homographies.613 41.1 Introduction. 613 41.2 Homography. 614 41.3 Creating Image Panoramas.618 41.4 Concluding Remarks. 621 42 Single View Metrology. 623 42.1
Introduction. 623 42.2 A Few Notes about Perception of Depth by Humans. 624 42.3 Linear Perspective. 626 42.4 Measuring Heights Using Parallel Lines.631 42.5 3D Metrology from a Single View. 637
Contents xvii 42.6 Camera Calibration from Vanishing Points. 641 42.7 Concluding Remarks. 642 43 Learning to Estimate Depth from a Single Image . 643 43.1 Introduction.643 43.2 Monocular Depth Cues .643 43.3 3D Representations .644 43.4 Supervised Methods for Depth from a Single Image. 645 43.5 Unsupervised Methods for Depth from a Single Image. 648 43.6 Concluding Remarks. 651 44 Multiview Geometry and Structure from Motion .653 44.1 Introduction. 653 44.2 Structure from Motion. 653 44.3 Sparse
SFM. 655 44.4 Concluding Remarks. 662 45 Radiance Fields. 665 45.1 Introduction. . 665 45.2 What is a Radiance Field?.666 45.3 Representing Radiance Fields With Parameterized Functions. 668 45.4 Rendering Radiance Fields. 670 45.5 Fitting a Radiance Field to Explain a Scene.674 45.6 Beyond Radiance Fields: The Rendering Equation . 677 45.7 Concluding Remarks. 678 XII UNDERSTANDING MOTION . 679 46 Motion Estimation
. 681 46.1 Introduction. 681 46.2 Motion Perception in the Human Visual System . 682 46.3 Matching-Based Motion Estimation. 684 46.4 Does the Human Visual System Use Matching to Estimate Motion?. 687 46.5 Concluding Remarks. 689 47 3D Motion and Its 2D Projection. 691 47.1 Introduction. 691 47.2 3D Motion and Its 2D Projection. 691 47.3 Concluding Remarks. 699 48 Optical Flow Estimation. 701 48.1
Introduction. 701 48.2 2D Motion Field and Optical Flow. 701
Contents xviii 48.3 48.4 49 Model-Based Approaches. 703 Concluding Remarks. 710 Learning to Estimate Motion. 711 49.1 Introduction. 711 49.2 Learning-Based Approaches. 711 49.3 Concluding Remarks.714 XIII UNDERSTANDING VISION WITH LANGUAGE. 715 50 Object Recognition. 717 50.1 Introduction. 717 50.2 A Few Notes About Object Recognition in Humans. 718 50.3 Image Classification.721 50.4 Object
Localization.727 50.5 Class Segmentation . 736 50.6 Instance Segmentation.737 50.7 Concluding Remarks. 739 51 Vision and Language . 741 51.1 Introduction. 741 51.2 Background: Representing Text as Tokens.741 51.3 Learning Visual Representations from Language Supervision. 743 51.4 Translating between Images and Text . 748 51.5 Text as a Visual Representation .752 51.6 Visual Question Answering.753 51.7 Concluding
Remarks.753 XIV ON RESEARCH, WRITING AND SPEAKING. 755 52 How to Do Research. 757 52.1 Introduction. 757 52.2 Research Advice.757 52.3 Concluding Remarks.760 53 How to Write Papers.761 53.1 Introduction. 761 53.2 Organization.762 53.3 General Writing Tips. 764 53.4 Concluding Remarks. 768 54 How to Give
Talks. 769 54.1 Introduction.769 54.2 Very Short Talks (2 - 10 minutes) . 769
Contents 54.3 54.4 54.5 54.6 54.7 54.8 XIX Preparation . 770 Nervousness . 771 Your Distracted Audience. 771 Ways to Engage the Audience. 771 Show Yourself to the Audience. 772 Concluding Remarks. 773 XV CLOSING REMARKS. i. 775 55 A Simple Vision System—Revisited . 777 55.1 Introduction. 777 55.2 A Simple Neural Network. 777 55.3 From 2D Images to 3D
.779 55.4 Large Language Model-based Scene Understanding. 781 55.5 Unsolved Solved Computer Vision Problems. 782 55.6 Concluding Remarks. 783 Bibliography. 785 Index. 807 |
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| id | DE-604.BV049610677 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:36:10Z |
| indexdate | 2025-02-24T09:01:07Z |
| institution | BVB |
| isbn | 9780262048972 0262048973 |
| language | English |
| lccn | 2023024588 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034954899 |
| oclc_num | 1425455659 |
| open_access_boolean | |
| owner | DE-83 DE-29T DE-473 DE-BY-UBG DE-1051 DE-1050 DE-860 DE-4325 |
| owner_facet | DE-83 DE-29T DE-473 DE-BY-UBG DE-1051 DE-1050 DE-860 DE-4325 |
| physical | xxviii, 810 Seiten Illustrationen, Diagramme |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | The MIT Press |
| record_format | marc |
| series2 | Adaptive computation and machine learning series |
| spelling | Torralba, Antonio Verfasser (DE-588)1266670807 aut Foundations of computer vision Antonio Torralba, Phillip Isola,and William T. Freeman Cambridge, Massachusetts ; London The MIT Press [2024] © 2024 xxviii, 810 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index "An up-to-date computer vision textbook incorporating the latest deep learning advances that have revolutionized the field over the last decade"-- Computer vision DLC Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Bildverarbeitung (DE-588)4006684-8 s DE-604 Isola, Phillip Verfasser (DE-588)1330984862 aut Freeman, William T. Verfasser (DE-588)1330984935 aut Erscheint auch als Online-Ausgabe, ePub, PDF 9780262378666 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034954899&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Torralba, Antonio Isola, Phillip Freeman, William T. Foundations of computer vision Computer vision DLC Bildverarbeitung (DE-588)4006684-8 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)4006684-8 (DE-588)4129594-8 |
| title | Foundations of computer vision |
| title_auth | Foundations of computer vision |
| title_exact_search | Foundations of computer vision |
| title_exact_search_txtP | Foundations of computer vision |
| title_full | Foundations of computer vision Antonio Torralba, Phillip Isola,and William T. Freeman |
| title_fullStr | Foundations of computer vision Antonio Torralba, Phillip Isola,and William T. Freeman |
| title_full_unstemmed | Foundations of computer vision Antonio Torralba, Phillip Isola,and William T. Freeman |
| title_short | Foundations of computer vision |
| title_sort | foundations of computer vision |
| topic | Computer vision DLC Bildverarbeitung (DE-588)4006684-8 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Computer vision Bildverarbeitung Maschinelles Sehen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034954899&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT torralbaantonio foundationsofcomputervision AT isolaphillip foundationsofcomputervision AT freemanwilliamt foundationsofcomputervision |