Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision

1st Edition - November 9, 2021
This is the Latest Edition
  • Editors: E. R. Davies, Matthew Turk
  • eBook ISBN: 9780128221495
  • Paperback ISBN: 9780128221099

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Description

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.  

Key Features

  • Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field
  • Illustrates principles with modern, real-world applications
  • Suitable for self-learning or as a text for graduate courses

Readership

Researchers, industry practitioners, and graduate students in computer vision and medical imaging

Table of Contents

  • List of contributors xi

    About the editors xiii

    Preface xv

    1. The dramatically changing face of computer vision

    E.R. DAVIES

    1.1 Introduction – computer vision and its origins 1

    1.2 Part A – Understanding low-level image processing operators 4

    1.3 Part B – 2-D object location and recognition 15

    1.4 Part C – 3-D object location and the importance of invariance 29

    1.5 Part D – Tracking moving objects 55

    1.6 Part E – Texture analysis 61

    1.7 Part F – From artificial neural networks to deep learning methods 68

    1.8 Part G – Summary 86

    References 87

    2. Advanced methods for robust object detection

    ZHAOWEI CAI AND NUNO VASCONCELOS

    2.1 Introduction 93

    2.2 Preliminaries 95

    2.3 R-CNN 96

    2.4 SPP-Net 97

    2.5 Fast R-CNN 98

    2.6 Faster R-CNN 101

    2.7 Cascade R-CNN 103

    2.8 Multiscale feature representation 106

    2.9 YOLO 110

    2.10 SSD 112

    2.11 RetinaNet 113

    2.12 Detection performances 115

    2.13 Conclusion 115

    References 116

    3. Learning with limited supervision

    SUJOY PAUL AND AMIT K. ROY-CHOWDHURY

    3.1 Introduction 119

    3.2 Context-aware active learning 120

    3.3 Weakly supervised event localization 129

    3.4 Domain adaptation of semantic segmentation using weak labels 137

    3.5 Weakly-supervised reinforcement learning for dynamical tasks 144

    3.6 Conclusions 151

    References 153

    4. Efficient methods for deep learning

    HAN CAI, JI LIN, AND SONG HAN

    4.1 Model compression 159

    4.2 Efficient neural network architectures 170

    4.3 Conclusion 185

    References 185

    5. Deep conditional image generation

    GANG HUA AND DONGDONG CHEN

    5.1 Introduction 191

    5.2 Visual pattern learning: a brief review 194

    5.3 Classical generative models 195

    5.4 Deep generative models 197

    5.5 Deep conditional image generation 200

    5.6 Disentanglement for controllable synthesis 201

    5.7 Conclusion and discussions 216

    References 216

    6. Deep face recognition using full and partial face images

    HASSAN UGAIL

    6.1 Introduction 221

    6.2 Components of deep face recognition 227

    6.3 Face recognition using full face images 231

    6.4 Deep face recognition using partial face data 233

    6.5 Specific model training for full and partial faces 237

    6.6 Discussion and conclusions 239

    References 240

    7. Unsupervised domain adaptation using shallow and deep representations

    YOGESH BALAJI, HIEN NGUYEN, AND RAMA CHELLAPPA

    7.1 Introduction 243

    7.2 Unsupervised domain adaptation using manifolds 244

    7.3 Unsupervised domain adaptation using dictionaries 247

    7.4 Unsupervised domain adaptation using deep networks 258

    7.5 Summary 270

    References 270

    8. Domain adaptation and continual learning in semantic segmentation

    UMBERTO MICHIELI, MARCO TOLDO, AND PIETRO ZANUTTIGH

    8.1 Introduction 275

    8.2 Unsupervised domain adaptation 277

    8.3 Continual learning 291

    8.4 Conclusion 298

    References 299

    9. Visual tracking

    MICHAEL FELSBERG

    9.1 Introduction 305

    9.2 Template-based methods 308

    9.3 Online-learning-based methods 314

    9.4 Deep learning-based methods 323

    9.5 The transition from tracking to segmentation 327

    9.6 Conclusions 331

    References 332

    10. Long-term deep object tracking

    EFSTRATIOS GAVVES AND DEEPAK GUPTA

    10.1 Introduction 337

    10.2 Short-term visual object tracking 341

    10.3 Long-term visual object tracking 345

    10.4 Discussion 367

    References 368

    11. Learning for action-based scene understanding

    CORNELIA FERMÜLLER AND MICHAEL MAYNORD

    11.1 Introduction 373

    11.2 Affordances of objects 375

    11.3 Functional parsing of manipulation actions 383

    11.4 Functional scene understanding through deep learning with language and vision 390

    11.5 Future directions 397

    11.6 Conclusions 399

    References 399

    12. Self-supervised temporal event segmentation inspired by cognitive theories

    RAMY MOUNIR, SATHYANARAYANAN AAKUR, AND SUDEEP SARKAR

    12.1 Introduction 406

    12.2 The event segmentation theory from cognitive science 408

    12.3 Version 1: single-pass temporal segmentation using prediction 410

    12.4 Version 2: segmentation using attention-based event models 421

    12.5 Version 3: spatio-temporal localization using prediction loss map 428

    12.6 Other event segmentation approaches in computer vision 440

    12.7 Conclusions 443

    References 444

    13. Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware

    systems

    CARLO REGAZZONI, ALI KRAYANI, GIULIA SLAVIC, AND LUCIO MARCENARO

    13.1 Introduction 450

    13.2 Base concepts and state of the art 451

    13.3 Framework for computing anomaly in self-aware systems 458

    13.4 Case study results: anomaly detection on multisensory data from a self-aware vehicle 467

    13.5 Conclusions 476

    References 477

    14. Deep plug-and-play and deep unfolding methods for image restoration

    KAI ZHANG AND RADU TIMOFTE

    14.1 Introduction 481

    14.2 Half quadratic splitting (HQS) algorithm 484

    14.3 Deep plug-and-play image restoration 485

    14.4 Deep unfolding image restoration 492

    14.5 Experiments 495

    14.6 Discussion and conclusions 504

    References 505

    15. Visual adversarial attacks and defenses

    CHANGJAE OH, ALESSIO XOMPERO, AND ANDREA CAVALLARO

    15.1 Introduction 511

    15.2 Problem definition 512

    15.3 Properties of an adversarial attack 514

    15.4 Types of perturbations 515

    15.5 Attack scenarios 515

    15.6 Image processing 522

    15.7 Image classification 523

    15.8 Semantic segmentation and object detection 529

    15.9 Object tracking 529

    15.10 Video classification 531

    15.11 Defenses against adversarial attacks 533

    15.12 Conclusions 537

    References 538

    Index 545

Product details

  • No. of pages: 582
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: November 9, 2021
  • Imprint: Academic Press
  • eBook ISBN: 9780128221495
  • Paperback ISBN: 9780128221099
  • About the Editors

    E. R. Davies

    Roy Davies is Emeritus Professor of Machine Vision at Royal Holloway, University of London. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance, crime detection and neural networks. He has published more than 200 papers, and three books. Machine Vision: Theory, Algorithms, Practicalities (1990) has been widely used internationally for more than 25 years, and is now out in this much enhanced fifth edition. Roy holds a DSc at the University of London, and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition.

    Affiliations and Expertise

    Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK

    Matthew Turk

    Matthew Turk is a professor and department chair of the Department of Computer Science at the University of California, Santa Barbara, California. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013[1] for his contributions to computer vision and perceptual interfaces. Starting on July 1st, he will be the president of the Toyota Technological Institute at Chicago[2]. In 2014, Turk was named a Fellow of the International Association for Pattern Recognition (IAPR)[3] for his contributions to computer vision and vision based interaction.

    Affiliations and Expertise

    Professor and Department Chair, Department of Computer Science, University of California, Santa Barbara, CA, USA