Advanced Methods and Deep Learning in Computer Vision

Erscheinungsjahr 2021
ISBN: 978-0-12-822109-9
Verlag: William Andrew Publishing

Buch, Englisch, 582 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1200 g

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.

Autoren/Hrsg.

Davies, E. R.

Turk, Matthew

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

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

Über Autor(innen)

Turk, Matthew
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.

Davies, E. R.
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.