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Advanced Methods and Deep Learning in Computer Vision
- 1st Edition - November 9, 2021
- Editors: E. R. Davies, Matthew Turk
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 1 0 9 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 1 4 9 - 5
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… Read more
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Request a sales quoteAdvanced 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.
- 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
Researchers, industry practitioners, and graduate students in computer vision and medical imaging
List of contributors
xiAbout the editors
xiiiPreface
xv1. 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- No. of pages: 582
- Language: English
- Edition: 1
- Published: November 9, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128221099
- eBook ISBN: 9780128221495
ED
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, UKMT
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, USARead Advanced Methods and Deep Learning in Computer Vision on ScienceDirect