Advanced Methods and Deep Learning in Computer Vision
- Length: 582 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2021-11-26
- ISBN-10: 0128221097
- ISBN-13: 9780128221099
- Sales Rank: #4481436 (See Top 100 Books)
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.
Cover image Title page Table of Contents Copyright Dedication List of contributors About the editors Preface Chapter 1: The dramatically changing face of computer vision Abstract Acknowledgements 1.1. Introduction – computer vision and its origins 1.2. Part A – Understanding low-level image processing operators 1.3. Part B – 2-D object location and recognition 1.4. Part C – 3-D object location and the importance of invariance 1.5. Part D – Tracking moving objects 1.6. Part E – Texture analysis 1.7. Part F – From artificial neural networks to deep learning methods 1.8. Part G – Summary References Chapter 2: Advanced methods for robust object detection Abstract 2.1. Introduction 2.2. Preliminaries 2.3. R-CNN 2.4. SPP-Net 2.5. Fast R-CNN 2.6. Faster R-CNN 2.7. Cascade R-CNN 2.8. Multiscale feature representation 2.9. YOLO 2.10. SSD 2.11. RetinaNet 2.12. Detection performances 2.13. Conclusion References Chapter 3: Learning with limited supervision Abstract Acknowledgements 3.1. Introduction 3.2. Context-aware active learning 3.3. Weakly supervised event localization 3.4. Domain adaptation of semantic segmentation using weak labels 3.5. Weakly-supervised reinforcement learning for dynamical tasks 3.6. Conclusions References Chapter 4: Efficient methods for deep learning Abstract 4.1. Model compression 4.2. Efficient neural network architectures 4.3. Conclusion References Chapter 5: Deep conditional image generation Abstract 5.1. Introduction 5.2. Visual pattern learning: a brief review 5.3. Classical generative models 5.4. Deep generative models 5.5. Deep conditional image generation 5.6. Disentanglement for controllable synthesis 5.7. Conclusion and discussions References Chapter 6: Deep face recognition using full and partial face images Abstract 6.1. Introduction 6.2. Components of deep face recognition 6.3. Face recognition using full face images 6.4. Deep face recognition using partial face data 6.5. Specific model training for full and partial faces 6.6. Discussion and conclusions References Chapter 7: Unsupervised domain adaptation using shallow and deep representations Abstract 7.1. Introduction 7.2. Unsupervised domain adaptation using manifolds 7.3. Unsupervised domain adaptation using dictionaries 7.4. Unsupervised domain adaptation using deep networks 7.5. Summary References Chapter 8: Domain adaptation and continual learning in semantic segmentation Abstract Acknowledgement 8.1. Introduction 8.2. Unsupervised domain adaptation 8.3. Continual learning 8.4. Conclusion References Chapter 9: Visual tracking Abstract Acknowledgement 9.1. Introduction 9.2. Template-based methods 9.3. Online-learning-based methods 9.4. Deep learning-based methods 9.5. The transition from tracking to segmentation 9.6. Conclusions References Chapter 10: Long-term deep object tracking Abstract 10.1. Introduction 10.2. Short-term visual object tracking 10.3. Long-term visual object tracking 10.4. Discussion References Chapter 11: Learning for action-based scene understanding Abstract Acknowledgement 11.1. Introduction 11.2. Affordances of objects 11.3. Functional parsing of manipulation actions 11.4. Functional scene understanding through deep learning with language and vision 11.5. Future directions 11.6. Conclusions References Chapter 12: Self-supervised temporal event segmentation inspired by cognitive theories Abstract Acknowledgements 12.1. Introduction 12.2. The event segmentation theory from cognitive science 12.3. Version 1: single-pass temporal segmentation using prediction 12.4. Version 2: segmentation using attention-based event models 12.5. Version 3: spatio-temporal localization using prediction loss map 12.6. Other event segmentation approaches in computer vision 12.7. Conclusions References Chapter 13: Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems Abstract 13.1. Introduction 13.2. Base concepts and state of the art 13.3. Framework for computing anomaly in self-aware systems 13.4. Case study results: anomaly detection on multisensory data from a self-aware vehicle 13.5. Conclusions References Chapter 14: Deep plug-and-play and deep unfolding methods for image restoration Abstract Acknowledgements 14.1. Introduction 14.2. Half quadratic splitting (HQS) algorithm 14.3. Deep plug-and-play image restoration 14.4. Deep unfolding image restoration 14.5. Experiments 14.6. Discussion and conclusions References Chapter 15: Visual adversarial attacks and defenses Abstract Acknowledgement 15.1. Introduction 15.2. Problem definition 15.3. Properties of an adversarial attack 15.4. Types of perturbations 15.5. Attack scenarios 15.6. Image processing 15.7. Image classification 15.8. Semantic segmentation and object detection 15.9. Object tracking 15.10. Video classification 15.11. Defenses against adversarial attacks 15.12. Conclusions References Index
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