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.
1. Advanced Methods and Deep Learning in Computer Vision - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. List of contributors - p. Page vi 9. About the editors - p. Page vii 10. Preface - p. Page viii 11. List of Illustrations - p. Page xiv 12. List of Tables - p. Page xv 13. Chapter 1 : The dramatically changing face of computer vision - p. Page xv 14. Chapter 2 : Advanced methods for robust object detection - p. Page 87 15. Chapter 3 : Learning with limited supervision: Static and dynamic tasks - p. Page 116 16. Chapter 4 : Efficient methods for deep learning☆ - p. Page 153 17. Chapter 5 : Deep conditional image generation: Towards controllable visual pattern modeling - p. Page 185 18. Chapter 6 : Deep face recognition using full and partial face images - p. Page 216 19. Chapter 7 : Unsupervised domain adaptation using shallow and deep representations - p. Page 240 20. Chapter 8 : Domain adaptation and continual learning in semantic segmentation - p. Page 270 21. Chapter 9 : Visual tracking: Tracking in scenes containing multiple moving objects - p. Page 299 22. Chapter 10 : Long-term deep object tracking - p. Page 332 23. Chapter 11 : Learning for action-based scene understanding - p. Page 368 24. Chapter 12 : Self-supervised temporal event segmentation inspired by cognitive theories - p. Page 399 25. Chapter 13 : Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems - p. Page 444 26. Chapter 14 : Deep plug-and-play and deep unfolding methods for image restoration - p. Page 477 27. Chapter 15 : Visual adversarial attacks and defenses - p. Page 505 28. Index - p. Page 538
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.
1. Advanced Methods and Deep Learning in Computer Vision - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. List of contributors - p. Page vi 9. About the editors - p. Page vii 10. Preface - p. Page viii 11. List of Illustrations - p. Page xiv 12. List of Tables - p. Page xv 13. Chapter 1 : The dramatically changing face of computer vision - p. Page xv 14. Chapter 2 : Advanced methods for robust object detection - p. Page 87 15. Chapter 3 : Learning with limited supervision: Static and dynamic tasks - p. Page 116 16. Chapter 4 : Efficient methods for deep learning☆ - p. Page 153 17. Chapter 5 : Deep conditional image generation: Towards controllable visual pattern modeling - p. Page 185 18. Chapter 6 : Deep face recognition using full and partial face images - p. Page 216 19. Chapter 7 : Unsupervised domain adaptation using shallow and deep representations - p. Page 240 20. Chapter 8 : Domain adaptation and continual learning in semantic segmentation - p. Page 270 21. Chapter 9 : Visual tracking: Tracking in scenes containing multiple moving objects - p. Page 299 22. Chapter 10 : Long-term deep object tracking - p. Page 332 23. Chapter 11 : Learning for action-based scene understanding - p. Page 368 24. Chapter 12 : Self-supervised temporal event segmentation inspired by cognitive theories - p. Page 399 25. Chapter 13 : Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems - p. Page 444 26. Chapter 14 : Deep plug-and-play and deep unfolding methods for image restoration - p. Page 477 27. Chapter 15 : Visual adversarial attacks and defenses - p. Page 505 28. Index - p. Page 538
