Advanced Methods and Deep Learning in Computer Vision

E. R. Davies, Matthew Turk

• Publisher: Academic Press
• Year: 2021
• Print ISBN: 9780128221099
• eBook ISBN: 9780128221495
• Topic: Technology & Engineering
• Subtopic: Computer Vision & Pattern Recognition
• Index: Technology & Engineering

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. - 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

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Dedication
• Contents
• List of contributors
• About the editors
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : The dramatically changing face of computer vision
• Chapter 2 : Advanced methods for robust object detection
• Chapter 3 : Learning with limited supervision: Static and dynamic tasks
• Chapter 4 : Efficient methods for deep learning☆
• Chapter 5 : Deep conditional image generation: Towards controllable visual pattern modeling
• Chapter 6 : Deep face recognition using full and partial face images
• Chapter 7 : Unsupervised domain adaptation using shallow and deep representations
• Chapter 8 : Domain adaptation and continual learning in semantic segmentation
• Chapter 9 : Visual tracking: Tracking in scenes containing multiple moving objects
• Chapter 10 : Long-term deep object tracking
• Chapter 11 : Learning for action-based scene understanding
• Chapter 12 : Self-supervised temporal event segmentation inspired by cognitive theories
• Chapter 13 : Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems
• Chapter 14 : Deep plug-and-play and deep unfolding methods for image restoration
• Chapter 15 : Visual adversarial attacks and defenses
• Index
• A