Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods,

emphasizing machine learning 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, 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 vision methods that were

created by leaders in the field

. Illustrates principles with modern, real-world applications

. Suitable for self-learning or as a text for graduate courses

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. The first, published in 1990, has been

widely used internationally for more than 25 years: in 2017 it came out in a fifth edition entitled Computer

Vision, Principles, Algorithms, Applications, Learning. 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.

Matthew Turk is the President of the Toyota Technological Institute at Chicago (TTIC) and an Emeritus

Professor at the University of California, Santa Barbara. He has received several best paper awards and was named

a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013, the International Association

for Pattern Recognition (IAPR) in 2014, and the Association for Computing Machinery (ACM) in 2020, for

contributions to computer vision, face recognition, and multimodal interaction.