Advanced Methods and Deep Learning in Computer Vision
- 13h 40m
- E. R. Davies, Matthew Turk
- Elsevier Science and Technology Books, Inc.
- 2021
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
About the Author
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.
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.
In this Book
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The Dramatically Changing Face of Computer Vision
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Advanced Methods for Robust Object Detection
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Learning with Limited Supervision—Static and Dynamic Tasks
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Efficient Methods for Deep Learning
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Deep Conditional Image Generation—Towards Controllable Visual Pattern Modeling
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Deep Face Recognition using Full and Partial Face Images
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Unsupervised Domain Adaptation using Shallow and Deep Representations
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Domain Adaptation and Continual Learning in Semantic Segmentation
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Visual Tracking—Tracking in Scenes Containing Multiple Moving Objects
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Long-Term Deep Object Tracking
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Learning for Action-Based Scene Understanding
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Self-Supervised Temporal Event Segmentation Inspired by Cognitive Theories
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Probabilistic Anomaly Detection Methods using Learned Models from Time-Series Data for Multimedia Self-Aware Systems
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Deep Plug-and-Play and Deep Unfolding Methods for Image Restoration
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Visual Adversarial Attacks and Defenses
