Handbook Of Research On Computer Vision And Image Processing In The Deep Learning Era

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Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era

Srinivasan, A.
Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era Book PDF

Author : Srinivasan, A.
Publisher : IGI Global
Page : 467 pages
File Size : 48,6 Mb
Release : 2022-10-21
Category : Computers
ISBN : 9781799888949

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Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era by Srinivasan, A. Pdf

In recent decades, there has been an increasing interest in using machine learning and, in the last few years, deep learning methods combined with other vision and image processing techniques to create systems that solve vision problems in different fields. There is a need for academicians, developers, and industry-related researchers to present, share, and explore traditional and new areas of computer vision, machine learning, deep learning, and their combinations to solve problems. The Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era is designed to serve researchers and developers by sharing original, innovative, and state-of-the-art algorithms and architectures for applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, and more. It integrates the knowledge of the growing international community of researchers working on the application of machine learning and deep learning methods in vision and robotics. Covering topics such as brain tumor detection, heart disease prediction, and medical image detection, this premier reference source is an exceptional resource for medical professionals, faculty and students of higher education, business leaders and managers, librarians, government officials, researchers, and academicians.

Computer Vision and Image Processing in the Deep Learning Era

A. Srinivasan
Computer Vision and Image Processing in the Deep Learning Era Book PDF

Author : A. Srinivasan
Publisher : Engineering Science Reference
Page : 300 pages
File Size : 44,5 Mb
Release : 2022-06-24
Category : Electronic
ISBN : 1799888924

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Computer Vision and Image Processing in the Deep Learning Era by A. Srinivasan Pdf

In recent decades, there has been an increasing interest in using machine learning and, in the last few years, deep learning methods combined with other vision and image processing techniques to create systems that solve vision problems in different fields. There is a need for academicians, developers, and industry-related researchers to present, share, and explore traditional and new areas of computer vision, machine learning, deep learning, and their combinations to solve problems. Computer Vision and Image Processing in the Deep Learning Era is designed to serve researchers and developers by sharing original, innovative, and state-of-the-art algorithms and architectures for applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, and more. It integrates the knowledge of the growing international community of researchers working on the application of machine learning and deep learning methods in vision and robotics. Covering topics such as brain tumor detection, heart disease prediction, and medical image detection, this premier reference source is an exceptional resource for medical professionals, faculty and students of higher education, business leaders and managers, librarians, government officials, researchers, and academicians.

Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments

Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nersisson, Ruban
Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments Book PDF

Author : Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nersisson, Ruban
Publisher : IGI Global
Page : 381 pages
File Size : 51,7 Mb
Release : 2020-12-25
Category : Computers
ISBN : 9781799866923

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Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments by Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nersisson, Ruban Pdf

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Handbook of Image Processing and Computer Vision

Arcangelo Distante,Cosimo Distante
Handbook of Image Processing and Computer Vision Book PDF

Author : Arcangelo Distante,Cosimo Distante
Publisher : Springer
Page : 0 pages
File Size : 44,5 Mb
Release : 2021-05-30
Category : Computers
ISBN : 303042376X

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Handbook of Image Processing and Computer Vision by Arcangelo Distante,Cosimo Distante Pdf

Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 2 (From Image to Pattern) examines image transforms, image restoration, and image segmentation. Topics and features: • Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy • Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal • Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image • Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration • Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest • Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration • Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence. Dr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.

A Guide to Convolutional Neural Networks for Computer Vision

Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun
A Guide to Convolutional Neural Networks for Computer Vision Book PDF

Author : Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun
Publisher : Morgan & Claypool Publishers
Page : 303 pages
File Size : 45,7 Mb
Release : 2018-02-13
Category : Computers
ISBN : 9781681732824

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A Guide to Convolutional Neural Networks for Computer Vision by Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun Pdf

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Handbook of Image Processing and Computer Vision

Arcangelo Distante,Cosimo Distante
Handbook of Image Processing and Computer Vision Book PDF

Author : Arcangelo Distante,Cosimo Distante
Publisher : Springer Nature
Page : 507 pages
File Size : 50,6 Mb
Release : 2020-05-28
Category : Computers
ISBN : 9783030381486

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Handbook of Image Processing and Computer Vision by Arcangelo Distante,Cosimo Distante Pdf

Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 1 (From Energy to Image) examines the formation, properties, and enhancement of a digital image. Topics and features: • Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy • Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal • Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image • Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration • Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest • Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration • Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence. Dr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.

Domain Adaptation in Computer Vision with Deep Learning

Hemanth Venkateswara,Sethuraman Panchanathan
Domain Adaptation in Computer Vision with Deep Learning Book PDF

Author : Hemanth Venkateswara,Sethuraman Panchanathan
Publisher : Springer Nature
Page : 256 pages
File Size : 44,6 Mb
Release : 2020-08-18
Category : Computers
ISBN : 9783030455293

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Domain Adaptation in Computer Vision with Deep Learning by Hemanth Venkateswara,Sethuraman Panchanathan Pdf

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

Deep Learning in Object Detection and Recognition

Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Deep Learning in Object Detection and Recognition Book PDF

Author : Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Publisher : Springer
Page : 0 pages
File Size : 41,8 Mb
Release : 2020-11-27
Category : Computers
ISBN : 9811506515

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Deep Learning in Object Detection and Recognition by Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng Pdf

This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Deep Learning for Medical Image Analysis

S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Deep Learning for Medical Image Analysis Book PDF

Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Page : 458 pages
File Size : 46,9 Mb
Release : 2017-01-18
Category : Computers
ISBN : 9780128104095

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Deep Learning for Medical Image Analysis by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen Pdf

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Deep Learning in Computer Vision

Mahmoud Hassaballah,Ali Ismail Awad
Deep Learning in Computer Vision Book PDF

Author : Mahmoud Hassaballah,Ali Ismail Awad
Publisher : CRC Press
Page : 322 pages
File Size : 42,7 Mb
Release : 2020-03-23
Category : Computers
ISBN : 9781351003810

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Deep Learning in Computer Vision by Mahmoud Hassaballah,Ali Ismail Awad Pdf

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Handbook of Image Processing and Computer Vision

Arcangelo Distante,Cosimo Distante
Handbook of Image Processing and Computer Vision Book PDF

Author : Arcangelo Distante,Cosimo Distante
Publisher : Springer
Page : 0 pages
File Size : 52,5 Mb
Release : 2021-06-09
Category : Computers
ISBN : 3030423808

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Handbook of Image Processing and Computer Vision by Arcangelo Distante,Cosimo Distante Pdf

Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 3 (From Pattern to Object) examines object recognition, neural networks, motion analysis, and 3D reconstruction of a scene. Topics and features: • Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy • Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal • Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image • Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration • Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest • Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration • Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence. Dr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.

Computer Vision and Recognition Systems

Chiranji Lal Chowdhary,G. Thippa Reddy,B. D. Parameshachari
Computer Vision and Recognition Systems Book PDF

Author : Chiranji Lal Chowdhary,G. Thippa Reddy,B. D. Parameshachari
Publisher : CRC Press
Page : 285 pages
File Size : 50,9 Mb
Release : 2022-03-10
Category : Science
ISBN : 9781000401028

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Computer Vision and Recognition Systems by Chiranji Lal Chowdhary,G. Thippa Reddy,B. D. Parameshachari Pdf

This cutting-edge volume focuses on how artificial intelligence can be used to give computers the ability to imitate human sight. With contributions from researchers in diverse countries, including Thailand, Spain, Japan, Turkey, Australia, and India, the book explains the essential modules that are necessary for comprehending artificial intelligence experiences to provide machines with the power of vision. The volume also presents innovative research developments, applications, and current trends in the field. The chapters cover such topics as visual quality improvement, Parkinson’s disease diagnosis, hypertensive retinopathy detection through retinal fundus, big image data processing, N-grams for image classification, medical brain images, chatbot applications, credit score improvisation, vision-based vehicle lane detection, damaged vehicle parts recognition, partial image encryption of medical images, and image synthesis. The chapter authors show different approaches to computer vision, image processing, and frameworks for machine learning to build automated and stable applications. Deep learning is included for making immersive application-based systems, pattern recognition, and biometric systems. The book also considers efficiency and comparison at various levels of using algorithms for real-time applications, processes, and analysis.

Mastering Computer Vision with TensorFlow 2.x

Krishnendu Kar
Mastering Computer Vision with TensorFlow 2 x Book PDF

Author : Krishnendu Kar
Publisher : Packt Publishing Ltd
Page : 419 pages
File Size : 46,8 Mb
Release : 2020-05-15
Category : Computers
ISBN : 9781838826932

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Mastering Computer Vision with TensorFlow 2.x by Krishnendu Kar Pdf

Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learnExplore methods of feature extraction and image retrieval and visualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-world scenariosBuild neural networks or adjust parameters to optimize the performance of modelsUnderstand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpaintingEvaluate your model and optimize and integrate it into your application to operate at scaleGet up to speed with techniques for performing manual and automated image annotationWho this book is for This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

Handbook of Image Processing and Computer Vision

Arcangelo Distante,Cosimo Distante
Handbook of Image Processing and Computer Vision Book PDF

Author : Arcangelo Distante,Cosimo Distante
Publisher : Springer
Page : 1617 pages
File Size : 44,7 Mb
Release : 2020-10-16
Category : Computers
ISBN : 303042507X

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Handbook of Image Processing and Computer Vision by Arcangelo Distante,Cosimo Distante Pdf

Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Topics and features: describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy; covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal; discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image; reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration; presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest; examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration; provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks. This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence.

Visual Domain Adaptation in the Deep Learning Era

Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Visual Domain Adaptation in the Deep Learning Era Book PDF

Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Morgan & Claypool Publishers
Page : 190 pages
File Size : 47,7 Mb
Release : 2022-04-05
Category : Computers
ISBN : 9781636393421

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Visual Domain Adaptation in the Deep Learning Era by Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi Pdf

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance/b>. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.