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Best Computer Vision Books

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Best Computer Vision Books for beginners
Best Computer Vision Books

In this post, you will find the Best Computer Vision Books for Beginners & Advanced. These Computer Vision Books are written by experts and these books are used for reference, and also for beginners to learn new Computer vision tips and techniques with real-time practical problems.

9+ Best Computer Vision Books for Beginners to Advance in 2022

Here I have gathered a list of 9+ best Computer Vision Books based on their usage, Concepts, Examples etc.

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques.

This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.

Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You’ll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.

You’ll learn how to:

  • Design ML architecture for computer vision tasks
  • Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
  • Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
  • Preprocess images for data augmentation and to support learnability
  • Incorporate explainability and responsible AI best practices
  • Deploy image models as web services or on edge devices
  • Monitor and manage ML models

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Computer Vision: Models, Learning, and Inference

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying them.

It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data.

With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision.

  • Covers cutting-edge techniques, including graph cuts, machine learning, and multiple view geometry.
  • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition, and object tracking.
  • More than 70 algorithms are described in sufficient detail to implement.
  • More than 350 full-color illustrations amplify the text.
  • The treatment is self-contained, including all of the background mathematics.

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Computer Vision: Algorithms and Applications:

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques.

Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field,

Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries.

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Computer Vision: A Modern Approach (2nd Edition)

Computer Vision: A Modern Approach, 2e, is appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.

This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field.

This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

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Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.

You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work.

As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform.

The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks.

You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you’ll move your NN model to production on the AWS Cloud.

By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.

What you will learn

  • Train a NN from scratch with NumPy and PyTorch
  • Implement 2D and 3D multi-object detection and segmentation
  • Generate digits and DeepFakes with autoencoders and advanced GANs
  • Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN
  • Combine CV with NLP to perform OCR, image captioning, and object detection
  • Combine CV with reinforcement learning to build agents that play pong and self-drive a car
  • Deploy a deep learning model on the AWS server using FastAPI and Docker
  • Implement over 35 NN architectures and common OpenCV utilities

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you’ll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

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TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges

Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.

The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x’s key features, such as the Keras and tf.data.Dataset APIs. You’ll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection.

The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO).

Moving on, you’ll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you’ll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.

By the end of this TensorFlow book, you’ll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.

What you will learn

  • Understand how to detect objects using state-of-the-art models such as YOLOv3
  • Use AutoML to predict gender and age from images
  • Segment images using different approaches such as FCNs and generative models
  • Learn how to improve your network’s performance using rank-N accuracy, label smoothing, and test time augmentation
  • Enable machines to recognize people’s emotions in videos and real-time streams
  • Access and reuse advanced TensorFlow Hub models to perform image classification and object detection
  • Generate captions for images using CNNs and RNNs

Who this book is for

This book is for computer vision developers and engineers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.

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Photogrammetric Computer Vision: Statistics, Geometry, Orientation and Reconstruction (Geometry and Computing)

This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision.

Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology.

Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their rela­tions, tools that are useful also in the context of uncertain reasoning in point clouds.

Part III is de­voted to modelling the geometry of single and multiple cameras, addressing calibration and orienta­tion, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features.

The authors provide algorithms for various geometric computa­tion problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exer­cises, and they are supported by an appendix that explains the basic mathematical notation and a de­tailed index.

The book can serve as the basis for undergraduate and graduate courses in photogrammetry, com­puter vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods.

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Concise Computer Vision: An Introduction into Theory and Algorithms (Undergraduate Topics in Computer Science)

This textbook provides an accessible general introduction to the essential topics in computer vision. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter.

Features:

  • provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image.
  • explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image.
  • introduces optic flow for representing dense motion and various topics in sparse motion analysis
  • describes special approaches for image binarization and segmentation of still images or video frames; examines the basic components of a computer vision system
  • reviews different techniques for vision-based 3D shape reconstruction; includes a discussion of stereo matchers and the phase-congruency model for image features
  • presents an introduction into classification and learning.

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Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

Deep learning (DL) is a key component of today’s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL.

Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others–including those with no prior machine learning or statistics experience.

After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer.

He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.

Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.

  • Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation
  • See how DL frameworks make it easier to develop more complicated and useful neural networks
  • Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
  • Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences
  • Master NLP with sequence-to-sequence networks and the Transformer architecture
  • Build applications for natural language translation and image captioning

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Computer Vision: Principles, Algorithms, Applications, Learning

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject.

  • Three new chapters on Machine Learning emphasise the way the subject has been developing:
  • Two chapters cover Basic Classification Concepts and Probabilistic Models.
  • The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition.
  • A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning Books and gives practical demonstrations of its application.
  • In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics.
  • Examples and applications―including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians―give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation.
  • Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples.
  • The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject.
  • Tailored programming examples―code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)

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Advanced Methods and Deep Learning in Computer Vision (Computer Vision and Pattern Recognition)

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.

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Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3.

You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds.

From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition.

You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed.

Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person’s gender and age.

By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.

What you will learn

  • Install and familiarize yourself with OpenCV 4’s Python 3 bindings
  • Understand image processing and video analysis basics
  • Use a depth camera to distinguish foreground and background regions
  • Detect and identify objects, and track their motion in videos
  • Train and use your own models to match images and classify objects
  • Detect and recognize faces, and classify their gender and age
  • Build an augmented reality application to track an image in 3D
  • Work with machine learning models, including SVMs, artificial neural networks (ANNs), and deep neural networks (DNNs)

Who this book is for

If you are interested in learning computer vision, machine learning, and OpenCV in the context of practical real-world applications, then this book is for you. This OpenCV book will also be useful for anyone getting started with computer vision as well as experts who want to stay up-to-date with OpenCV 4 and Python 3. Although no prior knowledge of image processing, computer vision or machine learning is required, familiarity with basic Python programming is a must.

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Conclusion:

In this post, you have found the Best Computer Vision Books. If you think that I miss your favourite Computer Vision Books, then please comment in the comment section. As soon as possible I will add that Best Computer Vision books to the list.

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