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Learn how to – Best Books To Learn OpenCV & Computer Vision in 2023

Learn how to – Best Books To Learn OpenCV & Computer Vision in 2023.

Artificial Intelligence is one field that is growing and increasing its boundaries with astonishing amounts of speed. With the globe always skewed towards more connectivity and the aggregation of petabytes of data, Computer Vision sets itself on a position that attracts a lot of opportunities. Be it in Agriculture, Industries, Education, or in other applicable areas, Computer vision cutting through every fabric that will usher in a new age of technological growth.

Borrowing the definition from IBM, Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.

1. Deep Learning for Vision Systems

The author of this book, Mohamed Elgendy, is the head of engineering at Synapse Technology, a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. He was previously an engineering manager at Amazon, where he developed and taught the deep learning for computer vision course at Amazon’s Machine Learning University. You can imagine his level of skills and experience that are shared in this resource he has personally penned.

Covering 480 pages, Deep Learning for Vision Systems answers how a computer learns to understand what it sees. Using only high school algebra, this book illuminates the concepts behind visual intuition. You will understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.

What you will find inside

  • Image classification and object detection
  • Advanced deep learning architectures
  • Transfer learning and generative adversarial networks
  • DeepDream and neural style transfer
  • Visual embeddings and image search

The author says that if you are an intermediate Python programmer or anyone with wild curiosity, this book will do you good. Shared below is one outstanding review:

“From text and object detection to DeepDream and facial recognition … this book is comprehensive, approachable, and relevant for modern applications of deep learning to computer vision systems!”
–Bojan Djurkovic, DigitalOcean

For ways through which you can find this resource, a link is provided below. Click it and you will land in Amazon where you can find more information including how to get it shipped to you.

2. Machine Learning Design Patterns

Computer Vision has deep roots in machine learning and it heavily depends on it. The three authors, Valliappa Lakshmanan, Sara Robinson, and Michael Munn share their hard-earned experience around the “why” that underlies the tips and tricks that experienced ML practitioners employ when applying machine learning to real-world problems.

In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

You will learn how to

  • Identify and mitigate common challenges when training, evaluating, and deploying ML models
  • Represent data for different ML model types, including embeddings, feature crosses, and more
  • Choose the right model type for specific problems
  • Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
  • Deploy scalable ML systems that you can retrain and update to reflect new data
  • Interpret model predictions for stakeholders and ensure models are treating users fairly

The authors assume that you have prior knowledge of machine learning and data processing. They advice that the resource is not a fundamental textbook on machine learning. Instead, this book is for you if you are a data scientist, data engineer, or ML engineer who is looking for a second book on practical machine learning.

Here is the magic link that will link you up with Amazon where you can peruse more and order a copy for your warm library where beautiful things come to life. Click below.

3. Concise Computer Vision: An introduction in Theory and Algorithms

Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, author, is a Professor at the Auckland University of Technology (AUT). He has authored numerous publications including the book “Computer Vision for Driver Assistance” (co-authored by Mahdi Rezaei) among multiple others like this one.

Features you will find theren

  • 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.

Dr. Reinhard has had lots of experience both in his career as well as his authorship. You will immediately get the force bwhind his intellect from the very first pages. Click below and get your interests served, and served well with a copy from Amazon.

4. Learning OpenCV 4 Computer Vision with Python 3

Joseph Howse and Joe Minichino exude expertise in their domains of influence and they have teamed up to give their audience this wonderful resource. The book will not only help those who are getting started with computer vision but also experts in the domain. They are keen in putting theory into practice by building apps with OpenCV 4 and Python 3.

To just scratch the surface, the reader will start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you will 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 will have opportunities for hands-on activities. Next, you will tackle two popular challenges: face detection and face recognition. You will also have the opportunity to 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. Pretty cute, right?

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)

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 having familiarity with basic Python programming and getting started with computer vision as well as experts who want to stay up-to-date with OpenCV 4 and Python 3. Click below to get it all in your hands from Amazon.

5. Computer Vision: Algorithms And Application

Dr. Richard Szeliski, the author, has a mind-blowing more than 40 years’ experience in computer vision research, most recently at Facebook and Microsoft Research, where he led the Computational Photography and Interactive Visual Media groups. He is currently an Affiliate Professor at the University of Washington where he co-developed (with Steve Seitz) the widely adopted computer vision curriculum on which this book is based.

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
  • Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality
  • Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
  • Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade
  • Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software.

For those in college, this is most suitable for you because the author targets upper-level undergraduate or graduate-level course in computer science or engineering. The textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision. It is awesome. Click below to get to grips with what is in store from Amazon store.

6. Computer Vision Metrics: Survey, Taxonomy, and Analysis

We will give you a reason to buy this book. The author, Scott Krig, is a pioneer in computer imaging, computer vision, and graphics visualization. He founded Krig Research in 1988 (krigresearch.com), providing the world’s first imaging and vision systems based on high-performance engineering workstations, super-computers, and dedicated imaging hardware, serving customers worldwide in 25 countries. Scott has provided imaging and vision solutions around the globe, and has worked closely with many industries, including aerospace, military, intelligence, law enforcement, government research, and academic organizations.

Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications.

The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine OpenCV community source code resources available for hands-on practitioners.

It is pretty intense and your mental faculties will enjoy the thrilling journey you will be introduced to by Scott. You can find it all laid out well just for you. Click below, get to Amazon and order one for your private library.

7. Modern Computer Vision with PyTorch

A little information about the authors is always worth it. V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has more than 10 years’ experience in the field of data science with prominent technology companies. On the other hand, his co-author, Yeshwanth Reddy is a senior data scientist with a strong focus on the research and implementation of cutting-edge technologies to solve problems in the health and computer vision domains. He has filed four patents in the field of OCR.

They offer this 18 chapter book that takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.

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

As you progress, you will 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. There is much more.

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

Good news rings the message home that 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 will definitely find the use cases accompanied by notebooks in GitHub present in this book useful. Have a stimulating read ahead by clicking on the link below to grab a copy from Amazon.

8. Computer Vision: Principles, Algorithms, Applications, Learning

This book by E.R. Davies 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.

It provides a wide selection of topics ranging from classical Computer Vision subjects like filters and morphology to more current methods involving deep learning. E.R. Davies has nicely divided the into sections, Low Level, Intermediate Level, and Advanced. The advance topics include a good introduction to both shallow and deep machine learning, and Vison and Motion.

Key features include:

  • Three new chapters on Machine Learning
  • Emphasize 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 and gives practical demonstrations of its application.
  • In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face formalization, 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++)

There is much to chew and digest in this voluminous resource that will equip you with knowledge, solutions and skills that you can run away into the world. Click below to find out more and to also order from Amazon.

9. Multiple View Geometry in Computer Vision

Author Richard Hartley is a professor at the Australian National University and Distinguished Researcher at NICTA in Canberra. His main area of research is Computer Vision.

Richard observes that a basic problem in computer vision is to understand the structure of a real world scene. This book he crafted covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms.

One reviewer, Michael Sander, had a very interesting comment as follows:

“Have you ever wondered why 4×4 matrices are used in computer graphics? In school we got a half-way answer about the projective matrix, but it was never really satisfying. This book explains the math behind that and much more clearly, leaving you with many “ah ha” moments”.

Grab it from Amazon and get those “aha” moments in your own way.

10. Practical Deep Learning for Cloud, Mobile & Edge

Relying on decades of combined industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.

Amazing things you will encounter

  • Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.
  • Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.
  • Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies.
  • Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.
  • Use transfer learning to train models in minutes.
  • Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.

Whether you are a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.

Click the link below to get your copy delivered from Amazon.


This scientific field that gives computers the capability to capture, interpret, understand, and process objects that are visually perceivable will simplify processes, eliminate costly errors and in the end reduce costs. Any serious organization or project that computer vision can augment should consider peeping into this tech to make their work super.

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