This article will list and briefly review the top ten computer vision books we recommend reading in 2022.
Computer vision is the most technologically mature field of modern artificial intelligence (AI). The ability to automate human sight using machine vision opens up tremendous opportunities in all sectors of the economy. From agriculture to retail, from insurance to construction, computer vision applications apply to a wide range of industry-specific use cases.
You are reading: Books on computer vision
Below, we will provide an up-to-date list of books we recommend reading to learn about the most popular computer vision concepts, algorithms, and applications.
the best computer vision books
- Book #1: Computer Vision: Algorithms & Applications
- Book #2: Practical Deep Learning for the Cloud, Mobile & edge
- Book #3: Computer Vision Concise: An Introduction to Theory and Algorithms
- Book #4: Computer Vision: Principles, Algorithms, Applications, Learning
- book #5: computer vision: models, learning, and inference
- book #6: deep learning for vision systems
- book #7: computer vision modern with pytorch
- book #8: multiview geometry in computer vision
- book #9: learning opencv 4 computer vision with python 3
- Book #10: Machine Vision Metrics: Survey, Taxonomy, and Analysis
1. artificial vision: algorithms and applications
by richard szeliski
description the book aims to provide a comprehensive course in computer vision for undergraduate computer science students; it is also known as “the computer vision bible”. therefore, the focus is on algorithms, applications, and techniques for image processing and recognition in machine vision.
The book describes a variety of real-world applications and discusses the implementation and practical challenges of machine vision techniques. is an excellent textbook on modern machine vision and covers all the newer methods except deep learning, which started after the book was published.
why should you read it? The book is suitable for teaching a high-level course in computer vision. Its primary use is as a general reference to fundamental techniques and recent research literature for graduate students, researchers, and practitioners.
2. Practical deep learning for the cloud, mobile devices & border
by siddha ganju, meher kasam and anirudh koul
description a step-by-step guide on how to build practical deep learning applications for cloud, mobile, and edge devices using a hands-on approach. The book covers how to train, tune, and deploy modern computer vision models with Keras, Tensorflow, Tensorflow Lite, and Core ML. covers how to develop AI applications for various devices like raspberry pi, nvidia jetson nano, google coral and others.
why should you read it?
Get up to speed quickly, with deep learning and modern development frameworks; it’s great to learn practical applications of deep learning. The book is excellent for software developers looking to develop applicable skills in the field of AI. data scientists can enrich their skill set and deepen their knowledge of the field to build real projects. we recommend the book to students to help them pursue a career in artificial intelligence by developing a portfolio of engaging and fun real-world projects and help unleash creativity.
The book is also suitable for those with no prior AI or machine learning experience looking to explore and begin their AI journey. We highly recommend reading this book if you’re interested in learning how deep learning and cv projects are built at top computer vision companies.
3. Concise Computer Vision: An Introduction into Theory and Algorithms
by reinhard klette
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description This book provides a general introduction to the fundamental topics of computer vision, highlighting important mathematical concepts and algorithms. The book includes programming exercises and quizzes at the end of each chapter. From mathematical concepts to image recognition, image segmentation, and the building blocks of a computer vision system, the book covers a wide range of cv-related topics.
why should i read it? this is an excellent easy to read guide on a difficult topic. carefully selected topics cover all essential topics in an equally balanced way. classic and modern methods are explained in very clear descriptions, making this a great introductory level book for learning and reference.
4. artificial vision: principles, algorithms, applications, learning
by e. r. David
description when it comes to the fundamental concepts of computer vision, ranging from classical computer vision techniques to deep learning, this is the best book available on the market. covers basic machine vision methodology, including essential elements of theory, while highlighting the practical and algorithmic limitations of design.
Why should I read this? The book includes concepts and applications of computer vision, making it suitable for undergraduate and graduate students, researchers, and engineers working in computer vision.
5. artificial vision: models, learning and inference
by simón j. d. prince
description the book is one of the most complete on artificial vision. it is written for people who are new to the subject or who already have intermediate knowledge. This book guides computer vision beginners and developers on how to build and customize computer vision systems. it is perfect for learning more about model fitting and probability. The author provides clear descriptions, figures, application examples and exercises, background math, and code examples. the book describes more than 70 machine vision algorithms.
why should i read it? the book is very well written, very concise and logically structured, outlining the basic principles of machine vision. this makes it a good read for anyone seriously interested in computer vision. Chapters 14-16 may be all you need for a quick introduction to computer vision.
6. deep learning for vision systems
by mohamed elgendy
description This book is excellent for starting a career in modern computer vision and machine learning. provides what you need to know to build a good model from scratch, using practical terms. therefore, the book covers how to use deep learning architectures to build machine vision applications for imaging and facial recognition.
why should you read it? The book offers a comprehensive walkthrough for anyone looking to build real-world vision systems. therefore, it does not delve into the math or cover every branch of research from the last 20 years, but instead focuses only on exactly what is used in industry today. As one of the best computer vision books on the market, it is very easy to read and absorb.
7. Modern Computer Vision with PyTorch
by yeshwanth reddy and v kishore ayyadevara
description the book covers the basics of artificial neural networks (ann), the basics of pytorch, and multiple basic examples of deep learning using pytorch. Additionally, the book discusses major computer vision topics such as object detection and classification, image manipulation, and more. in addition, advanced topics such as gans, reinforcement learning, self-attention, short-shot learning, etc. are covered, while all discussions come with pytorch-specific examples.
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why should you read it? is an excellent book for beginners and practitioners who want to learn the latest updates and advancements. It’s important to note that the book assumes the reader has at least some basic understanding of python.
8. multiview geometry in machine vision
by richard hartley
description the book deals with how to reconstruct scenes from images using geometry and algebra, with applications to artificial vision. therefore, it describes the main techniques of mainstream multiview geometry, both classical and modern, clearly and consistently.
In this way, the author covers the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. the book introduces the necessary knowledge in 3d remodeling from multiple images captured simultaneously or chronologically.
Why should I read it? The book is a must-read for computer vision researchers and anyone interested in geometry, computer vision, and projective geometry. For advanced researchers, this book is extremely useful, but it is quite difficult for beginners. the only thing it doesn’t provide is written code, not even pseudocode, which would be useful.
9. learning opencv 4 computer vision with python 3
by joseph howse and joe minichino
description this book helps those who are new to computer vision, but also experts in the domain. covers the theory and practice of building applications with opencv 4 and python 3 on various platforms. learn how to perform basic operations, how to perform image processing, video analysis, and depth estimation and segmentation. features two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which are used to create and use object detectors and even track objects on a video camera.
why should you read it? if you want to learn opencv with python, this is probably the best opencv book. It covers all the main topics under the opencv umbrella in a very readable way. If you’re interested in detecting custom objects, this book offers a simple, easy-to-follow approach. includes a set of object-oriented programs that you can create for custom applications.
10. machine vision metrics: survey, taxonomy and analysis
by scott krig
description an extensive survey and analysis of over 100 machine vision methods and description of current and historical features. The book covers image capture and rendering, image preprocessing, global and regional features, local feature design concepts, classification, and more.
Overall, the book is very well researched and cited, making it a useful summary and survey book as it points out relevant references. therefore, the book is often used by machine vision engineers as a reference for various machine vision techniques and algorithms.
why should you read it? the book is free to download in pdf format. this book contains literally hundreds of algorithms in a very well organized and easy to read form. In the fast-moving field of machine vision, it’s very useful to have this kind of catalog on hand.
What’s next?
Those books provide everything you need to know to get started with computer vision. however, as the field of computer vision is developing rapidly, it is important to stay current. Subscribe to the viso newsletter for free to be informed of the latest trends and news in ai vision.
To read more about state-of-the-art methods and techniques, I recommend you check out our other articles:
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