For years humans have been dreaming of attempting to develop models that can resemble human intelligence with great precision. From robotic vision to self-driving technology, the search is still on. One area that stands in the ocean of target capabilities is the machines’ ability to “act” in the same manner that we do. That includes visualizing and interpreting the world around and understanding the context of the environment. Decades back, this would be a fantasy, as opposed to now, when developing a reliable model is predominantly a matter of resource, time, and specialty. The credit goes to the massive breakthroughs in artificial intelligence that relies on high computational power whose applications are ingrained into our lives. Computer vision is the product of this technology, with a market projected to hit $48.6 billion by 2022, making it one of the most competitive and promising industries. In this blog, we will briefly cover the definition of computer vision and will share the top 15 computer vision books worth reading to drive your entire pipeline forward.

  • What do I need to know about computer vision?
  • How it works
  • Why is it relevant?
  • Top 15 computer vision books to read
  • Summing up

What do I need to know about computer vision?

Computer vision is an interdisciplinary scientific field that deals with identifying and processing objects in visual inputs, such as digital images, videos, and even text. Unlike computers, we, humans, have a head start. We process information using our lens, optical nerves, and retina, while technology uses cameras, data, and algorithms to perform the same function. Then what makes computer vision models exceptional? The ability of machines to process analogously complex information accurately at incredible speed.

How it works

A lot of data is required to kick off. The process involves running analyses of data as many times as necessary until the model learns to distinguish and recognize provided visual inputs. For example, for a model to be able to recognize a street light, it has to be fed by tons of street light images and street-lighting-related items to learn the differences and define categories. The model thus learns to make predictions based on the training data and gradually comes to recognize objects.

game-changing computer vision books

Why is it relevant?

In this modern age, companies are racing against time to implement scalable vision systems to tackle problems that offer value to their businesses. From product automation, medical diagnosis to recommender systems in marketing, computer vision helps us live and do business anew.

For instance, deep learning vision systems have been able to provide an accurate, early-stage medical diagnosis on life-threatening diseases, such as cancer. When it comes to self-driving technology, AI-assisted human drivers have taken the lead, with Tesla spearheading the new wave. The takeaway? Computer vision’s potential is huge, and the shift toward full AI automation is going at full throttle.

computer vision books

Top 15 computer vision books to read

Computer vision is an exciting field to AI enthusiasts: That explains the abundance of literature available on computer vision fundamentals. One of the proven ways of exploring this subject and learning from real-life experiences is through reading. The rest of this article will review some of the top picks among computer vision books in the market. Hopefully, this section will help you pick your next great read.

Computer vision: algorithm and applications by Richard Szeliski

The book was originally written for undergraduate students taking a computer science course. Yet, it became an essential resource for a wider audience, including researchers and professional practitioners. Written by computer vision expert Richard Szeliski, the book uncovers the different techniques that are applied in analyzing and interpreting images. This book is a must-read for anyone looking for a comprehensive yet subtle introduction to computer vision. Get the book.

Practical deep learning for cloud, mobile & edge by Siddha Ganju, Meher Kasam, and Anirudh Koul

This one provides a practical approach to deep learning applications for the cloud, mobile, and edge devices. Readers of this book get to learn in detail about how to train, tune and deploy modern computer vision models with Keras, Core ML, and TensorFlow. Practical deep learning for cloud, mobile & edge is a great pick for anyone who wants to learn about applications of deep learning using a hands-on approach. Get the book.

Concise computer vision: an introduction into theory and algorithms by Reinhard Klette

Here is another introductory book for newbies, classroom-focused, containing tested exercises and questionnaires. The book covers mathematical approaches to image mapping explains the topologic and geometric basics for analyzing image regions and distributions of image values. The book is recommended to anyone beginning the journey into computer vision. Get the book.

Computer vision: principles, algorithms, applications, learning by E.R. Davies

Among highly-rated computer vision publications, this one especially stands out. Davies extensively covers fundamental methodologies in computer vision while at the same time expanding on the theoretical part of it, including algorithmic and practical design limitations.

It is tailored for undergraduate and graduate students, professionals in technical fields, engineers, and researchers. This book is unique and palatable to this group because of its up-to-date approach and exposure to modern applications. Get the book.

Computer vision: models, learning and inference by Simon J.D. Prince

The intent behind Simon J.D Prince’s piece is to explain relationships that exist between image data and other aspects for estimation, such as object class and 3D structure. The end result is to make new inferences relating to the world from new image data. If you want to dive into real examples of developing and implementing functional vision systems, then this book is for you. Get the book.

Deep learning for vision systems by Mohamed Elgendy

Written by a seasoned researcher, the book covers topics on deep learning and computer vision, explaining how to make the computer understand what it “sees.” It expands further on image classification techniques and object detection, DeepDream, neural style transfer, transfer learning, and generative adversarial networks. If you want to get into image modification, image generation, and facial recognition, give the publication a chance. Get the book.

Modern computer vision with Pytorch by Yeshwanth Reddy and Kishore Ayyaderava

Yeshwanth Reddy and Kishore Ayyaderava provide learners with a hands-on approach to solving over 50 computer vision problems using PyTorch.lx on real-life datasets. The book specifically targets beginners in Pytorch and intermediate machine learning practitioners seeking to develop a stronger grasp of deep learning and PyTorch. Get the book.

Multiple view geometry in computer vision by Richard Hartley and Andrew Zisserman

Richard Hartley and Andrew Zisserman describe the basics in established traditional and modern multiple view geometry. Moreover, the book addresses the major geometric principles and explains the ways to define objects in algebraic form for easier computation. Finally, it gives learners an understanding of the computer vision structure within a real-world scene. Get the book.

Learning OpenCV 4 computer vision with Python 3 by Joseph Howse and Joe Minichino

In a nutshell, the book covers:

  • Latest in-depth cameras
  • 3D tracking
  • Augmented reality
  • Deep neural networks

Readers get the chance to solve computer vision problems with practical code, as well as learn to develop powerful models using Open CV 4 and Python 3. The book focuses on image processing, object categorization, and 2D/3D tracking. Get the book.

Computer vision metrics: survey, taxonomy, and analysis by Scott Krig

Computer Vision Metrics is a cutting-edge resource with extensive sections on historical feature description and machine vision methods. Detailed taxonomy for local, regional and global features is also presented. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness, and invariance attributes. If this is what you want to explore further, give it a shot. Get the book.

Programming computer vision with Python by Jan Solem

Published in 2012, the book describes how you can perform basic computer vision tasks using Python. You will learn techniques used in robot navigation, medical image analysis, and other applications, covering topics in image mappings and transforms, texture warping, and panorama creation. Additionally, it teaches you how to compute 3D reconstructions from a few images of the same scene. In short, a great pick for entry-level professionals or researchers, and students. Get the book.

Deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Deep learning is considered the most comprehensive book of a kind in the market as of now. Although technical, the authors have broken down everything you need to know and understand to get started. Below are some of the concepts covered:

  • Probability theory
  • Linear algebra
  • Numerical computation
  • Machine learning

The book further explains various deep learning techniques practitioners use, including optimization algorithms, deep feedforward networks, and regularization. What makes it a top pick is its thorough coverage of the foundational concepts bulleted above. Get the book.

Computer vision: a modern approach by David A. Forsyth

Forsyth covers computer vision comprehensively and gives enough detail for readers to create useful applications. This classic book is best suited for upper-level undergraduates or graduate readers taking computer science or computer engineering courses: a decent learning curve and expert insights are guaranteed.

Learn computer vision using OpenCV: with deep learning CNNs and RNNs by Sunila Gollapudi

Sunila’s book targets readers with a basic understanding of Python and machine learning. It covers computer vision pretty broadly, focusing on the following aspects:

  • Image and object detection
  • Tracking and motion analysis

The book is a must-read for students, researchers, and enthusiasts wanting to build applications using the OpenCV library with Python. Get the book.

Computer vision: advanced techniques and applications by Steve Holden

Steve Holden offers a deep dive into computer vision, bringing contemporary theories and practical applications of AI and video surveillance to the table. This book is dedicated to students, researchers, and enthusiasts who want to stay up-to-date with rapidly evolving computer vision trends and best practices. Get the book.

Summing up

Consistent refinement of technology projects a future where a multitude of tasks is handled by AI integration. Let’s face it; computer vision has exploded onto the technology scene over the last few years, which elevates the interest in the matter. If you want to become a computer vision expert or expand your knowledge of its applications, the books discussed will pave the path for you. What resources do you prefer for further learning? We’re looking forward to hearing from you! In the meantime, feel free to contact us should you have questions.

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