Top 5 computer vision books for everyone

Computer vision books.

Food for thought

Do you want to become a true computer vision expert? Then pick one of our top 5 computer vision textbooks and programmer books and start reading!

But before diving into it, you might want to take a look at our article on computer vision definition or our blog post on computer vision conferences to follow your passion for the field. 

Enjoy!

Computer Vision: Algorithms and Applications

  • Author: Richard Szeliski.
  • Date of publication: 2010.

This authoritative textbook is ideal for an upper-level undergraduate or graduate-level course in engineering or computer sciences. It encompasses a wide range of techniques used to analyze and interpret images. In addition, it covers several related and complementary disciplines such as statistics, linear algebra, etc. for a comprehensive preparation in computer vision. You can also practice with the exercises at the end of the chapters. Finally, the book also provides a concrete perspective on real-life applications of the technology.

Programming Computer Vision with Python

  • Author: Jan Erik Solem.
  • Date of publication: 2012.

Solem’s book is particularly suitable for students and researchers as well as for those with basic programming and mathematical skills and a strong passion for computer vision. Indeed, it thoroughly covers the main theory and algorithms in computer vision, supporting the learning experience with exercises and access to the well-known OpenCV library. The latter is presented with an interface written in Python. Far from being too distant from reality, the book illustrates code samples and the major computer vision applications

Computer Vision: A Modern Approach

  • Author: David A. Forsyth.
  • Date of publication: 2011.

A classic textbook in computer vision for upper-level undergraduate or graduate-level course in engineering or computer sciences. Though published in 2011, it still provides the most comprehensive account of computer vision theory and methods. 

Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs 

  • Author: Sunila Gollapudi.
  • Date of publication: 2019.

This recently-published book is addressed to people with a basic understanding of machine learning and Python. It covers the field of computer vision and, more specifically, image and object detection, tracking and motion analysis. Readers can build their own applications using the OpenCV library with Python and experiment with deep learning models with both CNN and RNN. A good way to understand computer vision and how this cutting-edge technology works. 

Computer Vision: Advanced Techniques and Applications

  • Author: Steve Holden.
  • Date of publication: 2019.

A great book to dive into the world of computer vision. You will find contemporary theories as well as practical applications of the technology such as the development of artificial intelligence (AI), video surveillance, etc. Food for thoughts to keep updated with this rapidly evolving and fascinating field!

That’s it for our favorite computer vision books. If you want to know more about computer vision and image recognition, visit our website or read our blog!

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