This article showcases our top picks for the Best Books On Computer Vision. We reached out to industry leaders and experts who have contributed the suggestions within this article (they have been credited for their contributions below).
We are keen to hear your feedback on all of our content and our comment section is a moderated space to express your thoughts and feelings related (or not) to this article This list is in no particular order.
Amita Kapoor is an associate professor in the Department of Electronics, SRCASW, University of Delhi, and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her master’s in electronics in 1996 and her PhD in 2011. She has more than 50 publications in international journals and conferences. Her present research areas include machine learning, artificial intelligence, deep reinforcement learning, and robotics. Sujit Pal is a technology research director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group. His areas of interest include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora. In addition to co-authoring a book on deep learning with Antonio Gulli, Sujit writes about technology on his blog, Salmon Run.
This product was recommended by Baker Mesh from Survival Mesh
The OpenCV 2 Cookbook has a very good introduction and it gets you started right away. It’s easy to work with the new interface and it teaches about the capabilities of OpenCV like how to use it for specific purposes. This book is suitable for my fellow Mac programmers.
This product was recommended by Chiara Gomiero from Handy Wine Guide
It’s the perfect book to start off if you have basic programming and mathematical skills. It’s also usually recommended for students and researchers. It covers in-depth theory and algorithms in computer vision.
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