Found 3 comments on HN
wyc · 2017-11-14 · Original thread
The book Mastering OpenCV helped me a bunch several years ago during implementation. The best advice I can give along with this book is to try and understand the fundamental geometric problem that computer vision tries to solve. It's been the same for like 30-50 years...we've just thrown some ML at it for these past 10-20. For that, I recommend a copy of Trucco and Veri's book that you can probably find floating around online (link below). Good luck!

wyc · 2017-06-28 · Original thread
This is pretty old school, but I recommend Multiple View Geometry by Hartley and Zisserman ( to get through the's really good to understand the geometric foundations for the past 4 decades. Along the same lines, you have Introductory Techniques for 3-D Computer Vision by Trucco and Verri (, which also goes over the geometry and the fundamental problems that computer vision algorithms try to solve. It often does come down to just applying simple geometry; getting good enough data to run that model is challenging.

If you just throw everything into a neural network, then you won't really understand the breadth of the problems you're solving, and you'll be therefore ignorant of the limitations of your hammer. While NNs are incredibly useful, I think a deep understanding of the core problems is essential to know how to use NNs effectively in a particular domain.

After getting a grip on those concepts, Szeliski's Computer Vision: Algorithms and Applications ( had some really amazing coverage of CV in practice. Mastering OpenCV ( was very useful when actually implementing some algorithms.

plinkplonk · 2007-10-29 · Original thread
This ( is the best book on Computer Vision I 've come across. I first encountered it when trawling Stanford courses for good problem sets and research projects. Sebastian Thrun used to teach a class with this book ( It is fairly expensive and fairly mathematical but it is worth every cent you pay for it (imho).

Good Luck !

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