
Computer Vision: Algorithms and Applications (2010) - kercker
http://szeliski.org/Book/
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colincsl
For those interested in learning vision from a machine learning perspective I
would suggest "Computer Vision: Models, Learning, and Inference" [1].
Szelinski's book is also great but gives a more classical overview of computer
vision.

[1]
[http://www.computervisionmodels.com/](http://www.computervisionmodels.com/)

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weavie
Most of my attempts at getting into CV fail because my knowledge of Maths is
incredibly rusty. I did Maths up to university level, but haven't used it
since.

Are there any good courses / books or other resources that would specifically
help at getting up to speed with the maths knowledge required to understand a
book like this?

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joshvm
I wouldn't recommend Szeliski for that - the book is basically a literature
review (and is now a little out of date given how fast CV moves). It's great
if you need a background survey on how things are done, but not for learning
from.

The level of maths required varies a lot. Linear algebra is used heavily
everywhere, you see a lot of optimisation (e.g. Levenberg Marquadt) and in
some places graph theory. However if you're just using tools like OpenCV then
you can get by with a fairly poor understanding of the maths (say 2nd year
undergrad of an engineering degree).

Part of the challenge is reading past the maths. If you read a paper from
Pattern Analysis there's a lot of, frankly, obtuse notation. So you see images
described as discrete mapping blah blah. A lot of it is fairly straightforward
set theory. It's necessary for proving things, but when it comes to actually
implementing this stuff it's nowhere near as complicated as it looks.

~~~
weavie
Yes I think the notation is quite a stumbling block. Eg, As soon as I see an
integral sign I am lost. I have no idea how to transform that into an
algorithm.

~~~
joshvm
You might be better off looking through the code of a mature image processing
library like OpenCV or this one (recent HN post) written in Go which is very
clean:

[https://github.com/anthonynsimon/bild](https://github.com/anthonynsimon/bild)

~~~
weavie
Good idea! Thanks.

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fitzwatermellow
Aude Olivia's course at MIT is also a good resource:

6.869: Advances in Computer Vision (Fall 2015)

[http://6.869.csail.mit.edu/fa15/index.html](http://6.869.csail.mit.edu/fa15/index.html)

Or just jump right into the deep end with OpenCV:

[http://opencv.org/](http://opencv.org/)

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stared
The only one risky thing is that in computer vision right now things change
each 6 months. So, one will never know which techniques are still state of the
art, and which were outclassed by some ConvNets 4 years ago.

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emanuelev
Regardless the impact machine learning techniques are having on the field, I
still think is valuable to know and understand what are the classic methods
that they are allegedly replacing.

~~~
rsp1984
It is actually not about "classic" vs. "modern". A lot of what's described in
the book is as relevant as ever.

Mostly what's changed is that what used to be heuristics and hand-crafted
features is now getting replaced by proper learned models. Also some
generative models are getting replaced discriminative models since deep
learning does a very good job in creating those.

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ansgri
This is maybe the best textbook on the basics of image processing, computer
vision and computational photography. The author is from MS Research and they
do know this stuff.

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zanalyzer
He is at Facebook now

~~~
zump
Geez, I wonder what the offers these notable researchers are getting.

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rawnlq
Given how fast this field moves, is this still a recommended textbook (other
than being free)? If not what would a good supplement for it be?

