(Part 2) Best computer vision & graphics books according to redditors

Learn as much linear algebra as possible.

Read Prince's computer vision book every day.

Make the switch to C++ as early as possible, or earlier.

I think that a rock solid foundation in mathematical statistics is really useful for reading about all other applied topics and the literature (to see the most advanced techniques you usually have to look beyond textbooks).

So I vote for Bickel and Doksum, Mathematical Statistics. Then for a good foundation for pattern recognition, I suggest DGL a probabilistic theory of pattern recognition.

From those, you will have a great base to stand on and learn anything else.

broken link.

http://www.amazon.com/Couscous-Other-Good-Food-Morocco/dp/0060913967

I can't tell exactly what you want to improve upon. If you just want better intersection algorithms/code, look at:
http://www.amazon.com/Introduction-tracing-Kaufmann-Computer-Graphics/dp/0122861604

http://www.amazon.com/Real-Time-Rendering-Third-Tomas-Akenine-Moller/dp/1568814240

The real performance gains in ray tracing, though, are parallelization and reducing the number of intersections tests that need to be done at all.

Parallelization is trivial.

For reducing intersection tests, you're best bet is some kind of space partitioning algorithm, like an octtree, k-d tree, or grid. It's not very hard either. The main idea is to pre-process the input into a grid-like data structure and do intersection testing by "walking" along the ray, through the cells. In each cell, do an intersection test with each object in that cell. Find the closest hit, and bail out. Traversing the grid is super easy and a lot faster than intersection testing. And (ideally) grid cells have very few objects, so the number of intersection tests is cut down a bunch, too.

The original paper for that technique, if you can find it, is this: http://portal.acm.org/citation.cfm?id=95111

If you want to learn about dicom, Start with this book: http://www.amazon.com/Digital-Imaging-Communications-Medicine-DICOM/dp/3642108490

Here is a rough paraphrasing from the book what will explain the DICOMDIR in a nutshell:

DICOMDIR is a very special DICOM metadate file. While all other DICOM files store
their own DICOM data objects, DICOMDIR stores the information about
DICOM files in a given file directory. Thus, DICOMDIR plays the role
of a small DICOM database, or an index of DICOM files, placed in the root
folder of the media.

DICOMDIR organizes all directory data into four principal DICOM levels: Patient, Study, Series,
and Image. for each file in the DICOMDIR folder, DICOMDIR will record four entries – patient, study, series, and image
information – corresponding to this file.

The list of all DICOMDIR items (patients, studies, series, and images)
is simply inserted into the DICOMDIR object as an SQ sequence element
(0004,1220). For each entry in the DICOMDIR (0004,1220) sequence, the
DICOMDIR object stores two types of data:

1. Entry-specific selection keys: This data type is provided to facilitate item
   searches in DICOMDIR. For example, series modality (0008,0060) is one
   of the most frequently used selection criteria when searching for series, so
   it is wise to have it as a selection key in our DICOMDIR index; we will
   know what modalities are present in the given directory.
   
   
2. Basic Directory Information Object: which stores exactly that, Directory info: file IDs, relationships between
   the files, and so on. The Basic Directory Information Object is meant to be an abstract
   representation of any media directory, something corresponding to a
   set of DICOM files, wherever they might be.
   
   So to answer both of your questions: Yes.

As a good reference book for OpenCV I recommend Learning OpenCV 3 by Kaeler and Bradsky

http://www.amazon.com/Making-Things-See-Processing-MakerBot/dp/1449307078

It's processing, not OpenCV-based, but it's entirely built around the kinect.

I'd suggest this book :

http://www.amazon.com/Pattern-Classification-Pt-1-Richard-Duda/dp/0471056693

For a start

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Absolutely. This topic is a favorite of mine and I've invested quite a bit of my free time in independent study of it. The place to start, for sure, is Li&Vitanyi. This is a graduate text and it assumes you already have a general familiarity with undergrad topics in CS. The key is to understand the prefix-free complexity measure. Once you get that down, the rest of it is a matter of careful applications of this key idea to other areas. It's not conceptually difficult but the proof techniques used in computability are a bit alien relative to proof techniques used in most other fields of CS/math. One of the coolest developments to come out of AIT is the incompressibility method, which is a new, general-purpose proof technique which is applicable to virtually any domain of mathematics.

Implications include artificial general-purpose intelligence (AGI), universal AI, philosophy and physics and many more. I consider AIT to be the under-appreciated diamond-mine discovered by 20th century mathematicians that outshines all other, admittedly beautiful, areas of modern mathematics by many orders of magnitude.

I recommend the 4th edition of "Computer and Machine Vision: Theory, Algorithms, Practicalities" by E. R. Davies. It might be a little out of date now, but I found the first couple chapters really helpful for introducing me to basic concepts. It does NOT have anything on machine learning or deep learning.

If you don't have a way to access the book (I got it through my college library) you can also try looking at https://en.wikipedia.org/wiki/Outline_of_computer_vision or find free lectures on youtube.

We used this one for a computer vision class I took last winter Trucco & Verri

We used this one, I remember it was pretty good. You can probably download it from libgen, along with problem solutions.

I recommend this book: Algorithms for Image Processing and Computer Vision. It's a great place to begin. It includes both explanation and implementation for many of the mathematical concepts utilized in image processing and computer vision. There's also a very good section on on linear classifiers and support vector machines which will give you an intuitive as well as a mathematical understanding of these concepts.

Matlab works well, but it can be pricey. R, and Python are amazing as a previous comment pointed out. If you're looking for a good IDE for R, I recommend R-studio.

The book we used for my graduate machine learning course was Pattern Classification by Duda, Hart, and Stork. It offers a great overview, and would be quite easy to read for someone with a background in statistics.

THIS

coldblade, your first book, http://www.amazon.com/Beginning-Game-Development-Unity-All-/dp/1430248998/ref=sr_1_1?ie=UTF8&qid=1376359953&sr=8-1&keywords=Unity, is the one that I have preordered. I have chosen that book because it builds upon previous examples and it focuses on Unity 4.

These 3 volumes are the best thing I can recommend. https://www.amazon.com/ESRI-Guide-GIS-Analysis-Relationships/dp/1879102064

Maybe not as accessible, but very pretty: Applied Partial Differential Equations - A Visual Approach by Peter Markowich

Thanks. Actually I wanted to buy a book by myslef. Just found some high level overview stuff about AR. Not interesting.

The best (although not specifically about AR and a bit old) seems to be this one:

https://www.amazon.com/Multiple-View-Geometry-Computer-Vision-ebook-dp-B00AKE1QK4/dp/B00AKE1QK4/ref=mt_kindle?_encoding=UTF8&me=&qid=1560576256