She's done some interesting work... wish the NYT article had focused more on that and less on Rubik's cube.

jgrahamc, That is some very impressive stuff. Do you know of any good places to start to get into image processing? (specifically the mathematical tricks (DCT's and such))

Short answer: No.

I didn't know anything about image processing at all until I read her paper. It took my quite a while to get to grips with all the terminology and ideas. I actually went through that paper line by line as I built my code and looked up every term I didn't understand on Wikipedia and then used links from there to understand what it was all about.

One thing that she needs to be commended on is the clarity of that paper. I was able to follow it and implement her algorithm starting from zero knowledge. She then provided me with the actual images that she had used so that I could verify that my implementation worked.

As with anything I'd suggest finding a project that inspires you and the inspiration will be enough motivation to make you learn anything.

There's a toolbox for Matlab that encapsulates a lot of digital image processing algorithms.

Here's the text we used in my computer vision class, probably the classic in the field:

http://www.amazon.com/Digital-Image-Processing-Rafael-Gonzal...

Learning the specific feature space of digital images is no big deal. You might explore wavelet transformation and different image formats (BMP, JPEG, JPEG2000). Pixel intensity, color, edge detection, high and low pass filtering, connectivity between objects, pattern recognition... there are a lot of topics.

And then there are disciplines built on top of image processing, like face detection, watermarking, image retrieval/search, editing/transformation (think Photoshop)... and of course video is another can of worms, adding time-series data.

There are strong connections between higher-level image processing and statistical AI and data mining, so you might consider exploring those topics too.

I recommend the book "Two-Dimensional Imaging" by Ronald N. Bracewell. I've looked at many signal and image processing books, and I prefer this one by far. Almost all others seem to either shy away from math to the point of absurdity or revel in it to the point of forgetting the practical aspects.

Image processing is one of those fields where there is an incredible amount of stuff online, but it is so fragmented, its almost useless to someone who isn't already an expert. A good book builds a consistent set of notation and terminology so that (once you've gotten used to it) you can understand the links and connections between different topics.