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I get what you mean, but the same situation applies when describing the algorithm to decode QR codes (it involves "deep" stuff like Reed-Solomon) and yet info o

by kaoD 3y ago

I get what you mean, but the same situation applies when describing the algorithm to decode QR codes (it involves "deep" stuff like Reed-Solomon) and yet info on that is far more prevalent. Why is that the case? Nobody expects a detailed tutorial, but it's hard to even come by a list of techniques that are known to work (or maybe I'm just trash at search).

alex_suzuki 3y ago
What you can do is read a paper that’s relevant to your problem (let‘s say QR code perspective correction), and then dig through the references. Good papers usually have plenty of good references.
avianlyric 3y ago
You can find plenty of information out there on various standard image and signal processing algorithms. Basic stuff like 2D convolutions form the backbone of many image processing techniques, and they’re well documented. But just like Reed-Solomon encoding is a very specific algorithm, with many different applications, of which QR codes is one. What you’re looking for is similar, it’s a huge set of very specific algorithms with huge set of applications, of which one is QR codes. QR codes are simply too niche for anyone to have put together a pubic document on what exact image processing techniques you might use to decode them. If you want to teach people about 2D signal processing, then QR codes are probably not a good starting point. I would also argue that Reed-Solomon encoding is not “deep” anything. It’s one of many different 1D signal error correction algorithms that exist. For people who work on signal processing as day job, Reed-Solomon is about as “deep” as quick-sort is to programmers. As for why quick-sort is well documented with many open implementations, and QR codes aren’t. I would argue it’s simple due to the industry they developed in. QR codes started in manufacturing, being used for inventory management, almost certainly by embedded and electronic engineers. All of those industries pre-date the open source movement by decades, trade secrets are still important for them, so they’re not naturally inclined to share IP.

by kaoD 3y ago

alex_suzuki 3y ago

What you can do is read a paper that’s relevant to your problem (let‘s say QR code perspective correction), and then dig through the references. Good papers usually have plenty of good references.

avianlyric 3y ago

You can find plenty of information out there on various standard image and signal processing algorithms. Basic stuff like 2D convolutions form the backbone of many image processing techniques, and they’re well documented. But just like Reed-Solomon encoding is a very specific algorithm, with many different applications, of which QR codes is one. What you’re looking for is similar, it’s a huge set of very specific algorithms with huge set of applications, of which one is QR codes. QR codes are simply too niche for anyone to have put together a pubic document on what exact image processing techniques you might use to decode them. If you want to teach people about 2D signal processing, then QR codes are probably not a good starting point. I would also argue that Reed-Solomon encoding is not “deep” anything. It’s one of many different 1D signal error correction algorithms that exist. For people who work on signal processing as day job, Reed-Solomon is about as “deep” as quick-sort is to programmers. As for why quick-sort is well documented with many open implementations, and QR codes aren’t. I would argue it’s simple due to the industry they developed in. QR codes started in manufacturing, being used for inventory management, almost certainly by embedded and electronic engineers. All of those industries pre-date the open source movement by decades, trade secrets are still important for them, so they’re not naturally inclined to share IP.