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Image Processing in C – Dwayne Phillips [pdf] (homepages.inf.ed.ac.uk)
TrackerFF 2 hours ago [-]
310 pages of text, 500 pages of C code in the appendix - this could need a supplemental github page.
typolisp 1 hours ago [-]
SanjayMehta 4 hours ago [-]
You might find this interesting as well:

https://www.spinroot.com/pico

numba888 3 hours ago [-]
2000-2003, both are pre-historic. We have neural networks now to do things like upscaling and colorization.
vincenthwt 3 hours ago [-]
Yes, those methods are old, but they’re explainable and much easier to debug or improve compared to the black-box nature of neural networks. They’re still useful in many cases.
earthnail 3 hours ago [-]
Only partially. The chapters on edge detection, for example, only have historic value at this point. A tiny NN can learn edges much better (which was the claim to fame of AlexNet, basically).
grumbelbart2 2 hours ago [-]
That absolutely depends on the application. "Classic" (i.e. non-NN) methods are still very strong in industrial machine vision applications, mostly due to their momentum, explainability / trust, and performance / costs. Why use an expensive NPU if you can do the same thing in 0.1 ms on an embedded ARM.
fsloth 2 hours ago [-]
"The chapters on edge detection, for example, only have historic value at this point"

Are there simpler, faster and better edge detection algorithms that are not using neural nets?

4gotunameagain 3 hours ago [-]
Classical CV algorithms are always preferred over NNs in every safety critical application.

Except Self driving cars, and we all see how that's going.

2 hours ago [-]
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