2000-2003, both are pre-historic. We have neural networks now to do things like upscaling and colorization.
14 minutes ago [-]
vincenthwt 1 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 1 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).
fsloth 9 minutes 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?
grumbelbart2 17 minutes 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.
4gotunameagain 1 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.
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Are there simpler, faster and better edge detection algorithms that are not using neural nets?
Except Self driving cars, and we all see how that's going.