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There’s a pretty vast gulf between 70s era threshold, edge detect, morphology type methods and deep learning. Robust feature detectors, voting methods, etc. ca
by neetdeth 8y ago
There’s a pretty vast gulf between 70s era threshold, edge detect, morphology type methods and deep learning.
Robust feature detectors, voting methods, etc. can fill that gap for object detection and pose estimation. Also simpler machine learning models for classification like SVMs.
It increasingly feels like the slice of computer vision techniques I learned in the early 2000s is receding in relevance. But at the same time, there ought to be a more modern grab bag of robust tools to build engineered solutions.
In the end any algorithm is only as good as the dataset it’s developed/validated against. Ad-hoc methods may fail in bright sunshine, but so too would a neural network if that condition never arises in its training set.
- trentlott 8y agoThe future of computing is here! After tiring of convoluted web 3.0 frameworks and Babel-esque dependency structures, programmers have finally fashioned a black box they cannot understand which relieves the anxiety of knowing they should.
- king_magic 8y agoDo you know how your brain recognizes objects in images? Not superficially, but truly understand how it works at a deep fundamental level? I'll wait. Until then, when it comes to the state of the art in things like reading license plates, it's the old algorithmic approaches (like described in the article) that result in convoluted, brittle, easily broken approaches - far closer to the "convoluted web 3.0 frameworks and Babel-esque dependency structures" you seem to despise. DL may not be fundamentally understandable (yet), but it works a hell of a lot better than stringing together image processing algorithms.