One approach that blew my mind was the use of FFT to recognize objects.
FFT has this property that object orientation or location doesn't matter. As long as you have the signature of an object, you can recognize it anywhere!
I want to believe that however obsolete these old algorithms are today, at least some aspects of the underlying code and/or logic should prove useful to LLMs as they try to generate modern code.
The idea that ML is the only way to do computer vision is a myth.
Yes, it may not make sense to use classical algorithms to try to recognize a cat in a photo.
But there are often virtual or synthetic images which are produced by other means or sensors for which classical algorithms are applicable and efficient.
I worked (as an intern) on autonomous vehicles at Daimler in 1991. My main project was the vision system, running on a network of transputer nodes programmed in Occam.
The core of the approach was “find prominent horizontal lines, which exhibit symmetry about a vertical axis, and frame-to-frame consistency”.
Finding horizontal lines was done by computing variances in value. Finding symmetry about a vertical axis was relatively easy. Ultimately, a Kalman filter worked best for frame-to-frame tracking. (We processed video in around 120x90 output from variance algorithm, which ran on a PAL video stream.)
There’s probably more computing power on a $10 ESP32 now, but I really enjoyed the experience and challenge.
You could even argue that ML does classical vision in addition to other stuff.
CNNs learn gabor filters. The AlexNet paper even shows this [0]
Or if you look at the work ViT built itself on, they show attention heads will also learn these fillers. [1] That's actually a big part of how ViTs work, the heads integrate this type of information
I don’t know anything about radar. I have a book called “machine vision” (Shmuck, Jain, Kasturi) easy undergrad level, but also very useful. It’s $6 on Amazon.
I find that modern OCR, audio transcription, etc... are beginning to have the opposite problem: they are too smart.
It means that they make a lot fewer mistakes, but when they do, it can be subtle. For example, if the text is "the bat escaped by the window", a dumb OCR can write "dat" instead of "bat". When you read the resulting text, you notice it and using outside clues, recover the original word. An smart OCR will notice that "dat" isn't a word and can change it for "cat", and indeed "the cat escaped by the window" is a perfectly good sentence, unfortunately, it is wrong and confusing.
Thankfully, most speech misrecognition events are still obvious. I have seen this in OCR and, as you say, it is bad. There are enough mistakes in the sources; let us not compound them.
I'm not sure I can sign on to this. In particular, this sounds kind of like an indictment of many algorithms. But, how many where there? And did any go on to give good results?
Considers, OCR was a very new field, such that a lot of the struggle was getting data into a place you could even try recognition against it. It should be no surprise that they were not able to succeed that often. It would be more surprising if they had a lot of different algorithms.
FFT has this property that object orientation or location doesn't matter. As long as you have the signature of an object, you can recognize it anywhere!