I too need a coffee in the morning before I can do anyhting
kmoser · 16h ago
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.
monkeyelite · 16h ago
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.
sokoloff · 13h ago
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.
Any recommendations on background reading for classical CV for radar?
monkeyelite · 9h ago
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.
klodolph · 16h ago
Maybe… some of these algorithms from the 1980s struggled to do basic OCR, so they may need a lot of modification to be useful.
PaulHoule · 16h ago
That whole approach of "find edges, convert to line drawing, process a line drawing" in the 1980s struggled to do anything at all.
Retric · 15h ago
There was a surprising amount of useful OCR happening in the 70’s.
High error rates and significant manual rescanning can be acceptable in some applications, as long as there’s no better alternative.
GuB-42 · 12h ago
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.
devilbunny · 8h ago
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.
alightsoul · 17h ago
Amazing. Wonder how fast it would be on a modern computer
Hydration9044 · 16h ago
+1, which is faster when compare to OpenCV findContours
cyberax · 13h ago
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!
changoplatanero · 12h ago
I believe orientation still matters but you’re right that position doesn’t.
Legend2440 · 12h ago
FFT is equivalent to convolution, which is widely used today for object recognition in CNNs.
bobmcnamara · 9h ago
> FFT is equivalent to convolution
What do you mean by that? Could you give me an example?
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.
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.
This was our vehicle: https://mercedes-benz-publicarchive.com/marsClassic/en/insta...
High error rates and significant manual rescanning can be acceptable in some applications, as long as there’s no better alternative.
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.
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!
What do you mean by that? Could you give me an example?
https://en.wikipedia.org/wiki/Convolution_theorem