When memory was measured in kilobytes: The art of efficient vision

155 pointsposted 8 months ago
by todsacerdoti
(softwareheritage.org)

34 Comments

cyberax

8 months 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

8 months ago

I believe orientation still matters but you’re right that position doesn’t.

Legend2440

8 months ago

FFT is equivalent to convolution, which is widely used today for object recognition in CNNs.

bobmcnamara

8 months ago

> FFT is equivalent to convolution

What do you mean by that? Could you give me an example?

kragen

8 months ago

The FFT, composed with pointwise multiplication, composed with the inverse FFT, is equivalent to convolution. The FFT is not.

timewizard

8 months ago

The basic convolution theorem.

https://en.wikipedia.org/wiki/Convolution_theorem

bobmcnamara

8 months ago

That is something else entirely.

timewizard

8 months ago

Then if you know what the OP meant why did you ask?

Grimblewald

8 months ago

because they made a nonsensical claim that doesn't align with my (and likely their) understanding of what the FT is and does.

The FT is _NOT_ just a convolution, but under certain conditions a specific operation on FT terms is equivalent to a convolution.

bobmcnamara

8 months ago

I didn't know what they meant. There are so many FFT tricks. I was hoping this was another.

kmoser

8 months 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

8 months 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

8 months 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.

This was our vehicle: https://mercedes-benz-publicarchive.com/marsClassic/en/insta...

digdugdirk

8 months ago

That's awesome! What kind of hardware was needed to pull that off? And was the size of the bus any indication of the answer?

thatcat

8 months ago

Any recommendations on background reading for classical CV for radar?

monkeyelite

8 months 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.

ipunchghosts

8 months ago

Kasturi was my undergraduate honors advisor!

monkeyelite

8 months ago

Small world! These are always just names on a book to me.

thatcat

8 months ago

Awesome, thanks!

sceadu

8 months ago

Don't know about radar but here's a good book on classical CV https://udlbook.github.io/cvbook/

even though I think Simon admits that most of it is obsolete after DL computer vision came about

monkeyelite

8 months ago

> is obsolete after DL computer vision came about

I just don’t understand this. Why would new technology invalidate real understanding and useful computer algorithms?

klodolph

8 months 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

8 months 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

8 months 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

8 months 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

8 months 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.

taeric

8 months ago

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.

alightsoul

8 months ago

Amazing. Wonder how fast it would be on a modern computer

Hydration9044

8 months ago

+1, which is faster when compare to OpenCV findContours

mrheosuper

8 months ago

I still deal with <128kb ram system everyday

weareregigigas

8 months ago

I too need a coffee in the morning before I can do anyhting