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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.

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.

thatcat 1y ago
Any recommendations on background reading for classical CV for radar?
- monkeyelite 1y 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 1y ago
    Kasturi was my undergraduate honors advisor!
    - monkeyelite 1y ago
      Small world! These are always just names on a book to me.
  - thatcat 1y ago
    Awesome, thanks!
- sceadu 1y ago
  Don't know about radar but here's a good book on classical CV https://udlbook.github.io/cvbook/ https://udlbook.github.io/cvbook/ even though I think Simon admits that most of it is obsolete after DL computer vision came about
  - monkeyelite 1y 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 is obsolete after DL computer vision came about I just don’t understand this.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?
sokoloff 1y 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/instance/picture/Prometheus-VITA-1.xhtml?oid=297093 https://mercedes-benz-publicarchive.com/marsClassic/en/insta...
- digdugdirk 1y 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?
godelski 1y ago
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 [0] https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3... [1] https://arxiv.org/abs/1911.03584 https://arxiv.org/abs/1911.03584

thatcat 1y ago

Any recommendations on background reading for classical CV for radar?

monkeyelite 1y 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 1y ago
  Kasturi was my undergraduate honors advisor!
  - monkeyelite 1y ago
    Small world! These are always just names on a book to me.
- thatcat 1y ago
  Awesome, thanks!
sceadu 1y ago
Don't know about radar but here's a good book on classical CV https://udlbook.github.io/cvbook/ https://udlbook.github.io/cvbook/ even though I think Simon admits that most of it is obsolete after DL computer vision came about
- monkeyelite 1y 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?

sokoloff 1y 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/instance/picture/Prometheus-VITA-1.xhtml?oid=297093 https://mercedes-benz-publicarchive.com/marsClassic/en/insta...

digdugdirk 1y 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?

godelski 1y ago

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 [0] https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3... [1] https://arxiv.org/abs/1911.03584 https://arxiv.org/abs/1911.03584