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.
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...
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/c399862d3...
even though I think Simon admits that most of it is obsolete after DL computer vision came about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?