51–60 of 60 posts
Most CV tasks are borderline impossible if your input is acquired under uncontrollable lighting. Whereas the right illumination setup can often let you get away with nothing but a threshold binarization.
It's nice, but missing the most valuable (and simplest) take from computer vision: the Hough transforms. Let's take the circle Hough transform as it's one of the most enlightening ones! Say you are looking for a circle of a given diameter. After a binarization to make the edge stand out, make all the potential points "vote" for a circle center. The method is simple: using a matrix, you +1 all the points that are as f…
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YOLO was such a shake up of the computer vision space that he could probably get hired just about anywhere with a resume crudely written in crayon.
The charts in this paper are hilarious: https://pjreddie.com/media/files/papers/YOLOv3.pdf Previous authors didn’t start their axes at 0, so he kept their axes and just put the timing for YOLO outside the original chart area.
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I think I'm missing the point. What does any of this have to do with computer vision?
He was able to turn a RAM chip into a camera, allowing the computer to process a video "feed" simply by polling the right bits in RAM. On a device that would normally be considered much too primitive to do any image processing.
Whoa sounds interesting! I always wondered what happened to him after giving up on YOLO because he felt it was against his morals. I honestly give him props because he probably could of capitalized on his work if he wanted to and play his cards right.
A few years ago he said he’d thought about quitting research and opening a vegan cafe or something. Not sure what he’s planning to do now though.
In terms of practical application (e.g. in industry), the biggest bang for your buck is "get the illumination right". Surprised this never appears in the course (at least from glancing over the syllabus and some slides). Most CV tasks are borderline impossible if your input is acquired under uncontrollable lighting. Whereas the right illumination setup can often let you get away with nothing but a threshold binarizat…
It's nice, but missing the most valuable (and simplest) take from computer vision: the Hough transforms. Let's take the circle Hough transform as it's one of the most enlightening ones! Say you are looking for a circle of a given diameter. After a binarization to make the edge stand out, make all the potential points "vote" for a circle center. The method is simple: using a matrix, you +1 all the points that are as f…
Edge binarization is dependent upon edge detection algorithm choice, threshold algorithm choice, and both of their respective parameters. It's often very difficult to find a set of parameters that aren't brittle due to occlusions, poor contrast, camera noise, etc.
Hough works great if you can do this part confidently. But in my experience, robust edge binarization for Hough is often not very feasible in the wild.
A really ancient secret, one of the grey beards I learned a lot from early in my career told me about how he got CV running on an Apple II way back in the day on the cheap. He decapped a DRAM, and carefully stuck a lens on it. They're not just susceptible to cosmic rays; without the package regular old visible light rays can cause bit flips too. If you look at CMOS sensors these days they actually have quite a bit in…
Earlier quoted context omitted.
He was able to turn a RAM chip into a camera, allowing the computer to process a video "feed" simply by polling the right bits in RAM. On a device that would normally be considered much too primitive to do any image processing.
Oh my god. That's amazing. I would never have believed that's possible.