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Ancient secrets of computer vision

pjreddie.com

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Re: Ancient secrets of computer vision

#21

Post by amelius »

csdvrx wrote: post #15

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…

> After a binarization to make the edge stand out, make all the potential points "vote" for a circle center.

It's even simpler to make artificial neurons vote for a circle center.

You don't need the binarization step, and you can apply the method to other shapes as well.

Re: Ancient secrets of computer vision

#22

Post by edge17 »

amelius wrote: post #21
csdvrx wrote: post #15

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…

> After a binarization to make the edge stand out, make all the potential points "vote" for a circle center. It's even simpler to make artificial neurons vote for a circle center. You don't need the binarization step, and you can apply the method to other shapes as well.

The hough transform generalizes to other shapes as well

Re: Ancient secrets of computer vision

#23

Post by syntaxing »

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.

Re: Ancient secrets of computer vision

#24

Post by jacobolus »

reedf1 wrote: post #17
jstx1 wrote: post #5

Earlier quoted context omitted.

Hm. Do you think this is deliberate to filter out people with certain prejudices? Or do they genuinely think it’s a good design?

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.

Re: Ancient secrets of computer vision

#26

Post by csdvrx »

amelius wrote: post #21
csdvrx wrote: post #15

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…

> After a binarization to make the edge stand out, make all the potential points "vote" for a circle center. It's even simpler to make artificial neurons vote for a circle center. You don't need the binarization step, and you can apply the method to other shapes as well.

> It's even simpler to make artificial neurons vote for a circle center.

Is it?

It's not conceptually simpler: people can more easily imagine circles around points converging to a center, so they can also put that idea into code more easily.

> you can apply the method to other shapes as well.

Yes you can. Read about Hough.

I just presented the one that is the most enlightening.

I may be biased against neural network approaches and their likes, because I see them as black boxes with failure modes that are hard to predict or work around: I prefer what I can understand and explain, and unfortunately, it seems at odd with the current demographics of ML (cf https://news.ycombinator.com/item?id=27361812 ) who has no clue about what makes these black boxes tick, sometimes even after they get a PhD in the dark art of tweaking black boxes.

Re: Ancient secrets of computer vision

#27

Post by abetusk »

csdvrx wrote: post #15

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…

Some links that helped me understand the Hough transform:

* https://towardsdatascience.com/lines-detection-with-hough-tr...

* https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.2....

Re: Ancient secrets of computer vision

#28

Post by edge17 »

csdvrx wrote: post #26
amelius wrote: post #21

Earlier quoted context omitted.

> After a binarization to make the edge stand out, make all the potential points "vote" for a circle center. It's even simpler to make artificial neurons vote for a circle center. You don't need the binarization step, and you can apply the method to other shapes as well.

> It's even simpler to make artificial neurons vote for a circle center. Is it? It's not conceptually simpler: people can more easily imagine circles around points converging to a center, so they can also put that idea into code more easily. > you can apply the method to other shapes as well. Yes you can. Read about Hough. I just presented the one that is the most enlightening. I may be biased against neural network…

One of the nicer things about the hough approach is also that you can get a bunch of other information from parameter space, like horizon lines and vanishing points.

Re: Ancient secrets of computer vision

#29

Post by [deleted] »

csdvrx wrote: post #15

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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