Computer Vision Basics: Hough Transform | By Dr. Ry @Stemplicity

[@haoshan7253]

I used hough transform in my work, now I understood the theory behind! Thank you so much Dr. Ry

[@theanamex9969]

This is excellent. Thank you for taking the time to explain why we are learning each step. Much better than my university class which I paid $1,000 for.

[@hermeticGreen]

"Simply put"

[@mohamedgbreel8618]

basically simply put ... تحيا مصر يا دكترة
THANKS for the super excellent explanation

[@anonymusunknown9199]

Very clear explanation. English is not my native language and you still made me understand it tired at 10pm. Excellent material, thank you Sir!

[@enricocaffagni6352]

Thank you very much Dr. Ry!

[@donaldduck5731]

Thank you, you are a gentleman and a scholar

[@annac625]

It's clear!! Thank you Dr. Ry. 😊

[@WahWahWeeWah]

This was the best video explaining this. Thank you for also explaining the Hough space.

[@parthsachan1130]

Damn he looks like Leonard from TBBT.

[@ronytesler]

Very well explained, thanks. I think the only thing that wasn't clear enough is how you know to go along with the blue and yellow lines. How the algorithm knows there are two horizontal lines.

[@ethtrader]

very clear explanation! you kinda remind me of leonard from bigbang theory 😅 brilliant people

[@Marin-ub5oj]

simply put, this is a very good video. Thats a joke, thank you so much

[@njbl6443]

Very helpful , thank u sir

[@johannreiter1087]

very clear and helpful explanation, thank you very much!

[@barrie1636]

You're the best dude

[@tinprojects695]

Great explanation!

[@lamaitani6992]

this is very helpful, thank you!

[@2806milinda]

Thank you. That was clear!!!

[@karakson]

Cool

[@mashmesh]

Thank you, so much! Awesome explanation!

[@marouaneazza3519]

merci

[@scorpion7434]

simply put, that was a great explanation :)

[@piereligio_ds]

Thank you, this is a clear explanation. Well done :)

[@professor-ryanahmed]

Glad you enjoyed it! thanks Piereligio

[@adamq272]

super

[@helpfulsources2813]

Thank you sir!!! Your explanations helped me to understand faster and easier!

[@sagarekbote2679]

just what i needed for my image processing coursework, thanks. And you kinda sound like nuclear Nadal from The dictator lol

[@anoopgeorge4597]

Very useful👍

[@michaelch3722]

Thank you sir!

[@sarerusoldone]

That was really easy to understand, thank you!

[@codyboudreau5544]

Can OpenCV be used for machine vision? I’m asking specifically for Keyence c-plug-ins. Any insight would be great.

[@TheFadime123]

very good

[@ruotianzhang3139]

brilliant

[@tubelight2026]

perfect

[@professor-ryanahmed]

Thanks. Much appreciated :)

[@GamerAvanzedPc]

How can I get the lines interceptions if I have the parametric form with rho (begins at the center of image) and theta? How to parametrize back if I have rho that is the distrance from the center of the image to the object?.

[@rohitghughtyal1679]

That was the best

[@aeshahalasmari1968]

Thank you for this useful tutorial

[@loc_research]

Hi Everyone: We have x.cos(theta) = p; y.sin(theta) = p so x.cos(theta) + ysin(theta) = 2d ???. How can we have that equation?

[@marcoottina654]

nope, those addends are equals to p/2.
You can have that equation only if you assume 2d=p, so d = p/2.

[@aqeelsajjad230]

Hi!
Have a good day.
Can you share these slides please.

[@etigorkhe41]

damn, you are too cute to focus on hough transform

[@kartikaykhattar96]

Kya chutiya kata

[@---_---_---_---_---_---]

This guy is (leonard + Raj ) from big bang theory