convolution of images

[@e3a87]

"I hope this helps man!!" goes directly into my lazy soul hwo never studies until the night of the exam! Thanks dude, it helps a lot

[@someoneelse707]

Same😂

[@emirhandemir3872]

Dude! We gotta do something about it. You probably graduated or dropped school but I at least need to quit this stupid habit of mine!

[@e3a87]

@@emirhandemir3872 ​ Bro, no one can destroy iron but its own rust !!
I don't know what is your goal and what are you going through but you need to realize one thing:
You are the only one that can make this work and you are the only one that can f*ck it up
You either control your mind or it controls you, you gotta choose...
But yeah I graduated thinking that the struggle will end with the degree but guess what... it never ends! This phenomenon of laziness is a perpetual war.
I hope this helps man!!

[@wolfabod9715]

@@e3a87 my exam is in 8 hours i really hope it does !!

[@XXxlightmarex]

3:51 "one second, let me just do a cheeky line of coke real quick"

[@triton62674]

Haha!

[@feelsunbreeze]

LMAOOOO

[@riseabovehate9476]

For convolution, you flip the mask horizontally as well as vertically and then computer the SOP. Since the mask, you have taken is symmetric Correlation and Convolution happen to be the same

[@tallurinani6059]

Bro, you are a savior. Thank you sooooo much. i didn't understand when i tried it fomr many websites and yt videos, yours just went straightly into the brain.. Thank you

[@tempdrive2342]

it was very useful put more videos

[@youtubegiuhotoinhe]

2024 and you are saving me sir! Thank you very much

[@alexmaingi9327]

Precise and understandable, Good job!!

[@DumplingWarrior]

I like the fact that I'm actually learning something while laughing lol, great video! you're funny

[@Noah_5x]

What a Great Man! YOU DID AMAZING

[@ajanthaalakkshmanan5854]

Awesome explanation

[@etziowingeler3173]

I like your laid back style Duderino, and it really helps

[@gravinpatel7700]

Thank you for giving such a simple example and explanation

[@StrangeParticl]

Thank you for the simple explanation of the convolution process. You did like it is a simple adding number to each other ...
That is grat, Sir.
Thank you so much agine

[@fase29]

thank you very much!

[@highelojungler]

lmao all these videos all professional and ur calling me dude and man, love you. take this like

[@karthikrajaloganathan3067]

Useful . Do some more videos

[@SirJonathonDanielGregorySrVthe]

You have no idea how fucking dull my lecturer is for this unit, this has helped a lot in avoiding something that probably would've been a half-hour explanation.

[@kuqiu5003]

Very clear interpretation. Thanks a million!

[@AzarZeynalli-mp5zj]

It helps more than you imagine. Thanks man:)

[@navigator171]

Finally somebody that did exactly what I need... Thanks man.

[@navigator171]

Você deveria fazer mais videos como esse, salvaria outras vidas.

[@freedom1880]

Thank you.I am deeply thankful.

[@OmarAhmed-tk1ow]

Great explanation

[@ancient_living]

Great work explaining that the size of the convolved image is decreased in dimensions. Keep up the good work.

[@abdelhafidbenchikh4426]

Very useful, thanks so much

[@briciomattosdeoliveira]

Wow, dude! That was a great explanation. I precisely understood the details of this process. I will apply that to all sorts of areas in my life. You rock, Dãmáiou!

[@Megan-gl7pi]

Thanks for explaining this super simply and quickly.

[@vagumanasa7044]

Literally this helped me a lott...thnq soo soo muchhh...

[@ajinzrathod]

I was laughing at 3:56. Really appreciate your efforts. Thanks

[@ashutoshmaurya9726]

Thanks for such a nice explanation .

[@ghufrongalih8354]

really help me man, thx
have a good day always

[@ifargantech]

This video is means alot to me. Thank you! Please make more videos on DIP

[@eyadbereh]

Bro, I'm having this for an exam tomorrow, and you just saved me from an M x N headache

[@Arnankhan]

Thank you! Very good tutorial.

[@EnglishRain]

This is so excellent thank you so so much

[@pawanyendigeri3157]

Nice video

[@osamamirza4220]

Thanks man!! this helped me a lot

[@zohaibkhanjadoon1255]

Tomorrow is my exam and this was what i needed and it helps alot thanks man

[@maheshbhosale1838]

this is not what convolution is, you need to flip the kernel first.
This is a correlation.

[@minhanhpham2508]

You save me in my midterm exam, thanks a lot!

[@drchastr208]

great video man

[@hieutran5099]

Thank you for the very clear and precise answer.

[@mohammadalifffirdausmohamm8125]

i love this video very good

[@notben49]

why are you black

[@avaneeshchavan8821]

Thanks, man i wasn't able to understand this in my school and now I understood it in 5 mins

[@JoeDhirk]

Thank you.

[@adl30]

You saved me from reading big book of convolution theory. Respect bro.

[@rahulkambale2627]

Great

[@glebignites5278]

Great explanation! Thank you very much.

[@Agreedtodisagree]

Thanks.

[@himanishmishra5490]

thanks brother
tomorrow is my exam and I just forgot the topic
it really helped

[@farhamrezqimaarifm8537]

thanks dude its help a lot

[@danusd1811]

Thanks a lot bro

[@AR-scorp]

Helped a lot. Thank you.

[@crazyvideos6474]

Great explanation but I think you are wrong. You are doing a correlation not a convolution

[@alexandredamiao1365]

Hi, thank you for the polite criticism. However, the operations I gave in the video are indeed used in convolution of images. Take a look at the explanations given in these links: web.pdx.edu/~jduh/courses/Archive/geog481w07/Students/Ludwig_ImageConvolution.pdf,
machinelearninguru.com/computer_vision/basics/convolution/image_convolution_1.html,
docs.gimp.org/en/plug-in-convmatrix.html

[@turbasdd]

Well, the thing is that this kernel you used as example is symmetric, because of that when you flip it horizontally and vertically (before the convolution) you get the exactly same kernel... Therefore, the way it is explained it works, but because the kernel is symmetric... and then it seems like a correlation as the other fellow mentioned.
You can see this in here machinelearninguru.com/computer_vision/basics/convolution/image_convolution_1.html
And you can also read about on chapter 3 of the book:
"Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods (www.amazon.com/Digital-Image-Processing-Rafael-Gonzalez/dp/0133356728)

[@ashavarghese8458]

Thanks

[@juaneugeniodebenedettti6187]

@@turbasdd touche

[@rionacko748]

@@turbasdd That link no longer working :(

[@juyeon_kim]

i think its correlation but thank you a lot. you helped me understand

[@張哲銘-k5v]

Good job man!!! It's useful.

[@kwekureginaldwade]

Great explanation !!!

[@abhijithbs997]

cool stuff dude.....
Thanks a lot

[@Sean-ow7qb]

amazing!

[@RaunakPinakJoshi]

Thanks man! Really helpful.

[@shwetabhsingh5437]

for sure bro, thanks

[@tawheedalabed6447]

many thanks realy it is very good

[@ashvinisharma8178]

Thank You dude

[@tymothylim6550]

Thank you very much for this video, Alexandre! It was a really simple and easy-to-understand video :)

[@rionacko748]

I love you man

[@waytoprogramming]

Well done.

[@arash4232]

Hi,
So what?
Should we normalise the calculated values? What colour does 514 refers to?

[@Serinebey13]

Thank you bro

[@ann61320]

terrible camerawork but solid explanation xD thanks bro!

[@gaoqiangzhou9960]

life saver!

[@aswinsurendrank6560]

this looks easy: those who know the real one💀

[@syedmhamudulhasanakash1675]

Best explanation ever man!

[@kavishkanilan9137]

😍

[@jyothish99]

Great explanation dude !!

[@mattkan2876]

Sir, your tutorial is nice in contents, but its better for you to buy a fixed frame to hold your mobile phone recorder

[@arturkot4108]

Thanks, man!

[@renasouza8261]

Very good vídeo mano

[@amartinezsilberstein]

chu tweny chu

[@wqkkta8787]

This helped me so much! Thank you!!!

[@chanhungnguyen3089]

I saw in many documents they say the multiplication between the kernel and each patch of the image matrix is a dot product. Can you explain it?

[@meirzilkha]

Thanks! This is great.

[@narendrakumar5504]

Clear and concise explanation

[@imranwahid9871]

Thanks a lot brother. It helped.

[@sumeshkumar9940]

good!!!

[@jcgonzalez9122]

dude this was awesome lol.

[@philipp849]

I cannot thank you enough.
You saved my butt.

[@shoruqm9753]

You saved my life man

[@zephjaz947]

Thank you for the precise explanation

[@DangThinh261]

Omgg thanx

[@alihan60]

Clear explanation! This is what i need! Thanks man you save the day!

[@Isa25monalisa]

THANK YOU!!! You helped me SO MUCH!!! Such an excellent explanation!

[@abdussami2998]

This one was good

[@ajanthaalakkshmanan5854]

Plz explain red deer optimization

[@metinyorgun1963]

You should scale pixel value because its cannot be greater than 255.

[@tldyesterday]

IT DID HELP MAAAN

[@childhoodgames1712]

Thank you , but the SUM of the results of the applied filter should be at the center pixel of the filter so, 649 is at the centered pixel

[@vloggetts]

I was thinking this same thing. It's the 32 that should be replaced by 649 after convolution, right? And to find the values of pixels closer to the edge after convolution, the kernel must be centred on these edge pixels and some kind of boundary strategy must be employed(eg. zero padding, wrap etc.)

[@markpx]

Since the convolution result produces numbers higher than 255, it no longer can be treated like an image?

[@saminbinkarim6962]

DUDE. This helped me pass. :D

[@alexandredamiao1365]

Very nice to hear! Congrats! :)

[@zhiyiwang6466]

still deserves an upvote

[@saifulahmad3770]

very helpful. Thank you

[@imagenigraphics]

Question: After applying convolution, is the resolution of the image reduced or maintained? If maintained, how when it looks like it was reduced?