[@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 !!

[@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

[@XXxlightmarex]

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

[@triton62674]

Haha!

[@feelsunbreeze]

LMAOOOO

[@brimamedou]

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!

[@ancient_living]

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

[@DumplingWarrior]

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

[@kuqiu5003]

Very clear interpretation. Thanks a million!

[@etziowingeler3173]

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

[@hieutran5099]

Thank you for the very clear and precise answer.

[@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

[@tymothylim6550]

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

[@ifargantech]

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

[@highelojungler]

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

[@alihan60]

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

[@gravinpatel7700]

Thank you for giving such a simple example and explanation

[@zephjaz947]

Thank you for the precise explanation

[@eyadbereh]

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

[@jcgonzalez9122]

dude this was awesome lol.

[@Isa25monalisa]

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

[@navigator171]

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

[@navigator171]

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

[@adl30]

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

[@osamamirza4220]

Thanks man!! this helped me a lot

[@vagumanasa7044]

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

[@wqkkta8787]

This helped me so much! Thank you!!!

[@Megan-gl7pi]

Thanks for explaining this super simply and quickly.

[@AzarZeynalli-mp5zj]

It helps more than you imagine. Thanks man:)

[@alexmaingi9327]

Precise and understandable, Good job!!

[@glebignites5278]

Great explanation! Thank you very much.

[@ashutoshmaurya9726]

Thanks for such a nice explanation .

[@tempdrive2342]

it was very useful put more videos

[@ilegadh]

Thanks, this really helped me understanding!

[@imagenigraphics]

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

[@minhanhpham2508]

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

[@metinyorgun1963]

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

[@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.

[@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

[@drchastr208]

great video man

[@fase29]

thank you very much!

[@markpx]

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

[@MattoUchiha]

Thank you so much this made it seem so simple lol

[@chvrleshd]

please how do you convolve and wrap around image cyclically??!

[@narendrakumar5504]

Clear and concise explanation

[@chvrleshd]

please how do you convolve and wrap around image cyclically??

[@alexandredamiao1365]

Hi Charleone. I've never had to implement a cyclical (circular) convolution. I assume you're trying to perform a 2 dimensional FFT.
I believe the idea is to do exactly as I explained in the video when the kernel is completely within the image. Once you get to a point where the kernel edges are outside of the image on one side, you take those edge values and multiply by the pixels of the other side of the image (at the same height/row).
The following links may help you: (go through the answers, they are insightful!)
dsp.stackexchange.com/questions/6302/circular-and-linear-convolution

[@ajanthaalakkshmanan5854]

Awesome explanation

[@rahulkambale2627]

Great

[@ghufrongalih8354]

really help me man, thx
have a good day always

[@meirzilkha]

Thanks! This is great.

[@I34R]

how do you do it with circular indexing?

[@avaneeshchavan8821]

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

[@freedom1880]

Thank you.I am deeply thankful.

[@youtubegiuhotoinhe]

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

[@philipp849]

I cannot thank you enough.
You saved my butt.

[@AR-scorp]

Helped a lot. 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?

[@Sean-ow7qb]

amazing!

[@EnglishRain]

This is so excellent thank you so so much

[@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 :(

[@renasouza8261]

Very good vídeo mano

[@arash4232]

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

[@farhamrezqimaarifm8537]

thanks dude its help a lot

[@user-dg4oo8tn3w]

Good job man!!! It's useful.

[@Arnankhan]

Thank you! Very good tutorial.

[@kwekureginaldwade]

Great explanation !!!

[@zhiyiwang6466]

still deserves an upvote

[@MrRobi10]

Hello, I have an 11×11 image having in its center a 5×5 square, the image it's noiseless and I don't know how to compute the gradient of the image function given by the compass operator for this image. If I remember correctly, I should use a derivative, but I don't know exactly what and how can I use it. Can you please help me?

[@jyothish99]

Great explanation dude !!

[@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.)

[@muneebabbas2424]

I'm working on an example similar to this, when using the kernal on the image matrix I got an output of -2 (some of the values in the kernal were negative), I'm not sure if you can get a negative value for the output but what would that mean for the image matrix if, when convoluted, a pixel becomes a negative value?

[@fiethsing9988]

For those wondering, when you get a negative value, you just put the lowest value the pixel can be. So if you got a greyscale image and it's pixel values range from 0-255, you'd put 0.

[@shwetabhsingh5437]

for sure bro, thanks

[@syedmhamudulhasanakash1675]

Best explanation ever man!

[@abdelhafidbenchikh4426]

Very useful, thanks so much

[@saifulahmad3770]

very helpful. Thank you

[@aswinsurendrank6560]

this looks easy: those who know the real one💀

[@athenanaki94]

Before start doing this process, I have to apply zero padding on the image, right?

[@ArminXtosis]

It depends on what you want. If you want the kernel applied to the edge of the image as well then yes you should pad it.

[@tawheedalabed6447]

many thanks realy it is very good

[@stavrosk.3773]

thanks DUDE

[@waytoprogramming]

Well done.

[@ajanthaalakkshmanan5854]

Plz explain red deer optimization

[@juyeon_kim]

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

[@mattkan2876]

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

[@mohammadalifffirdausmohamm8125]

i love this video very good

[@notben49]

why are you black

[@maheshbhosale1838]

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

[@JoeDhirk]

Thank you.

[@raze7x]

explanation was good, but use some sort of tripod for the camera next time! thx for the lesson

[@alkodjdjd]

Ajudou muito a pesar de ter ficado zonzo de tanto a camera se mexer :)

[@RaunakPinakJoshi]

Thanks man! Really helpful.

[@user-ok3qx5kf4o]

Thank you🌸

[@pawanyendigeri3157]

Nice video

[@coccosapiens]

a pixel greater than 255??

[@ubaidurrehman4377]

I guess, he didn't divide by the sum of filter matrix i.e (649/4) = 162.25

[@arturkot4108]

Thanks, man!

[@OmarAhmed-tk1ow]

Great explanation

[@abhijithbs997]

cool stuff dude.....
Thanks a lot

[@dhimanroy2831]

thanks man

[@sumeshkumar9940]

good!!!

[@saminbinkarim6962]

DUDE. This helped me pass. :D

[@alexandredamiao1365]

Very nice to hear! Congrats! :)

[@imranwahid9871]

Thanks a lot brother. It helped.

[@rionacko748]

I love you man

[@vibithas87]

What is the purpose of the number we are putting inside the box

[@augusto786]

Are you Brasilean? I can notice a Brasilean accent in your voice background.

[@alexandredamiao1365]

Sou sim! Dá uma olhada nesse vídeo se tiver interesse: ua-cam.com/video/1Ad6cH_7DQ8/v-deo.html

[@danusd1811]

Thanks a lot bro

[@omsaichand9798]

I think each value has to be divided by d sum of kernel

[@andreaf8940]

no, that's not what you do when doing cross-correlation or convolution.