@@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!!
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
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
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
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!
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.
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
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)
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.)
@@AhmadImtiaz320 It will be different when using other masks. The convoluted matrix will be smaller when using a 5x5 mask. And the result of course depends on the numbers used in the mask^^
Then give a C program to this question Prepare a matrix using dynamic memory allocation of size 7x7 with random characters. Check for valid English words by convovling a 1x3 kernel mask over the matrix
"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
Same😂
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!
@@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!!
@@e3a87 my exam is in 8 hours i really hope it does !!
3:51 "one second, let me just do a cheeky line of coke real quick"
Haha!
LMAOOOO
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
it was very useful put more videos
I like the fact that I'm actually learning something while laughing lol, great video! you're funny
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
Precise and understandable, Good job!!
I like your laid back style Duderino, and it really helps
lmao all these videos all professional and ur calling me dude and man, love you. take this like
Useful . Do some more videos
What a Great Man! YOU DID AMAZING
Thank you for giving such a simple example and explanation
Nice video
This video is means alot to me. Thank you! Please make more videos on DIP
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
Tomorrow is my exam and this was what i needed and it helps alot thanks man
great video man
really help me man, thx
have a good day always
I was laughing at 3:56. Really appreciate your efforts. Thanks
Bro, I'm having this for an exam tomorrow, and you just saved me from an M x N headache
It helps more than you imagine. Thanks man:)
Awesome explanation
Literally this helped me a lott...thnq soo soo muchhh...
Finally somebody that did exactly what I need... Thanks man.
Você deveria fazer mais videos como esse, salvaria outras vidas.
You save me in my midterm exam, thanks a lot!
Very useful, thanks so much
i love this video very good
why are you black
This is so excellent thank you so so much
thank you very much!
Thanks man!! this helped me a lot
2024 and you are saving me sir! Thank you very much
thanks dude its help a lot
Thank you! Very good tutorial.
thanks brother
tomorrow is my exam and I just forgot the topic
it really helped
Thanks for such a nice explanation .
Great explanation
Thanks, man i wasn't able to understand this in my school and now I understood it in 5 mins
Thanks for explaining this super simply and quickly.
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!
Very clear interpretation. Thanks a million!
Thank you.I am deeply thankful.
Great work explaining that the size of the convolved image is decreased in dimensions. Keep up the good work.
Good job man!!! It's useful.
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.
cool stuff dude.....
Thanks a lot
You saved me from reading big book of convolution theory. Respect bro.
amazing!
Helped a lot. Thank you.
Thanks a lot bro
many thanks realy it is very good
Great
Well done.
i think its correlation but thank you a lot. you helped me understand
Thanks man! Really helpful.
Very good vídeo mano
Great explanation! Thank you very much.
I love you man
Thank you.
Great explanation !!!
Sir, your tutorial is nice in contents, but its better for you to buy a fixed frame to hold your mobile phone recorder
Thank You dude
Thank you very much for this video, Alexandre! It was a really simple and easy-to-understand video :)
dude this was awesome lol.
Thank you for the very clear and precise answer.
Thanks a lot brother. It helped.
Thanks.
Best explanation ever man!
for sure bro, thanks
This helped me so much! Thank you!!!
good!!!
Great explanation but I think you are wrong. You are doing a correlation not a convolution
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
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)
Thanks
@@turbasdd touche
@@turbasdd That link no longer working :(
Great explanation dude !!
Thanks, man!
Thanks! This is great.
Thank you bro
this is not what convolution is, you need to flip the kernel first.
This is a correlation.
He technically flipped it by multiplying lines of each matrice
Omgg thanx
life saver!
You saved my life man
I cannot thank you enough.
You saved my butt.
DUDE. This helped me pass. :D
Very nice to hear! Congrats! :)
THANK YOU!!! You helped me SO MUCH!!! Such an excellent explanation!
Plz explain red deer optimization
This one was good
IT DID HELP MAAAN
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
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.)
Clear explanation! This is what i need! Thanks man you save the day!
terrible camerawork but solid explanation xD thanks bro!
Thank you for the precise explanation
Hi,
So what?
Should we normalise the calculated values? What colour does 514 refers to?
how do you do it with circular indexing?
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?
Thanks man ...it was really quick
I see you bro..XD
@@ManishKumar-iy4vx Brooooo
chu tweny chu
For larger image, do we have to stick to 3x3 mask ? Or the mask increases with the size of image?
You don't have to stick to a 3x3 mask. It can be whatever size
@@marcelmcjackson4257 but the result will be different each time . Right?
@@AhmadImtiaz320 It will be different when using other masks. The convoluted matrix will be smaller when using a 5x5 mask. And the result of course depends on the numbers used in the mask^^
@@marcelmcjackson4257 Thank you :)
Thank you so much this made it seem so simple lol
please how do you convolve and wrap around image cyclically??!
Then give a C program to this question
Prepare a matrix using dynamic memory allocation of size 7x7 with random characters. Check for valid English words by convovling a 1x3 kernel mask over the matrix
😍
You should scale pixel value because its cannot be greater than 255.
this looks easy: those who know the real one💀