Best filtering course I've seen :)

Some one asks about sigma and instructor explains it without any reproach...this is rare humility. Instructor is no doubt well experienced is immensely knowledgeable so as to be able to explain the concepts in lucid terms.

Awesome lectures! The most didactic and coolest material in that topic I found until now. Thank you professor Mubarak, and everyone involved. PS: On the example showed in 10:42, at row 3, column 6 I think there is a mistype on the slide, it needs a "-" signal (-20 instead of 20).

@AVI470

8 жыл бұрын

+Charles Prado thanks for saying that now I know I'm not alone.... :-)

Probably the best lecture series on computer vision out there!

@coffle1

8 жыл бұрын

Watch the 2014 series if you're going to go ahead with watching these videos though. It's a lot clearer!

this is really good, it puts Things in perspective.

Thanks Dr. Mubarak your lecture was very helpful

Awesome videos Prof Shah! I believe in 1:02:30 the x derivative filter should be [-1 1] instead of [1 -1] if you follow the same coordinate system as earlier in the video.

wow ! the way he explained ! really good lecture !

Hi, There should be a division of 2 at 10:11 for central difference?

Thanks a lot man :) , it helped me through my MV problems

gradient direction is fx/fy or fy/fx? because i hv studied in school time that y component/ x component gives the angle or the direction. if u can clear it how u took gradient direction?

For the correlation and convolution equations (19:31) do the i and j values represent the x and y dimensions of the kernel? so for a 3x3 kernel i and j would be 3 and 3?

@Lainlein

8 жыл бұрын

+Benjamin Montz I believe they are related to the pixel of the resulting image.. For the convolution, at least, if you put the indices of the kernel as (-1, -1) to (1,1) (or generally, centralize the kernel, so for a 5x5 kernel, indices should go from -2 to 2), then f*h(i,j) fulfills the equation. For the correlation I found a formula that is basically the same, but f is the kernel and h is the image. Alternatively, one can make an index shift and sum over f(k,l)h(k-i,l-i) with f being the image and h being the kernel. But I'm not 100% sure I understood it, either.

do we have lecture slides for this that I can print out?

It seems weird to me that x determines the row and y determines what column it is (0:03:38). When i think of x, it always spans from left to right and y (in 2D) spans from bottom to top when i think of math and top to bottom when i think of computer science. I realize that the assignment is completely arbitrary, but this declaration seems to add unnecessary confusion.

I have problem on understanding how the equation of correlation and convolution works (especially the i and j, where they come from?). As for k and l, it is just the coordinate of the pixel right? Hope someone can explain this to me.

@kshiray

8 жыл бұрын

+Hafiz Sofian (i, j) is the (row, column) coordinate in image f. (k,l) are the coordinates in kernel h. Correlation or convolution is applied at every pixel (i,j) in image f by iterating over every element (k,l) in kernel h.

At 13:33, there is an error in the slide (but not in the explanations). The gradient direction is atan of f_y over f_x, not f_x over f_y. Around 21', the definitions of correlation and convolution are fishy; it's not f * h that's defined, but f * h [i, j], and the indices i and j should be used to select cells in f, not in h.

@bat102

7 жыл бұрын

good point. I noticed these but since i'm not confident i thought maybe there's something unearthly about it that's beyond my scope. thanks for sharing

@tarunuday

6 жыл бұрын

Also at 10:16, the central difference is the given expression divided by 2

very nice content better than many overrated course..

Thank You sir. Great lecture

Thank you Dr. it is helpful

Does anyone understand what i and j represent in the convolution/correlation equations? There are summations over k and l but not over i and j. Thanks!

@childhoodgames1712

4 жыл бұрын

I really wondered about those two symbols !!

Really nice, thank you!

very clear and helpful, Thx!

can any one help me with gaussian filter with kernel convulation

@10:25 whle discussing forward / backward differences...... it would have been great to see the "Advantage" of doing such an operation ........ Else, lectures will be very dry.

What a nice lecture!!!!!!!

he starts the next lecture on edge detection at 53:18

thanks alot. it is too useful

what is the difference between operator and filter ... i'm some confused ???

Brandon Pelfrey You say the central derivative should be divided by 2, but why is the mask still [-1 0 1]? It is stepping over 2 pixels, is delta x not 2 then?

@LoganDunbar

9 жыл бұрын

And if you take a look here www.holoborodko.com/pavel/numerical-methods/numerical-derivative/central-differences/ the formula he derives ends up being divided by 2h, so I'm a bit confused how we can just find the difference of the previous and next pixel in our masks, without halving the result. Thanks!

@thecguyl3056

9 жыл бұрын

Logan Dunbar i think that he made a mistake because by logic and even on wikipedia in the central difference there is a division by 2*delta x which in our case is one...

@lyyau9689

8 жыл бұрын

+theCguy L Yes, agree with you. The general form should be f(x+h1) - f(x-h2)/(h1+h2). Or the equation change to: f(x+1/2) - f(x-1/2) to be correct at 10:19, where f(x+1/2) is the mid point of f(x+1) and f(x), f(x-1/2) is the mid point of f(x-1) and f(x). f(x+1/2) should equal to (f(x+1) - f(x))/2, f(x-1/2) = (f(x)-f(x-1))/2 => f(x+1/2) - f(x-1/2) = (f(x+1) - f(x))/2 - (f(x) - f(x-1))/2 = (f(x+1) - f(x-1) / 2

@philippelandry5209

8 жыл бұрын

Indeed, if you need accuracy for differential equation system solving (ex: fluid simulations), you should use the true central difference mask is [-0.5 0 -0.5], but in detection applications, where you are searching for extremums or zero crossings, you can drop the division.

really helpful!

Really helpful

Yes.

I have a very fundamental question. An image (original) comes with inherent NOISE. In all these discussions .. I observe that a model for NOISE is added (superimposed) on the original image. Why should you voluntarily ADD NOISE and do all these unnecessary unwanted operations (filtering, averaging etc) instead of removing the INHERENT NOISE in the original image ? All these operations suggested are only trying to remove the NOISE voluntarily added to the original image. To make it simple .. Why add Noise and try to remove it ?

@sarathsivaprasad5712

7 жыл бұрын

We are not adding noise to an image.The noisy image is represented as pure image + noise. Its just modeling the input (assuming all noise i the image are just additions over pure image ). In some scenarios it is not that straight forward. I guess that answers ur question :)

@EngBandar1

6 жыл бұрын

You are mixing two different scenarios. In reality, when you take a picture, usually the snapshot has imperfections. You can observe that in cheap cameras when the image contains imperfections or noise for short. Modern cameras like expensive cameras, you barely notice any thing wrong with the image and you will be happy with the high quality (i.e. a lot of filtering under the hood). The second scenario in which researchers need first to generate noise which should resemble the true noise (i.e. at an approximated level otherwise if we know it then we can remove it) and then add it to the images. They need to do this in order to test their filters and conclude if the filters are capable of handling real scenarios. Modeling noise is extremely important in all engineering fields alike.

like this, very helpful

in center derivative , I think, the value should be (f(x+1) - f(x-1))/2, because delX = 2 here. can anyone comment on it

@malharjajoo7393

7 жыл бұрын

This is a fair point , which is what I might have done as well, but I believe here he is trying to provide an intuition of the derivative process in images. An alternative way of thinking can be - Since you want to detect edges , you want to look at the two sides of a pixel , hence you ignore it ( by placing a zero at the pixel location ,like in centre derivate mask ) and hence you would have delX = 1 ...

@naiden100

6 жыл бұрын

I think it too

Why the gray level is from 0 to 250? Shouldn't it be from 0 to 255 (i.e. 2^8 = 256 ) at 1:13 min.

@ranxie9593

10 жыл бұрын

Prob his typos

@CyberWorx

9 жыл бұрын

Bandar the full scale is not shown in the slide ..only shown till 250..scale continues upwards..nextslide shows a longer full scale

This lecture series is excellent, thank you for sharing them! I found the jump from the image derivative to correlation confusing--it made more sense, though, once the discussion got around to the topic of image filters.

@kiangus3491

2 жыл бұрын

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@emersonsaint8164

2 жыл бұрын

@Kian Gus Instablaster :)

@kiangus3491

2 жыл бұрын

@Emerson Saint i really appreciate your reply. I got to the site through google and im in the hacking process atm. I see it takes a while so I will get back to you later when my account password hopefully is recovered.

@kiangus3491

2 жыл бұрын

@Emerson Saint It did the trick and I finally got access to my account again. Im so happy:D Thank you so much you saved my ass!

@emersonsaint8164

2 жыл бұрын

@Kian Gus Happy to help :D

I don't get the derivative mask part! how does it work?

@MrTienDH

9 жыл бұрын

Ahmad Rashad Same to me, it's confused

@TheHalalPolice

9 жыл бұрын

tien dh got it, you use this linear algebra convention to calculate derivatives, so, if we want to calculate delta I(x) as I(x+1)-I(x) we calculate it as I(x+1) + (-I(x)) where I(x) is intensity value of a pixel number x now let us assume we have a matrix nx2 and we want to get its partial derivative wrt x axis, if we multiply this matrix by the vector [-1,1] we will have a vector I' with n dimensions, having each element as I(x+1)-I(x) the use of this filter is by repeating the above operation for each column from 2 to m where m is number of columns, as if we are sliding this vector across x axis we can perform the same operation across y axis, or z-axis if this is a volume image

@coffle1

8 жыл бұрын

+Ahmad Rashad Was looking at it for probably 15 min, no joke. The weird thing about it is the masks aren't used as matrices being multiplied, but rather just as an overlay on the corresponding positions, summed up.. So at 16:00, he says "-1 to 10" three times because he's multiplying each mask position to the corresponding number in the image. He then sums up the column, and divides by 3 to average. I don't know why the operation is defined so confusing.

@ramandutt3646

6 жыл бұрын

I don't think he was following the rules of matrix multiplication. He was just multiplying corresponding elements.

good!

In the video. kzread.info/dash/bejne/Y4h816WIe7HUZrg.htmlm14s. The slide indicates .... function f = image ... f = kernel . Is that correct?

@ctltpro

9 жыл бұрын

no it was a mistake. The prof. didn't notice it. It should have been h = kernel.

thanks sir

Who the hell is Pavel Babenko...

The explanation is quite good.

Why there is not English Subtitle . My English Listening is so poor. I really admire those who are speak English.

In 19:10 f = Image and h = Kernel

dont like the ads..

www.cs.cornell.edu/courses/cs6670/2011sp/lectures/lec02_filter.pdf for better understanding the video

Watch on 1.5x, it's a lot better.

So... every teacher uses the same ppt.

most confused prof ever!! DO NOT WATCH!!

@everblut

10 жыл бұрын

explain yourself.

@amarug

10 жыл бұрын

seems extremely clear to me.

@EngBandar1

10 жыл бұрын

@amarug, I agreed.

@mnzavachris5423

6 жыл бұрын

can any any prof from anywhere just do this stuff as in uploading videos to teach???

@shashanksahu1971

6 жыл бұрын

Knowledge needs patience. I think this tutorial is amazing and upto the point.