UCF Computer Vision Video Lectures 2012 Instructor: Dr. Mubarak Shah (vision.eecs.ucf.edu/faculty/sh...) Subject: Interest Point Detection Presentation: crcv.ucf.edu/courses/CAP5415/F...
He is the best... Exactly the whole series. Please upload more such research oriented concepts. Very good for researchers starting their research. Thank you.
Thank you for posting these videos. I was trying to learn the contents from slides that just contain mathematical formulas without explanations. I was very lost. (a German computer science student)
whatever you do , whoever you become (as a teacher) a shade of being a Pakistani teacher always go with you. not denying that the overall video is awesome but jis tarah kuch baton ko slides dkh k pela hai
Hello. Thank you for lectures. There is an error in description. Presenation link points to lecture 3. The right link is /courses/CAP5415/Fall2012/Lecture-4-Harris.pdf
Can anyone explain why we can use Gaussian window there? I think that a uniform window is enough because we just use it as a kernel to look for a corner reason in our image. In my opinion, the gaussian window is some kind of weighted average one which may affect on our algorithm.
hello can any one help me about >>> why ( if the result of correlation is high so there are similarity ) i think it is possible to be high even if there is no similarity
20:42 what if there comes noise. then the selected point as point of interest will not be visible. and we have discussed that it should be noise robust.
In the last slice, it says "convolve these three images with a large Gaussian (window)", what do you mean by large Gaussian window, is it in terms of window size or sigma of gaussian, also, what is considered large in practice? Thank you
never mind, since large sigma indicates large window size, i.e. should approach to 0 at the edge. The Szeliski book says it works best when sigma=1 for the derivative gaussian and sigma=2 for integration gaussian (gaussian used for window function w(x,y))
Thank you! You are helping education worldwide! - by a Brazilian student
The first time I tried to understand harris corner, it cost me a few days and still out of blue. Now it is very comprehensive, thank you sir!
He is the best... Exactly the whole series. Please upload more such research oriented concepts. Very good for researchers starting their research. Thank you.
Hi Mr Ansuman, may I know what is your research area?
Thank you Dr Mubarak Shah, for sharing videos in youtube
That is one of a GOOD lecture.
Thank you, this video helped me understand the math behind harris.
Thank you for posting these videos. I was trying to learn the contents from slides that just contain mathematical formulas without explanations. I was very lost. (a German computer science student)
Thanks for this valuable collection of Machine vision lectures ......it helped me alot
Can you get the source of this information. Please Help
For the first time in my life feeling grateful to a Pakistani-- A poor Indian student who doesn't have much exposure.
+Minus Infinity Same here man, this guy is a very good teacher.
Your lecture saved my ass from my mid term exam.
whatever you do , whoever you become (as a teacher) a shade of being a Pakistani teacher always go with you. not denying that the overall video is awesome but jis tarah kuch baton ko slides dkh k pela hai
As a part of my Ph.D. course, I studied one subject here from a Professor of a German university. He also does the same !!
what a good video! Thanks a lot!
Thank you Dr Mubarak Shah, Allahu yubarik fik.
Can you get the source of this information. Please Help
Amazing!! Thank you!
thanks very much; it is so helpful
thanks for uploading.
Thank you so much~
Please i am very much interested to know about threshold to detect corners. How can we set threshold value to detect corners?
very good lectures
if there is a block diagram of interest point detection process?
Hello. Thank you for lectures.
There is an error in description. Presenation link points to lecture 3. The right link is /courses/CAP5415/Fall2012/Lecture-4-Harris.pdf
what is the dimension of the M matrix ?
thank you
Hello Sir
Thank you for the useful information about interest point detection
Can you get the source of this information. Please Help
Can anyone explain why we can use Gaussian window there? I think that a uniform window is enough because we just use it as a kernel to look for a corner reason in our image. In my opinion, the gaussian window is some kind of weighted average one which may affect on our algorithm.
Thx ;)
How is 27:43 equation of an ellipse ?!
thanks, how did you calculate the derivative of I(x+y,u+v) please 23:44
Taylor series I(x+u,y+v)=I(x,y)+differentiation of inside wrt x u*I(x+u-x)/dx+differentiation of inside wrt to y ie v*I(y+v-v)/dy
@@rohetoric Can you get the source of this information. Please Help
@@zahraaalmhana9157 Its Taylor series.. You can google it and check.. Its a common formula..
thanks professors , but why you used taylor series
R > 0, does not bring only real corners. it also include fake corners. so how can it say R > 0 for corners?
I think explaining the formula of the Corner Response would have been helpful. Many times just assuming formulas does not help convince the viewer.
hello can any one help me about >>> why ( if the result of correlation is high so there are similarity ) i think it is possible to be high even if there is no similarity
20:42 what if there comes noise. then the selected point as point of interest will not be visible. and we have discussed that it should be noise robust.
Waseem Ullah did you know how he calculated the derivative of I(x+y,u+v) please 23:44
yes. that was easy. I(x,y) was canceled with -I(x,y). and wrote the remaining term
Actually, I'm talking about the shifted intensity term how did he get its derivation using Taylor's formula I want to know even it is easy for you !!
+soumia soumia, Wikipedia article on Corner Detection has much more elaborate description
Where I find SURF algorithm mathamatical implementation
What is K & L in correlation?
How to get his slides plx help
What's the full link??
Can you get the source of this information. Please Help
In the last slice, it says "convolve these three images with a large Gaussian (window)", what do you mean by large Gaussian window, is it in terms of window size or sigma of gaussian, also, what is considered large in practice? Thank you
never mind, since large sigma indicates large window size, i.e. should approach to 0 at the edge. The Szeliski book says it works best when sigma=1 for the derivative gaussian and sigma=2 for integration gaussian (gaussian used for window function w(x,y))
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