Lecture 05 - Scale-invariant Feature Transform (SIFT)

UCF Computer Vision Video Lectures 2012
Subject: Scale-invariant Feature Transform (SIFT)

[MrRoyzalis]

This professor has a talent for explaining things clearly and concisely.

[maximeprieur]

As a French student, i understand most of the video and find it way clearer than the orignal paper from Lowe. I think it is due to to quality of the presentation, the fluency of the teacher. Moreover you can feel that the teacher knows what he is talking about! :) Great video!

[alivaramesh]

One of the best lectures I have seen. Very clear explanation of all the technical steps.

[vbond]

Thanks to Dr Shah and the uploader. I just did this in class at my university, but it wasn't half as clear as this one. Very helpful. Amazing that an 8 year old recorded lecture is more relevant than a current live one

[ruairc]

I think this guy is great. This is the first time I have bothered to write something about anything on the internet apart from facebook.

[morganma]

A polished lecture given by a nice guy. Dr. Mubarak describe SIFT in a straightforward way.

[benjaminchen]

The part where he explained how the Laplacian of Gaussian works as a specific size of blob detector to achieve scale invariance at 18:19 was really helpful for me. My CV professor just skipped straight to difference of Gaussians and I didn't get why we used them or the benefit of it until now.

[talharehman]

BEST VIDEO ON SIFT! Explains the algorithm really well. Thank you so much.

[madhivarman]

Even though i am new to CV he clearly made me to understand about SIFT.. Thanks! professor.. :)

[samrockseagle]

after playing it almost 5 times over a month every thing is clear now.

[kelvinpaul]

This lecture really helped me acquire a better understanding of the SIFT algorithm. Thank you very much.

[kelvin.salton]

The best SIFT explanation I ever found. Thanks

[manikantabandla]

Where are we using DOG's calculated on downsampled image?

[JohnTheHumbleMan]

this is for the purpose of robustness. for a descriptor in image 1, we may find more than one pretty close matches in image 2, the closeness of these matches are measured by their Euclidean distance from the descriptor in image 1. The smaller the distance the better. The ratio is the ratio between the best match and 2nd best match.

[manikantabandla]

Does the scale refer to Sigma of Gaussian within an octave or downsampled image size?

[karthikavadivel]

Very clear explanation! I was very interested in this topic because of the way it was delivered.

[ShreyaGuptaella]

Thank you so much for these videos, very detailed and helpful :) please don't stop posting these lectures.

[buianhvu]

Thank you for your contribution, it's much easier for me than reading the paper myself.

[AhmadPTafti]

That is a great demonstration on the SIFT algorithm. Thanks much!

[nik]

This lecture is so good. I loved the way of explaining it by Dr. Shah