This video is amazing. I need people to find this exact video. You explain it in a way that is easy understandable, unlike most of the other videos out there.
You all prolly dont give a damn but does anybody know of a way to log back into an instagram account?? I was dumb forgot the password. I would love any tips you can offer me.
@Luke Ruben I really appreciate your reply. I found the site thru google and Im in the hacking process atm. I see it takes quite some time so I will get back to you later when my account password hopefully is recovered.
One would think step 2 lead to the loss of information. When you bin the angle values and their intensities you discard their positions. Images are highly spatial data hence ignoring the placement of the gradients and only vectoring their angles and magnitudes seems like a way to cut corners. It must negatively impact the accuracy of neural networks being trained on these images.
nicey explained. you have gone to the depth. just clarify one my my doubt. at 8 15 , how each pixel can have gradient magnitude and direction independently .. plz . as gradient is for moving from one pixel to other. with respect to other pixel we can find. not for each pixel right
sir, what is on the vertical axis of histogram . it is supposed to be no. as you said how many gradients having particular anle. but in the video it is shown as magnitude... kindly clarify
Each 8x8 cell is represented as a 1x9 HOG vector. How are all these vectors fed to the SVM? Do we add them up and feed the aggregate 9d vector or are the vectors appended? Thanks!
The author who proposed HOG considered range that is from 0 to 180 and These are called “unsigned” gradients because a gradient and it’s negative is represented by the same numbers. ( gradient of 5 and -5 are essential same in magnitued but direction differs.. ..) In other words, a gradient arrow and the one 180 degrees opposite to it are considered the same. But, why not use the 0 - 360 degrees ? Empirically it has been shown that unsigned gradients work better than signed gradients for pedestrian detection. Some implementations of HOG will allow you to specify if you want to use signed gradients. Here the magnitude is playing more role in the final descriptor ...But, why not use the 0 - 360 degrees ? Empirically it has been shown that unsigned gradients work better than signed gradients for pedestrian detection. Some implementations of HOG will allow you to specify if you want to use signed gradients.
Nice video tutorial. I was hoping if you could give an advise a new method on how to detect rules of thirds on a photograph using OpenCV? Thanks in advance.
Thanks sir I have my 538 subscribers yet, I want to extend my channel subscribers, I have changed the my microphone, I am now providing a complete demo of the code along with voice description. Now I need your kind suggestions about how further changes should be made thanks 👍, kindly guide me how you changed the background to pure white, and what microphone you are using? Thanks
It's so pleasant to see someone who takes all aspects of the video so seriously as well as the enthusiasm and the explanation.
It's amazing. So pleasant to see the person with a huge sparks in the eyes explaining such non intuitive concept. Deserves millions of likes
This video is amazing. I need people to find this exact video. You explain it in a way that is easy understandable, unlike most of the other videos out there.
Definitely.
You all prolly dont give a damn but does anybody know of a way to log back into an instagram account??
I was dumb forgot the password. I would love any tips you can offer me.
@Marcus Blaze Instablaster :)
@Luke Ruben I really appreciate your reply. I found the site thru google and Im in the hacking process atm.
I see it takes quite some time so I will get back to you later when my account password hopefully is recovered.
@Luke Ruben it worked and I actually got access to my account again. I am so happy!
Thank you so much you really help me out !
This video is a just awesome, explained HOG in a very simple but detailed way
How are the HOG features visualized at 10:45? What is being plotted exactly?
This video is giving me a chance to survive my senior life. T_T
One would think step 2 lead to the loss of information. When you bin the angle values and their intensities you discard their positions. Images are highly spatial data hence ignoring the placement of the gradients and only vectoring their angles and magnitudes seems like a way to cut corners. It must negatively impact the accuracy of neural networks being trained on these images.
nicey explained. you have gone to the depth. just clarify one my my doubt. at 8 15 , how each pixel can have gradient magnitude and direction independently .. plz . as gradient is for moving from one pixel to other. with respect to other pixel we can find. not for each pixel right
sir, what is on the vertical axis of histogram . it is supposed to be no. as you said how many gradients having particular anle. but in the video it is shown as magnitude... kindly clarify
Very good explanation on image gradients.
Amazing video
Amazing
I have covered all concepts regarding histogram of oriented gradient
Amazing presentation
I am confused about the different variants of it, for example, UoCTTI , what do we mean by directed and und-rected gradients.
Each 8x8 cell is represented as a 1x9 HOG vector. How are all these vectors fed to the SVM? Do we add them up and feed the aggregate 9d vector or are the vectors appended? Thanks!
that's amazing video, your example help me alot to grasp this content
how can i calcule th histogram bleu
Wow, loved it, very nice explanation👍
Are HOG features currently the best method of image simplification?
No, there are other descriptors which are superior to HoG
@@mohalemolefe like what
@@jaiv you can try local descriptors such as SIFT and SURF
@@mohalemolefe orb and fast are much faster
I can't find next part of this video which is about code of it ???
From where did you get 50, 70, 100, 120 ? >>>> Did you assume them?
Amazing, please continue
How to create your own deep learning library?
Why 180 degree only ? .. should we apply this on 360 degree ?
it same cause we are dealing with positive values so the resultant will be same as 360=0 degrees.
@@hamzamahmood5232 Thanks
The author who proposed HOG considered range that is from 0 to 180 and These are called “unsigned” gradients because a gradient and it’s negative is represented by the same numbers. ( gradient of 5 and -5 are essential same in magnitued but direction differs.. ..) In other words, a gradient arrow and the one 180 degrees opposite to it are considered the same. But, why not use the 0 - 360 degrees ? Empirically it has been shown that unsigned gradients work better than signed gradients for pedestrian detection. Some implementations of HOG will allow you to specify if you want to use signed gradients. Here the magnitude is playing more role in the final descriptor ...But, why not use the 0 - 360 degrees ? Empirically it has been shown that unsigned gradients work better than signed gradients for pedestrian detection. Some implementations of HOG will allow you to specify if you want to use signed gradients.
btw, the above info is more explained in here -- www.learnopencv.com/histogram-of-oriented-gradients/
Nice video tutorial. I was hoping if you could give an advise a new method on how to detect rules of thirds on a photograph using OpenCV? Thanks in advance.
awesome video, love to see such well thought stuffs
very good lecture and very good energetic sir
Thanks sir I have my 538 subscribers yet, I want to extend my channel subscribers, I have changed the my microphone, I am now providing a complete demo of the code along with voice description. Now I need your kind suggestions about how further changes should be made thanks 👍, kindly guide me how you changed the background to pure white, and what microphone you are using? Thanks
does anyone know what's the deficiency of HOG feature?
great presenting!
Amazing explanation.
extraordinary, Thank You
matwk3tsh al2ek fel comments , we mts2lnesh ba3ml eh fel comments wana 3andy final bokra xD
very nice explaination thank you.
Well Explaind thank you!!
you are amazing keep going
and opencv?
OpenCV 3.x has HOG Descriptor class objects, which apparently you can feed the data from the output vectors into an SVM classifier class object.
Thank you sir 🙏🏼🙏🏼
thank you sir, helpful !!
this video will be perfect if content ONLY.
Thank you!
Excellent
perfect video
thanks for about this video.
Thank you Jaime Camil :)
He's Leonard
Thank you
amazing :)
thnx
Thanks!!!
why the fuck have you written opencv in the title if you have not used it anywhere.
Show off body language irritates lot