Thank you very much for your explanation. It was exhaustive and very comprehensive. The graphics helped me a lot to understand the entire process. My compliments!
The advantage of having to explain something like this in an exam is that the examiner probably knows already how it works and he just has to see that you understand it too. And he can ask you for clarification where he's unsure if you understood it. :-)
Hello! I am currently working on a lane detecting program and I was having trouble understanding the concept of the Hough Line detection algorithm. You explained it very well. That you so much!!!!!!
This is amazing. I mean my prof hasn't done it half that good in double the time. Just one little detail I had initially trouble with: we do not use y = a*x+b because a is infinite for a line that has 90 degrees.
This video was soooooooo useful! I have a paper due tomorrow on Hough Transform, and how it compares to Inverted Gradient Hash Maps. This was amazingly helpful. I was on struggle street. Thank you!!
What an absolutely great explanation. The visual of iterating over each pixel to find the whitest spots in the Hough Space was particularly helpful. Thank you!
Amazing animation! For me its very hard to fallow only formulas and a lot of words. But that animation made me understand the idea behind all this! Thank you! :D
Thanks for your comment. Please like/share/subscribe. I feel the same as you, so I try hard to create animations for the theories that I suffer a little bit to understand :) Regards
Thanks a lot for your comment. Please like/share/subscribe. It is not the first time that people talks about Javier Bardem :) But probably is because I record in the silence of the night. Regards
So ,it's like we have points on XY plane and when we transform the y=ax+b in b=-ax+y and plot that b=-ax+y in ab plane and do it for all the given points of x,y and when we have so many lines in ab plane intersecting a common point we take it and use it as xy and plot a line that will fit maximum number of points ? is that so ?
Hi, I am not getting the exact idea behind this. So first, we are detecting edges using canny or any algorithms which leaves us with edges in the image. Now, edges can be lines in the image which is what you explained as y = ax +b where a is the slope and b is the intercept of the line. In the fitter space, I will call it as hough space, my question is "How points in the line equation of the edge becomes line in the hough space"?
Thanks for your question Please subscribe to my channel You are right, first detect edges, then use the edges to find lines. Maybe the point that you did not understand is the accumulator (which creates the figure), the more two points intercept in the hough space, the more points are aligned in the original one. This is why the location of the lines are in the maximum points of the hough image. Regards
At 1:55 how can the green line have a positive slope? Both (Xi, Yi) and (Xj, Yj) have a negative value for a, so in feature space both lines should have a positive slope right? I do believe that your eplanation is correct, however I still have trouble to grasp this transformation. It doesn't make sense to me how the lines are drawn in feature space.
Can you explain me how the another way r = x cos(theta) + y sin(theta) arrived ? I assume r is the shortest distance from origin to the line. I came across henesse normal form and the shortest distance between points and planes but I still couldn't relate with the above equation. Can you explain it to me how we arrived/derived to the above equation ? Edit : found it after some deep diving into basics People who wants to know how this equation originated, the term we're looking to search and learn for is "normal form representation of a line"
So, I have a question. On the Generalised Hough Transform, the difference is that we don't compare with line equations. Instead we compare with the target form/path - for every rotation and scale possible. Is that it? If so, it looks very heavy to calculate that.
If I understood your question, yes you need to compute several values to find the lines. That's why we create the matrix, and use the maximum values on the matrix to point to lines. It is expensive computationally. Regards
How do you know where the lines start and stop? I mean, you have detected that there is a line with r=xcos30 + ysin30. Fine. Now there are some 4 sections that lay on same line (i.e. there are gaps). How do you know where are the boundaries?
Hi Gregory, thanks for your feedback. Please like/share and subscribe. To discover the start/end points, recall that we applied the Hough transform in the image of edges (1 for edge, 0 for no edge). So with the line equation, you can iterate over x, and find the first and last y with edges. It is a basic suggestion. Regards
This is one good explanation. I just have one question. Why is a point in xy plane a line in ab plane and not any other shape? Same thing or a circle? Please let me know.
Dear friend, thanks for your feedback. Please like/share the video and subscribe. Recall that the shapes we showed are for the conversion from xy space to the line equation using radius and angle (2:56). The variation of radius and angle produces the shapes. A point in xy plane is not a line in ab plane. Indeed two points in xy plane that should form a line, are represented by a single point in ab plane. If more groups of points in xy plane are part of the same line, then the point will bright more in the resultant image. Regards
Hi, I am getting this error in OpenCV probablistic hough lines. please help to resolve :- ---> for x1, y1, x2, y2 in lines[0]: cv2.line(image, (x1,y1), (x2,y2),(0,255,0), 3) ValueError: not enough values to unpack (expected 4, got 2)
Thales Sehn Körting sir I am using matlab , the hough function returns rho ,theta and H matrix ,as u expalined i understood what are rho and theta but what is H ?if u please elaborate it
+Mallesh Kumar thanks for your feedback. Please like/share the video and subscribe to my channel. I will put your suggestion in my videos wish list. Regards
Thanks for your feedback, please like/share the video and subscribe to my channel. please email me (tkorting at gmail) and I send you the code (in octave) regards
thanks for your feedback, please like/share and subscribe. I have used the 1st edition (in portuguese) from 2000, but to cite in the video, I had to find an english version. Regards
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YOUR video is gold sir, thank you very much !
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The animation is awesome! I have a very visual memory, and this helped me A LOT to understand exactly what's going on. Thank you so much!!!
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Finally! It made sense both visually & mathematically. The animation of it's working is the best part. Thanks for sharing.
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Thank you very much for the effort you have taken to explain the subject in a simplified manner..
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A very clear and concise explanation of the Hough Transform.
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Thank you very much for your explanation. It was exhaustive and very comprehensive. The graphics helped me a lot to understand the entire process. My compliments!
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that animation made perfect sense and know i finally get it but how the hell do i explain that in an exam hahaha
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I hope you have managed to explain in your exam ;)
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same here :D
The advantage of having to explain something like this in an exam is that the examiner probably knows already how it works and he just has to see that you understand it too. And he can ask you for clarification where he's unsure if you understood it. :-)
Well done, literally the best explanation I have found online.
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The explanation with the animation was so good. Thank you!
your video is better than some textbooks, thank you sir!
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This is exactly how the universities should teach. Thank you very very much for this one.
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Thank you! The visualization makes the workings of the algorithm a lot more clear.
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Finally i got it! I had read the same book but now i see what i was doing wrong. Thank you mate.
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Hello! I am currently working on a lane detecting program and I was having trouble understanding the concept of the Hough Line detection algorithm. You explained it very well. That you so much!!!!!!
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This visualization really helped me understand the Hough Transform. Thanks!
+Joseph Sheedy thanks for your feedback,
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This is amazing. I mean my prof hasn't done it half that good in double the time.
Just one little detail I had initially trouble with: we do not use y = a*x+b because a is infinite for a line that has 90 degrees.
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A vertical line hasn't a =1 ?
@@aitorjara100 No it does not. A 45 degree diagonal line has a=1.
@@hermeticGreen Jesus.... sorry I don't know what the hell was I thinking when I posted it
@@aitorjara100 haha no worries
This video was soooooooo useful! I have a paper due tomorrow on Hough Transform, and how it compares to Inverted Gradient Hash Maps.
This was amazingly helpful. I was on struggle street. Thank you!!
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What an absolutely great explanation. The visual of iterating over each pixel to find the whitest spots in the Hough Space was particularly helpful. Thank you!
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Best explanation and visualization I have ever seen! Thanks so much for this.
Amazing animation! For me its very hard to fallow only formulas and a lot of words. But that animation made me understand the idea behind all this! Thank you! :D
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I feel the same as you, so I try hard to create animations for the theories that I suffer a little bit to understand :)
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Such a great explanation in a very short time! Thank you!
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the lines in the Hough space are beautiful
Clean and simple explanation.. Thanks!!!
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Hmm.. i'm studying for a test. I didn't understand my script at all... but now it's pretty simple. Thank you Thales.
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Excellent visualization. Thank you very much.
Great explanation, OpenCV tutorials could definitely point to your videos!
Thanks a lot. I would live to have your suggestion true.
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wow,this animation is great to help me undertstand the Hough Transform!!!!
Thanks Thales. Most informative channel about AI. Please supply +1000 videos! Oh! What a wonderful day!
+Fatih ÖZTÜRK many Thanks For your ositive feedback
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l best regards
this help me with my college project.
thank you sir for sharing.
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great explanation and the visualization helped me a lot. Thank you for this great content
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Excellent explanation! Thank you very much, sir!
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Wonderfully explained sir, thanks a lot..it was soo helpful.
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Thank you so much. Everything is crystal clear now.
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Thank you for this video! The animation is very helpful and your explanation is easy to follow. You also sound a bit like Javier Bardem!
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It is not the first time that people talks about Javier Bardem :) But probably is because I record in the silence of the night.
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Very very helpful and clear!
Excellent explanation. Thanks!
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Great explanation, the visualization helped a lot.
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Thank you very much for this wonderful explanation. This was really really helpful for me.
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ohh, it was a very clear explanation! many thanks and well done!
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That's so clear and thank you!
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Thanks for the simple explanation :)
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Best video on HT
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Just purely fantastic.
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Thank you from the sunny Spain
Amazing!! Really clear with animations. Thanks
+Alexis Gaziello thanks for your feedback.
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Very nice explanation. Thanks!!
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thank you sir. very easy to understand.
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So ,it's like we have points on XY plane and when we transform the y=ax+b in b=-ax+y and plot that b=-ax+y in ab plane
and do it for all the given points of x,y and when we have so many lines in ab plane intersecting a common point we take it and use it as xy and plot a line that will fit maximum number of points ? is that so ?
learned a lot.
thank you very much.
It's really good !
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thank u very much that was a very easy explanation I really enjoyed it.
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great video! Found it very useful :))
The explanation is very good. Thanks
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REALLY helpful!! helped me finally understand the maths behind it!
Awesome tutorial! Thanks man!
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thanks...for the video..it simplified the concept...
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Hi, I am not getting the exact idea behind this. So first, we are detecting edges using canny or any algorithms which leaves us with edges in the image. Now, edges can be lines in the image which is what you explained as y = ax +b where a is the slope and b is the intercept of the line. In the fitter space, I will call it as hough space, my question is "How points in the line equation of the edge becomes line in the hough space"?
Thanks for your question
Please subscribe to my channel
You are right, first detect edges, then use the edges to find lines. Maybe the point that you did not understand is the accumulator (which creates the figure), the more two points intercept in the hough space, the more points are aligned in the original one. This is why the location of the lines are in the maximum points of the hough image.
Regards
At 1:55 how can the green line have a positive slope? Both (Xi, Yi) and (Xj, Yj) have a negative value for a, so in feature space both lines should have a positive slope right? I do believe that your eplanation is correct, however I still have trouble to grasp this transformation. It doesn't make sense to me how the lines are drawn in feature space.
Great visualization.
Thanks for your feedback,
please like and share the video and subscribe to my channel.
Regards
Can you explain me how the another way
r = x cos(theta) + y sin(theta) arrived ?
I assume r is the shortest distance from origin to the line.
I came across henesse normal form and the shortest distance between points and planes but I still couldn't relate with the above equation. Can you explain it to me how we arrived/derived to the above equation ?
Edit : found it after some deep diving into basics
People who wants to know how this equation originated, the term we're looking to search and learn for is "normal form representation of a line"
Awesome explaination!
Many thanks for your feedback.
Please like and share the video and subscribe to my channel.
Regards
Thales Sehn Körting sure thing! Done that!
So, I have a question. On the Generalised Hough Transform, the difference is that we don't compare with line equations. Instead we compare with the target form/path - for every rotation and scale possible. Is that it? If so, it looks very heavy to calculate that.
If I understood your question, yes you need to compute several values to find the lines. That's why we create the matrix, and use the maximum values on the matrix to point to lines. It is expensive computationally.
Regards
Quite informative. Thank you!
+Menard Hernandez Thanks for your feedback
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Best explenation ever! Thank you!
+Angelo Conconi Thanks for your feedback
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Great explanation, thanks for making this video.
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muy bello y todo, pero la transformada para círculos?
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Thankyouu!! Shared with my mates! :D
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Yo soy tu mate ;D
Really good and useful video :) thank you
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How do you know where the lines start and stop? I mean, you have detected that there is a line with r=xcos30 + ysin30. Fine. Now there are some 4 sections that lay on same line (i.e. there are gaps). How do you know where are the boundaries?
Hi Gregory, thanks for your feedback. Please like/share and subscribe.
To discover the start/end points, recall that we applied the Hough transform in the image of edges (1 for edge, 0 for no edge). So with the line equation, you can iterate over x, and find the first and last y with edges. It is a basic suggestion.
Regards
Thank you SO MUCH!
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Amazing video 🤘🏻
+F V thanks for your feedback.
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This is one good explanation. I just have one question. Why is a point in xy plane a line in ab plane and not any other shape? Same thing or a circle?
Please let me know.
Dear friend, thanks for your feedback. Please like/share the video and subscribe.
Recall that the shapes we showed are for the conversion from xy space to the line equation using radius and angle (2:56). The variation of radius and angle produces the shapes.
A point in xy plane is not a line in ab plane. Indeed two points in xy plane that should form a line, are represented by a single point in ab plane. If more groups of points in xy plane are part of the same line, then the point will bright more in the resultant image.
Regards
thanks a bunch! :) awesome animation!
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Now I know what I don't understand! Good video
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Eu amo os seus videos que sao muito claros e didaticos. Voce eh do Parana ?
Muito obrigado pelo retorno! Sou gaúcho morando no estado de SP. Inscreva-se no canal.
Um abraço
very nice
Hi,
I am getting this error in OpenCV probablistic hough lines.
please help to resolve :-
---> for x1, y1, x2, y2 in lines[0]:
cv2.line(image, (x1,y1), (x2,y2),(0,255,0), 3)
ValueError: not enough values to unpack (expected 4, got 2)
nice explanation thanks !
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thank u for your explinatio , did u work on matlab ? I need some help in that
+muhib ullah thanks for your feedback
I used octave which is similar to Matlab.
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Thales Sehn Körting sir I am using matlab , the hough function returns rho ,theta and H matrix ,as u expalined i understood what are rho and theta but what is H ?if u please elaborate it
Great video, thank-you for uploading! :)
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Thales o video é muito bom. Bem explicado. Faça um em português também. Há uma carência muito grande de videos neste nível em nossa língua.
Muito obrigado pelo comentário, por gentileza inscreva-se no canal.
Existe o canal do Hemerson Pistori com bastante conteúdo em português.
Um abraço
Great video, thank you
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thank you vary much sir may i know line detection
+Mallesh Kumar thanks for your feedback. Please like/share the video and subscribe to my channel. I will put your suggestion in my videos wish list.
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Great video! Thank you for the explanation! :D
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Can anyone tell me if there is equipment out there that utilizes this principle?
lane detection systems
Awesome video, please improve audio quality. Thank you
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You are right about the sound quality. Thanks
Very nice explanation. Thanks. can you give me matlab code to impleminte it ?
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please email me (tkorting at gmail) and I send you the code (in octave) regards
I could not find Hough Transform in 3rd edition ... Please reply ... Which edition of the book you used ?
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I have used the 1st edition (in portuguese) from 2000, but to cite in the video, I had to find an english version.
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Thank you so much
muito very bueno 👍
amazing video, also incredible voice, you sound like javier bardem in skyfall
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You are awesome 💯. thank you so much sir.
Thank you
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great thanks
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Thank you.
Muito bom! Este video me ajudou muito com o meu Master project. A animação ajudou muito na intuição do algoritimo.
Matheus ~ Sheffield
Muito obrigado pelo feedback.
Não se esqueça de se inscrever no canal.
Um abraço
Thanks a lot
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Lovely sir Lovely.
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Very good video, I like and comment
Thanks!
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Thank you!
Can I have the transcript of this please
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