We only consider the X positions (horizontal )right do not calculate for the y(verticle). I consider like that because the filter you used actually the convolution of the original types for X. I hope I could explain myself
Nice video but why using only horizontal edge gradient?, answer should be combining both gradient. this way you only detect one directional edge. G= |Gx| + |Gy| or u can take its Euclidian form with sq roots and sq.
@@collegefriendly okay maam, and if it is not mentioned then we need to match for each pixels with their respective neighbors even in sobel and prewitt filter, right??
When we have to do pixel replucation or zero padding and when to apply directly? Is it necessary to do zero padding/pixel replication before applying any filter? Please help me mam
Hello Anushree, Please continue your channel again because the way of explanation of yours is far better than my college teachers. And your voice is amazing. Please share your social media handle i.e. LinkedIn, so that I mentioned you and give you a shoutout. Hope you have a very bright future.
literally i've watched multiple videos regrading this topic and coudn't understand but now i understood it very well Thanks mam
You are a god ma'am...Tomorrow I've my DIP exm and thanks to your playlist I've understood all the problems well....Great work ma'am...
You are a gem mam..cant describe your work in words..thanks for your effort. You surely deserve lot more subscribers
Thank you so much for your wonderful comment😊
Alhamdulillah, you explained everything so simply! I have an exam on this tomorrow and this is helping a lot!
can you please tell why pixel (1,1) is not the first 50 @top left corner??? is the replication padded is already done?
Thankyou for this playlist ma'am .
It is helping me a lot for the academics purpose
maam your lectures are amazing and very very helpful, i am so much thankful to u, u are the best teacher.
just watched till this video for my mid sem.
got the same question od distances in exam aced it thanku
Ty for the easy to understand explanation, wasn't taught about these filters and could not understand how it worked when it was mentioned in a paper.
شكرا جزيلا على كل فيديوهاتكم، المادة نفسها و الشرح بسيط يفهم.
We only consider the X positions (horizontal )right do not calculate for the y(verticle). I consider like that because the filter you used actually the convolution of the original types for X. I hope I could explain myself
Aren't we required to use both horizontal and vertical edge detection operators so that we're able to find gx as well as gy to compute the gradient?
I'll use tangent for that
I can't focus on the topic while listening to such a beautiful voice. do we have any other option left?
Nice video but why using only horizontal edge gradient?, answer should be combining both gradient. this way you only detect one directional edge. G= |Gx| + |Gy| or u can take its Euclidian form with sq roots and sq.
What about zero padding???? In case sobel and prewit. Like when we are calculating magnitude of gradient of given input image using sobel or prewit???
Thankyou so much from the land of mountains, Nepal
you didnt mention anything about gradient and magnitude.
If he gives me an image ( numbers) and ask me to generate gradient image how I will do it?
Thnak you for sharing
Amazing examination nd vioce also✨
If pixel is not given then what we have to do
mam, why are you not rotating the mask vertically and horizontally for convolution before multiplication?
you just took my breath away
Could you pass on to your parents that they did an awesome job ????
Thank you for this!
Thank you for your comment😊
Thanks help me amazing! congrats from portugal
Thank you so much!😊
Thanks for this video. Please if after this operation the result is inferior to zero, what should we do ? Put 0 as the answer ?
Show the output of any edge detection alogrithm ma'am pleaeee
Can you make a Video on Point, Line and Edge Detection pleaseee
Thank you, this is useful
Thanks, it helped a lot
Thank you very much dear, the explanation is very clear and simple
Most welcome!😊
Great explanation!! Thanks a lot
Welcome😊
On a serious note this is really good, but need more help
Apart from this can you explain what is edge linking and boundary detection???
Is there a normalization involved in sobel operator or not?
Nice video and example!
Really nice! Looking forward to more such amazing videos🙌
Thank you so much Pranav! Will be uploading more videos soon.
This all algorithms help to smooth the noise in the image right?
Sound is very high, please make video with low sound
wounderfull session maam
you are the best
Thank you soooo much
kindly clearify are these set of steps for edge detection??
Do you use the (Roberts) filter once over the matrix (in the topleft corner), or do you use that another 3 times to get all the numbers in the matrix?
We'll just apply it once, as in the question, we have to apply the filters only on the pixel (1,1).
@@collegefriendly okay maam, and if it is not mentioned then we need to match for each pixels with their respective neighbors even in sobel and prewitt filter, right??
Yes
Thank you mam
Thank u mam ❤️
When we have to do pixel replucation or zero padding and when to apply directly?
Is it necessary to do zero padding/pixel replication before applying any filter?
Please help me mam
Very nice lecture, if any doubt where I can ask??
thank you , what about the drive of Roberts filter ?
Sorry I didn't get your question! Can you please elaborate?
@@collegefriendly Second Derivative for the Robert
Roberts filter is a type of first-order derivative filter
madam thoda jor se bola karo sunai ni dera
Txns
Cool voice mam
You didn't apply the full operation, just the Gy component. This video is misleading. Thumbs down.
I WENT TO PDF TO ANSWER
Pen of steel 🦸
Thoda bolke padhaya kro
Not audible 👎
Hello Anushree, Please continue your channel again because the way of explanation of yours is far better than my college teachers. And your voice is amazing. Please share your social media handle i.e. LinkedIn, so that I mentioned you and give you a shoutout. Hope you have a very bright future.