Image Gradient

[@Rebo3D]

"Where is the edge? Hope you know the answer abit". . . . . 4-years-later-Me: "Not yet"

[@youneshamza381]

6 years after ... still not answered. ( LOOL )

[@ranad2037]

It's perpendicular to the direction of the gradient

[@ahmadbelhaj1756]

I don't understand why can't you just do it 3*3 matrix?

[@Hans_Magnusson]

What do you mean? You can apply this to any rank of ℝⁿ… But you are analyzing a two-dimension image… hence two dimensions. But the results could be stored in a matrix and then you can plot the minima and maxima and get a nice 3D plot..!

[@MrRobi10]

Hello, if I have an image with 11x11 pixels and In the center of the image is a square of 5x5 pixels. The gray level of the background is 0 and the gray level of the square is 50. How can I compute the result of the magnitude of edges given by the compass operator for this image taking into account that the image is not noisy? I have the code but I don't know how to apply the math on paper...

[@satashreeroy1652]

The image function is the function of intensities, right? How do we write it in the form a 'function'?

[@mohammadidreesbhatresearch6505]

Image is also a function.

[@sepidet6970]

it can be represented as function in spatial domain with x and y as variables, the variables show the spatial location of pixels and the output of the function is the pixel value in that location. f(x, y) = r that r is pixel value

[@sergeimerekin8193]

Thank you! Concise and clear explanation!

[@divyar3668]

Thank you so much , nothings i had seen like this kind of explanation very informative with practical explanation . thank you again :-)

[@emirhandemir3872]

Was it this simple? OMG

[@anp6700]

Hello, thank you for your very informative and helpful video. Could you please tell the answer for the last question (where is the edge)? I don't have a base on image processing so it's really hard to find the answer. Thanks again.

[@abdurrehmanatif5016]

oky well we can find edge using theta and Ro by applying equation of line Ro = XsinQ + ySinQ

[@ranad2037]

It's perpendicular to the direction of the gradient

[@chawza8402]

what is the f?

[@jrfgeographystudy]

Thanks sir

[@Hans_Magnusson]

In economy we use the two-dimensional gradient, but call it a Jacobean!!! A Hessian is then a multi-dimensional second derivative… One use case is in credit scoring your customer for a credit card application approval…! My two 💳 swipes Oh and when scanning a 3d surface to find local and global minimum and maximum… Eg is it the local hill by the church, or is it perhaps Mount Everest we found, perhaps the Mariana Trench… 😂

[@Hans_Magnusson]

Then there is some basic Pythagoras and the geometry of a triangle involved 👌

[@donfeto7636]

Give me the fucking answer :D