Because I squared, I multiplied the second derivative of y with respect I multiplied it times itself.

If pixels are negative, we just ignore the negative sign and map back to the absolute value.

No? Nonlinear equations?

How about our Gaussian kernel that we discussed in class of size 5 by 5?

This answer is not at all trivial, and I don't blame you if you get it wrong.

First is our gradient kernel here minus 1, 1.

Busy response

These are the x values, these are y values.

Now the second thing we have to figure out is this linear or is this a non linear differential equation?

And hopefully it will recognize that that is a car.

You have your order, so what is the order of my differential equation?

And that is the key.

It works. Can anybody hear me? We have an emergency.

It's ordinary, because we only have a regular derivative, no partial derivatives here.

Response

Answer

liter

linear

lightyears

Linear

Server response

HTTP Response

Any response yet?

John, I'm in.

Anything?

Anyone there?

Gunship, come in.

Linear Sweep

Y linear

Linear Scale

Now, all of the sudden, I have a non linear differential equation.

We talked about the gradient magnitude kernel, which was defined over a square root of the squares of the image gradients.

The server reported

This is your office calling...

Earth to Rochelle.

Come on!

Answer them.

He's not responding.

Respond!

Copy.

Acknowledge.Over.

Anybody copy?

In other words, if you give it a weak stimulus, it gives a weak response, if you give it a stronger stimulus, it gives you a stronger response, if you give it a really strong stimulus, it gives you a really strong response called linear.

We have an imager of a certain size and like to calculate just the average brightness of all the individual image pixels.

If they're not, then we're dealing with a non linear function.