We saw left feature number 1.

Describe the level characteristics

He look familiar to you?

And a social movement is comprised, in my view, it's comprised of four main characteristics.

They are usually localizable, like corners are.

There'sbeen a massive explosion in midcity washington.

And you should observe, that the perceived order of features in the scene, by virtue of using a pinhole, will be inverted inside the pinhole camera.

So what are these characteristics that I'm talking about?

He look familiar to you?

They did report one distinctive feature which bears mentioning.

Well, and you're glad I didn't show you this.

This is an image I took a while back in Amsterdam, and it's interesting because there's a lot of interesting features

Selam unites us all and gives us a unique account on what makes us human.

These are the basic features of my device, or our device.

We're human, that's what we do. It's one of our features.

By combining both of them, we get a gradient s l magnet? image, and we have our very first feature detector, which is a feature detector of any edge in the image.

And I did. We started to redesign them, one by one.

We have a second camera over here, camera B, a pinhole camera which can see the same features.

So we can say that a state is this collection of Feature 1, Feature 2, and so on.

Let me use lowercase x to denote the input variables often also called the features.

Let's study the first case.

Let's say we're using the tumor classification example.

You may notice they're wearing very thick gloves.

The perfect desert machine. I'm smartly designed.

Anything else?

Any description?

That sure narrows it down.

Can you give me a description of the car, sir?

And the people who said, Yeah, yeah.

Eh, I never get around to brushing and flossing as much as I should, they were characterized as low efficacy.

And he was really gagging. I thought he might throw up on me, actually.

Black hair. That's about it.

For a different configuration let's now assume we get to see 1, 2, 3 in camera B, and we care about the feature indices we see in camera A that corresponds to those features over here.

Its markings match those of a ship... that blasted its way out of Mos Eisley.

Other features

Okay, what does your husband look like?

How will I recognize the person I'm meeting?

They really loved it.

(Laughter)

Look at this. It's an old typed Japanese toilet.

I'm going to do an experiment here with you.

Here's a 'Structure from Motion' type problem, that is similar to what I asked you on a homework assignment.

So that's how you flip a featured TED Ed video,

Uh...

Sometimes you can analyze a problem in two different ways and flip from one of these attributes to another, depending on how you analyze it.