Market segmentation.

to their habitual supplier.

My supplier, he gone.

3D Structure Segmentation

You had a monopoly supplier.

I'm systematically working up to her supplier.

Who is he? He was a supplier.

but you can't decide I'm your only supplier.

The orderly was just your most recent supplier.

Let's build a probabilistic word model of segmentation.

What one really wants is fully automatic semantic segmentation.

By definition, the best segmentation, which we'll call S , is equal to the one which maximizes the joint probability of the segmentation.

Guess you were not too sick to meet with your supplier.

Sometimes people call this segmentation. Sometimes people call it grouping.

And we saw special effects like segmentation and changing background.

Needless to say, Norway has become the world's second largest oil supplier.

We just don't want to be a supplier or a joint venture

So you might be a graphics supplier to Apple or Hewlett Packard.

Ain't nothing I can do. My supplier only comes out after dark,

You don't give us the name of your supplier you're going nowhere.

That is the segmentation that comes out now is the time.

But what we really need is one for the interactive segmentation approach.

So reorganization means segmentation. So bottom up. Assemblies of super pixels, regions.

And, from bottom up segmentation we can come up with candidate objects.

Because, examples of objects against the natural background really challenge segmentation systems.

Both in the medical example as well as in the image segmentation example.

So let's look at each of those in turn. First, the segmentation model.

So here are some examples, segmentations from the work of segmentation data set.

First, a segmentation model of how do we break up the German into phrases?

And for these more bottom up segmentation approaches, we need them to include semantics.

Another process is the one of reorganization or sometimes its called grouping and segmentation.

A very different application where PGMs have also been used is that of image segmentation.

The best segmentation of the rest of the words doesn't depend on the first word.

Now I want to give you an idea of how well the segmentation program performs.

Hell, I was pretty much blown out at just about every dive supplier south of Key West.

And here are some results of the semantic segmentation task on the Pascal visual object challenge.

Now, if you just tell me who your supplier is I think this can end very well for you.

A semantic segmentation really means division of the image into regions corresponding to different objects, fully automatic.

We'll have a lot of segmentation problems for example if we see that the string y o r k dash any w as part as New York New Haven, how do we know, the correct segmentation is New York?

Sometimes it's purchased directly from the machine tool supplier but usually its purchased from, and installed by a third part integrator

We'd like some way of searching through them efficiently without having to consider the probability of every possible segmentation.

This word here is not a word like in dash law. We have to solve the segmentation problem correctly.

And in the context of the image segmentation, it's the labels for different pixels or these larger regions called superpixels.

We would also consider the probability that they're considered a phrase together, and come up with a high probability segmentation.

One is market segmentation, where you may have a database of customers and want to group them into different market segments.