Let's take a look at that. Let's think about adding some edges here.

Edge Smoothing

Now, the BA edge is already in there, but the BC edge is not.

So given all these nicely decorated nodes, we now have a rule for figuring out which ones are the bridge edges and this is the rule.

Find Edges

Detect Edges

Edge Detection

Create a New Edge

Okay, on each of the edges of the graph.

Top Edge Detection

Right Edge Detection

Bottom Edge Detection

Left Edge Detection

The approximate diameter, it's a three inch (3 ) shell mill

So, what we're going to do is we're going to take this entire graph and express it essentially as a tree, but some of the edges aren't going to be part of the tree.

It looks like b was our winner.

But with a Warren Ellis edge.

You have a Warren Ellis edge.

How many edges are in a tree where 26 nodes, 25. How many do we have 39.

Select the rectangle tool again and start from this upper edge.

So, this is the vertical mask applied to finding horizontal edges.

Let's look at what happens if you loop on all the different values of n from 1 to 9 and print n and the number of edges in the clique.

Now, I've filled out almost all of this deterministic equivalent.

We start in the start state, and the character we see is a 2, so we follow this edge to state 2.

But that orientation distribution corresponds to edges in the image.

It could be 100 different characters. It doesn't really matter.

You can now just focus on that graph. So here's our graph.

It grows linearly with the number of edges.

In our last question, we used a list of tuples to represent a graph where each tuple represented and edge between nodes.

There's going to be some graphs that can't be drawn as a planar graph. If we add this edge between this node and this node, the resulting graph is no longer planar.

The second node can't connect to anything else and the third node can't connect to anything else.

So, here's a mask that's a special case of a Prewitt mask, and I'd like to ask you a quiz.

Let's zoom in on one section of it right here.

A graph with one node has no edges.

I'll just hold on to it for a while.

Here's a planar graph on five nodes.

So, the root filter is shown here so this is kind of the root filter and this has kind, is looking for, this is a hot template.

Corners have an advantage over edges.

If you think about three times the number of regions, the number of edges has to be at least that big, though, we're counting each edge twice, because each edge can actually participate in two regions.

Once again, a graph with 1 node has zero edges.

We use the Greek letter Epsilon.

We create a graph on the other half of the nodes recursively.

Now what would happen to the H value or the L value if we had one of these edges.

There's really no point in having an edge that goes to nowhere, that goes to fail.

Node here and node here they're connected, connected. This is a node and they're connected.