The next step is, or the next topic is edge detection. So we said, we'll see here, in the overall screen, I said, we said we wanted to have a symbol in this description,

and we can move on to our input spectrum. We said, well, we did that point, extract this, give it an edge, extract this, give it an edge, extract this, and then, in the end, something that can be found in three of them. And we discussed point extraction, and now we need one creation in this way.

So what is that, or alignment? Well, first of all, we're gonna do different details, but we want to look at actual realign in the US. We've made a huge variation of the surface orientation.

For instance, here we have an edge of our object, and typically, thanks to the fact that the light now arrives at a certain angle and is reflected, and then to the center, we're gonna get brightness differences with the revision, if you see these two sides

of the object, and this leads to a change in brightness as the percentage in units per quantity in each edge here. And for this, what it might be wanting is to develop a very interesting looking for us, because we're trying to create the smallest externality of the object.

It may also be a variation of material. So if you have a mark on top of our object, then of course, there are these mark methods, different color, different brightness, and correspondingly around the same field, and then we have the boundary of these mark fields of the object, so ever-opportunity to the brightness.

Some are just due to the analogy, something that occurred in the object, and then it's due to other properties. If you change it in the image, so there we often do that shadow, and we always say, if you're looking for shadows,

you're expecting, but those are, they're just, they're not interesting. So the color to the right is my CD, and the color to the left is text, so we can use the texture. So we want to use the model of this in a different way,

and most of the time, you can also use the effective type of area, and for the time being, you can use the D. And there are the kind of, if you wanted to do two different types of linear structures, you can have

what we call step edges, so it's a boundary, separate between the D, you have the drive area, you have the darker area, D, D, there is some kind of brightness. This tape is, occurs kind of in a more smooth curve, in the optic space,

and in the inner space, sorry, I missed one, it's here. If you can cross it, you have to, it's the gray area, and of course, it's actually in the heart area, and you have the step edge here, and the step edge here, and the step edge here.

The linear aspect, if you look like this, the golden lines are bars, right? So if you take the cross step here, and for instance, you have an average way that looks, and you're in a complete sense of anything that you thought, right?

A narrow, right, line, it could be seen as an elevated region, like D, but perhaps you might have written it same sector line, but it's same sector line, okay? This is a typical, a goal, something that you can't see, it looks like a race, and those are the way you do it,

the way you do it, and the way you do it, it looks like a good sector line. So it's actually a narrow D region, which you never mark off D as an ordinary line, and the line is then of course a simple line. We use different methods,

for example, you can have the lines, here I'll put it in D as sector lines, in fact, I'll talk about that later.

Obviously we have, at ages, not every second homogeneity of brain language typically, typically it's a five to 10. So we can, if you consider brain language, well, what do we have observed? At ages, we observe a large gradient magnitude, and in two years, we have low magnitudes, at ages, we have high magnitudes.

What about the orientation of the brain language? Well, we can say it's a very long degree, and in terms of an object we can speak about a lot about this topic, the IMETP element of the head. We would represent the head by some geometrical time,

or whatever it is, we can represent the basic pixel chain, this is not a very useful phrase, but you probably can do it in a unique way, but it's much better to have it for a metric picture, so this is by polygon,

you can know the geometrical shape, and you can see the parameters of the geometrical shape, or of the geometrical values, or you can have some very geometric attributes, it can be the main relative medium, it can be your case, it can be your alternative, it can cost you every query, and it can be the effort to divide it here,

and that can be all the pieces. Now, age extraction can be the frequencies of a series of steps, which are carried out one after the other. So we would first define, well, where do we actually have the length, we would identify 80 digits.

In many cases what we get are not 3.9 or 8 digits here, but we get kind of dense pixels, we would be seeing good edges. And so in the second step, we would have to thin out these 80 digits we get, and thus we would get very thin,

very thin lines at the edge peaks. This is what we call thinning, and it's usually done using maximum and maximum action. In some cases you can keep it very well. You can also estimate the position of the edge,

of every edge point, which is typically identified with some things you see here. This would also be here, and what you would get, you would get a mutual image, where the pixels are marked as edge pixels, these edge pixels would have, it would consist of thinned struts of edge pixels.

The width of these thinned struts would be one pixel step differently. And for each of these edge pixels, you would get the exact position of the edge point inside of the edge pixels if you want to do the sub-pixels. The next thing is,

you want to go from fixed representation to, you want to know how these edge pixels belong together. Edge is not just the same pixel, it's a series of connected series of pixels, edge pixels. This is called edge tracking, and what we get are edge pixels streams,

so edge pixels streams. And here, of course, we have a series of edge pixels that are all very close to each other, and you might get a similar representation of the edge, this is not really what we want, but we're going to participate in a series of projects, and so we will approximate it by implementing these ideas

and this is all against the surface. And we've already discussed all the concepts, but no, this is the end. Any questions? Okay, so that's it for today. Next time, I'll see you in a few minutes,

and I hope for the next time, my connection with the HDMI cable will work all the way up to the mic. Thank you. Bye.