Hi and welcome back to lesson on feature actors, and we'll take a look at how each of the doctors help
us classify images.
So let's go into the slides.
So take a look at this image here.
This is a cut.
As as I discussed earlier, we know this is a cut, but how do we know because we instinctively see
the patterns and our brain probably combines, unbeknownst to us, all of those little features we're
seeing in the cut and automatically associates that with a cut.
Just so you know, our brain brain is absolutely incredible at visual processing.
I think roughly two thirds or two to four brain is dedicated to vision and being a being a computer
vision engineer, I can definitely see why vision is such a complicated tests for a computer to do well.
So back to this lesson.
So imagine we have a convolutional filter, and as you saw previously, we sliders filters looking for
specific features.
So imagine we have a fidget us looking for a cat eye.
That's that's going to be a common filter that is looking specifically for that.
So let's take a look just dragged across there.
And this just to illustrate what the coin filters do, and it's going back and forth like that over
and over with that image.
So what what is the what are the convolutional filters look for?
So imagine we had a tree in CNN that was able to detect cuts.
You would have filters.
It would actually.
And this is this is true.
It actually creates filters that correspond to the new features in the image of cats.
So it learns what a cat looks like.
Bit by bit.
So you can get you get features that looks like whiskers, one that looks like eyes, one that looks
like the is.
I believe.
I'm not sure what I drew there, but generally you can see that's what that's how it works.
So it's going to go through this image slide window back and forth here looking for these features,
and it's going to trigger the feature maps that are basically activated.
Windows features are seen and those are basically going to be stored is a lot more to CNN.
So don't worry.
But for now, just think of it that the hidden layers will now take these features and no true like
some imagine like this.
Like a decision tree here.
If this is this and this is here and all of those things, then it a cat.
If not, it's a dog or whatever class it's trained to recognize.
OK, so we're doing this sliding window again.
So in the past, this is something that this is why this is what makes CNN's Sue missing in the past.
Computer vision involves us using hand-crafted features, which were basically painful because it involves
so much trial and error, so much different feature extraction methods that it was it was.
I mean, it worked when when there wasn't much variation in images.
So you can imagine, like it works well for things like OCR and other other types of activities where
there isn't a huge variation in lighting and angles and class defamation.
However, it was, it's hard to scale those types of methods to other types of images of other types
of data sets as well.
So as I said, handcrafting was hard, messy, and in most cases, it often led to poor results.
CNN's solved this problem by having the ability to learn the features themselves, which was a godsend
for us, and it basically created this revolution in computer vision.
So these here orderlies I spoke about, the hidden layers are comprised of CNN.
So in the next chapters, we're going to take a look at really layers pulling layers, fully connected
dense layers of butt.
And we're going to take a look at a lot of other things like Max Boot Lexical as a player, but we're
going to take a look at stride and depth and kernel size and all of this off max and all of those things.
So starting.
But before we dive into the building blocks of CNN's, I just want to explain how convolutions work
on color images because this is something that is really talked about in most online tutorials and guides.
And it often confuses beginners because we just, I just showed you illustrated how convolutions work
on grayscale images.
However, it's not.
It doesn't look exactly the same on color images, and you'll see why in the next section.
Thank you.