Hi and welcome to the section or chapter where we take a look at convolution, how the convolution operation
works on color images.
So let's take a look at this.
Let's go back to our gray scale scenario.
Remember, we built the edge detector here.
This is our edge detector, colonel.
This is our grayscale two dimensional image, and this is a feature of output.
And you can see it's a fairly simple, straightforward calculation.
So a lot of times beginners have trouble understanding when there's tree dimensions here, as you can
see in the slide now, tree components how and we do have tree kernels one for each color.
I'll tell you why that's important for people are often confused with why is the output two dimensional?
And the reason the output is still two dimensional here.
The feature map is because we take the colors here and we sum them up.
So you can see this.
A one is not too visible here.
So you can see it's one plus one plus one gives this tree and we just some each each.
Think of it like a like a hole that goes through each of these, these color components, and you can
just some through each cell here so that each other corresponds to the same location.
It just keeps summing it.
And that's how you get the output can also that now the output, you know, is still a two by a tree
by tree, two dimensional image, but not not a three dimensional image.
That's what important point is here.
So what are the advantages of having color components as your filters?
Now, so imagine we're looking for a red, a stop sign.
But however, imagine this there's a similar sign that screen and a green stop sign.
The way we detect colors is by having these different color coded notes here.
So one of these candidates could be corresponding to that red stop sign.
The other one could be corresponding could be a filter that's meant to detect green signs and blue signs
and different color combinations.
So that's why it's quite important to have it each offensive, each color component.
So this is how we treating volumes work, and we're going to take a look at basically how it works when
we have multiple filters.
So you previously so examples where we just had one filter.
However, in reality, as I said, CNN's tend to have many, many filters that can have hundreds, even
thousands, although that's a bit excessive.
Most in most cases, it has like between 64 and 256 filters for most simple classifiers.
So let's take a look at this.
This is now with two filters, and you can see we have a five by five by tree image here.
Remember, this is a color component of each image had a red, green and blue RGV.
And this here is the pixel.
So remember, each pixel point does zero by zero top left pixel has three color components associated
with it.
That's why it's a five by five by tree grid of volume right now.
And here we have.
We're using two filters now instead of one, like, you know, previous examples.
So it's a tree by tree, by tree by two, because it's two filters now.
And this allows us now to produce two feature maps instead of one.
So you can see now the future maps are directly related to the number of future maps is directly related
to the number of keynotes here.
So this is how you actually calculate the feature map volume here.
You can see end by end by see and see which is the in depth.
So it's five by five by tree.
This one here is ftf.
That's a feature or this sort of filter.
So you have tree by tree and we have tree depth again here.
And this is what the of the output feature size is going to be.
It's going to be an minus f plus one, which is tree.
Similarly, Tree Hill multiplied by an F, which is two, and what is NF and F is a number of filters.
So that's basically a mathematical way of just showing how these sizes are mapped back to the output
of the feature map.
It's not that complicated, it's just way too.
It just it's just a way to establish some guidelines.
So when you're actually calculating what your future map should be, you have a formula to work with.
This is this formula right here at the end of it here.
So that concludes this chapter on how you operate convolutions on treaty images or color images.
Next, we'll take a look at kernel size, which is a filter size and depth.
So stay tuned for that.
Thank you.