So in order for us to understand the limitations of computer vision, it's important to understand what
makes it so hard, because anything with easy or deep learning is essentially even though it seems easy
to us.
It's not easy for an algorithm or computer or model.
So let's take a look at this.
So firstly, like I just said, our brains make things easy for us.
Our brains are amazing at vision and so many other tasks like speech, reading, comprehension, strategic
thinking.
Well, some people at least, but generally everyone can do vision quite well.
And that's because we have a very well-developed visual cortex at the front of our brain here that basically
can see and understand what the visual information that's being fed to eyes very, very well.
It's amazing, actually, what it can do.
It beats robots any day, at least currently right now.
And there's a reason for that, actually.
And that's because our eyes are very good at general purpose, computer vision, that is.
Let's assume you're watching this slide right now.
You're understanding that you're watching a screen or a laptop screen of a monitor.
You understand that there's like a background behind it.
You understand that it's adept.
There's so many different things.
You're understanding your own, you know, where's well-lit.
You know what a desk is.
You know, the objects are on your desk, keyboards, everything.
So there's a lot of information being fed into your eyes and you just naturally understand it.
However, it isn't so for computers, they have to basically understand every object.
And then we actually have background knowledge, like what an object is, like what a cup is, cup stores,
water or other drinks, but robots don't have that.
So you have to kind of build all of that general knowledge into it, which is a different topic, though.
I mean, eventually what computer vision will lead to, however, because robotics a huge area where
computer vision can be applied, but as of now, it's more in niche applications like understanding
one type of medical detail or understanding faces very well, or understanding how to transform your
image into artistic styles.
Basically, we've trained these models to do niche things, but unlike our brains or brains, a very
general computer vision isn't there yet.
So it might excel.
We might even beat humans in one test, but it sucks and everything else.
And now let's take a look at what it actually makes computer vision so hot.
Now there are a number of things.
So first one is you're limited by two cameras.
So eyes are very good camera systems.
If you think about it.
However, physical cameras often have limitations with noise, with granularity or resolution.
Things fall away if it's a teeny tiny camera are going to be quite blurry compared to someone who has
2020 vision.
So there's those limitations then viewpoint variations.
We and we naturally understand that this is the Statue of Liberty at different angles, or this is a
house that's just rotated, but a computer vision model might not.
You might think it's different objects it's looking at.
There's also changing lighting conditions.
You can see how drastic this like being here or it's like being not there changes the scene, then there's
scaling issues.
So you can see the Taj Mahal at this level, at this scale looks completely different, at least to
a computer vision model.
It might hopefully not.
But you can see when you scale back out how different it looks as well as these comparisons here, then
there's natural non-rich deformations, like a dog or a horse has many different poses similar to a
human.
You can be sitting, standing, crouched.
So there's this that to consider that definition that an object like this can do.
So you're going to have to use other characteristics to identify it, which we naturally learn in our
brains.
But a computer vision algorithm has to be fed many different instances of a dog or a horse to understand
that it's not.
The fact that the animal is standing this way makes it a dog.
It's because of its fur or its facial shape.
Or is that's what makes it the dog.
There's also a occlusion where basically which means that part of the object is blocked by another object.
So it's a form of clutter, which we'll take a look at next.
So this isn't technically a form of clutter.
It's not camouflage, but in a way it is clutter because it's a scene where it's hard to detect the
octopus in next.
This is a scene somewhere in China possibly that just shows you how many clutter, how many different
objects are around.
So it's quite hard to make out anything in this picture for us for computer vision algorithm, trying
to make sense of it.
Then there's object class variation.
Look how many different types of beds they are in this scene.
We all know their beds, except this is like a sofa bed.
But we all know their beds.
But a computer vision model might not.
Then there's ambiguous images.
An object, optical illusion.
Sorry.
So you can see this is actually a flat 2D image.
However, it does look like a truly image.
Is this.
Is this a fizz or two faces?
Who knows?
So as you can see, there's a number of ways we can trick vision systems.
And actually there's a whole field of complete division, antagonistic type of training where you create
models to beat other models effectively.
So it's not foolproof.
It's very well developed right now.
But as you can see, naturally, vision is basically a messy field and field, but I mean like messy
images coming in.
So there's a lot of ambiguity inside of it.
So in the next section, we'll take a look at what exactly are images, because this is the foundational
knowledge of computer vision.
Understanding what images actually are.
So I'll see you in the next lesson where we take a look at this topic.
Thank.