OK.
So let's start at the beginning.
Let's talk about object really object vectors.
So I'm going to introduce you to the history of it so fiercely detection is one of the holy grails of
computer vision because previously what we have been doing is just classifying like an entire image
and seeing what objects are what Hassid belong to.
But can we take an image like this and label each major component into being a dog car person horse
person in the back.
Not yet until we have come across up to detection.
So object detection is a mix of object classification and localization object action is it is the identification
of a bounding box outlining the object.
So like in my face here basically is extraction a bony box or on my face and this direction is perhaps
one of the most popular object detection algorithms that we all know.
We're all quite familiar with from using cameras in our cell phones.
OK.
So basically it DL tells you instead of telling you this object here is a cat.
It actually tells you where is the cat and that is the whole point of object detection.
So let's get into the history of it and start with horror Cassiar classifiers.
Now there were many public detectors before this.
However here is what made it hard to justify this is what made it mainstream and quite popular because
it was so fast.
So basically this was a this was developed by Viola Jones in the face detection algorithm in 2001 not
that long long ago 17 years ago.
To be fair and it was superfast and it's actually still use to the number of applications.
Basically it's been optimized and tweaked to be even faster.
So it basically reduces the CPQ load and it's very very accurate.
Basically what it does it's a cascade of classifiers.
That's basically how it got it got its name and it uses a horror.
Basically let's go into the next slide.
Actually I don't have it in this section but it basically uses horror features and harsh features are
basically basically like you have rectangles.
Overling here.
You imagine a white rectangle here and one here and then there are different types of Arcacha pacifies.
So basically is just a feature extraction.
Basically what we learned before and it's led this box is that over the window over and over continuously
looking for a face they're very good but they are pretty hard to train and develop and optimize.
So let's move on to histogram with gradients and SVM sliding windows so sliding windows is a method
where we extract segments a full image piece by piece in the form of a rectangular extractor box.
So I mentioned it in previous slide when I was talking about this box being slid across this image.
What it does here in this image is a picture of my wife from the last bodybuilding bikini competition
two months ago.
And what it does is just imagine this window is being moved here then down here and then down here just
like remember how we moved across the image.
And CNN's it's exactly the same thing.
And we can actually set the same parameters like stride and the size of this box and what this box does
here in sliding windows with histogram of gradients.
SVM is that it basically extracts the entire hawgs all his brilliance in this box at different scales.
So basically it does it with image at one scale and then not a scale smaller scale and then this one
here and this one basically has no room to go right to just go straight down.
And it tries to match up to how gradients went what it knows.
It's supposed to look like to find the object.
Now as you can see this could be an effective way but it's not really that resilient.
Why.
Because imagine we have to do this for every segment of image continuously.
It gets exhaustive and computationally expensive
so previous action which is basically TISM feature extraction I just mentioned that and why would we
want to actually manually find co-features if CNN's actually eliminate that.
All right CNN's actually automatically find features by just running all these tests destroying data
Trulia algorithm and finding the last matching it with the correct last.
So that's what's brilliant about CNN's.
It takes that step away from us.
So as I said once of problems we're doing this is a sea of scale.
Imagine this is a simple image just 20 by 20.
So this box can be passed over here.
But imagine this was a much bigger continue TV image.
How many different times how many different boxes would we extract.
How do we know what size box should be.
I mean that's where we rescale image but how many different rescaling are we going to do.
So as you can see this is not a very effective way of doing object detection.
So talk a bit the bullet histogram gradients are not going to go in go into this in detail of taught
this in my other op and see the course you can.
The video is included free in that section.
So that's why I'm going to talk about it much here.
But basically the slides are here for you to go through on your own and you can pretty much infer from
these steps here what hawgs really are.
So now we move on to our CNN's.