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OK.

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So let's start at the beginning.

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Let's talk about object really object vectors.

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So I'm going to introduce you to the history of it so fiercely detection is one of the holy grails of

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computer vision because previously what we have been doing is just classifying like an entire image

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and seeing what objects are what Hassid belong to.

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But can we take an image like this and label each major component into being a dog car person horse

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person in the back.

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Not yet until we have come across up to detection.

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So object detection is a mix of object classification and localization object action is it is the identification

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of a bounding box outlining the object.

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So like in my face here basically is extraction a bony box or on my face and this direction is perhaps

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one of the most popular object detection algorithms that we all know.

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We're all quite familiar with from using cameras in our cell phones.

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OK.

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So basically it DL tells you instead of telling you this object here is a cat.

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It actually tells you where is the cat and that is the whole point of object detection.

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So let's get into the history of it and start with horror Cassiar classifiers.

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Now there were many public detectors before this.

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However here is what made it hard to justify this is what made it mainstream and quite popular because

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it was so fast.

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So basically this was a this was developed by Viola Jones in the face detection algorithm in 2001 not

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that long long ago 17 years ago.

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To be fair and it was superfast and it's actually still use to the number of applications.

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Basically it's been optimized and tweaked to be even faster.

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So it basically reduces the CPQ load and it's very very accurate.

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Basically what it does it's a cascade of classifiers.

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That's basically how it got it got its name and it uses a horror.

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Basically let's go into the next slide.

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Actually I don't have it in this section but it basically uses horror features and harsh features are

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basically basically like you have rectangles.

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Overling here.

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You imagine a white rectangle here and one here and then there are different types of Arcacha pacifies.

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So basically is just a feature extraction.

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Basically what we learned before and it's led this box is that over the window over and over continuously

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looking for a face they're very good but they are pretty hard to train and develop and optimize.

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So let's move on to histogram with gradients and SVM sliding windows so sliding windows is a method

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where we extract segments a full image piece by piece in the form of a rectangular extractor box.

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So I mentioned it in previous slide when I was talking about this box being slid across this image.

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What it does here in this image is a picture of my wife from the last bodybuilding bikini competition

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two months ago.

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And what it does is just imagine this window is being moved here then down here and then down here just

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like remember how we moved across the image.

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And CNN's it's exactly the same thing.

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And we can actually set the same parameters like stride and the size of this box and what this box does

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here in sliding windows with histogram of gradients.

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SVM is that it basically extracts the entire hawgs all his brilliance in this box at different scales.

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So basically it does it with image at one scale and then not a scale smaller scale and then this one

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here and this one basically has no room to go right to just go straight down.

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And it tries to match up to how gradients went what it knows.

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It's supposed to look like to find the object.

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Now as you can see this could be an effective way but it's not really that resilient.

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Why.

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Because imagine we have to do this for every segment of image continuously.

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It gets exhaustive and computationally expensive

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so previous action which is basically TISM feature extraction I just mentioned that and why would we

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want to actually manually find co-features if CNN's actually eliminate that.

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All right CNN's actually automatically find features by just running all these tests destroying data

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Trulia algorithm and finding the last matching it with the correct last.

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So that's what's brilliant about CNN's.

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It takes that step away from us.

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So as I said once of problems we're doing this is a sea of scale.

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Imagine this is a simple image just 20 by 20.

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So this box can be passed over here.

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But imagine this was a much bigger continue TV image.

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How many different times how many different boxes would we extract.

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How do we know what size box should be.

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I mean that's where we rescale image but how many different rescaling are we going to do.

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So as you can see this is not a very effective way of doing object detection.

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So talk a bit the bullet histogram gradients are not going to go in go into this in detail of taught

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this in my other op and see the course you can.

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The video is included free in that section.

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So that's why I'm going to talk about it much here.

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But basically the slides are here for you to go through on your own and you can pretty much infer from

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these steps here what hawgs really are.

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So now we move on to our CNN's.