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So let's move on to edge detection and image brilliance.

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So before we continue into this section I want you to think about what exactly is an edge.

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You may find an edge as being the boundaries of images and that actually is sort of rates as you can

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see in a still picture of a dog here we can understand that this is a dog because the edges are drawn

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in.

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We have the edge of it till his body's face and his paws.

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So we know it's a dog.

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So as you can see edges do preserve a lot of an image even though a lot of the detail is missing here.

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So how do we formally define edges edges can be defined.

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A sudden changes or discontinuities is in an image and this picture I've drawn here actually highlights

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this example.

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So imagine you're moving cross this line here and this is the intensity as we move along this line here.

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So as you can see the intensity is high here.

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Then suddenly it gets low which is what this trough represents.

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And then it gets higher again.

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So this dip here represents an edge here.

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And vice versa if this was a white line and a black background here black background here we would have

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the opposite shape here.

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So that's how we actually define edges here by finding new image gradients of the intensity changes

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across these lines here.

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So fortunately open see if he provides us with some very good education algorithms we're actually going

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to discuss tree of Dmin once here at SoBo Plus the uncanny.

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Now can is actually an excellent education algorithm and is really good because it has a lot of it.

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It defines it as well and it's actually very good at picking up edges here.

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This is actually a very hard image here for protection algorithm to look at it's a dog a dark dog a

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Rottweiler actually.

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And as you can see you can actually make out that it's a dog quite easily.

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So it wouldn't go into details of all of these algorithms because they actually get quite mathematical.

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However it does give a high level explanation for canny edge detection.

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So what can he does it food supplies that go see him blurring effect to the image as you previously

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saw we did some gaffes in luring a few chapters behind.

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And then what it does it finds the intensity gradient across the image then it applies and none maximum

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suppression of which is actually removing the pixels aunt edges and the image and then apply some hysteresis

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Tressel's Zufall pixels within an upper or lower threshold then it's considered an edge and you can

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learn a bit more but canny edge addiction and different edge addiction techniques.

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These links here.

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So now let's look at implementing these edge addiction algorithms in our code.

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So let's look at implementing some of those additional algorithms we just discuss.

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So if this one we're going to discuss a Sabel edges and what's cool about Sobol edges is that we can

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extract vertical and horizontal viceversa edges from the image.

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I'm not going to go into the details of this function although this is this is going to just to get

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different strengths.

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And this is the input image here.

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And then we can actually combine both x and y edges and using the bitwise all and showed them together

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get to the Plassans run here with C-v to the.

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And it just is no trouble.

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As parameters are things the person is just a blanket function and then it is county and county is actually

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quite simple as well.

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It just sticks to attritional values here.

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So let's run these education algorithms and compare them.

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So this is our input image remember it.

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These are the horizontal edges in Sobell.

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Does it have vertical edges and Sobell.

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These are the board combined.

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This is the Plassy And then as you can see the plastic actually and then face a lot of edges but also

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a lot of false positives in the sky.

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What we did was we can to end these thresholds to give us nicely some nice edges.

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Maybe that's what county is good for.

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As you can see Connie actually disregarded the sky and a lot of excess noise but actually preserved

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the edges of we wanted to see which is in the structures.

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So what we can look at his uncanny is that we can adjust these two attritional promises you everton

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up something quickly here.

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However just note that these prompter's here are greedy and prompters.

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So let's set degree and parameters that we want to look at look at to some tighter value.

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So let's see 50 and 120.

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Let's go over with Sobell horizontal edges quite nice vertical edges combined Tousey in there we go.

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So as you can see the image is that it looks a little different so vastly different.

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However you can play with your thresholds in county and see if you get the best.

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Whatever whatever you want as a best is to represent you image.

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OK.

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So that's it for edge detection.

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We'll move on to some of the stuff now.