So now let’s take our Gaussian and convolve it with the image. So now remember that A is 512 x 512 x 3, which is a three-dimensional matrix, and H is a two-dimensional matrix. So if I try to do this I’m going to get an error I’m sorry we are using the CONV2 function, and you still get an error because A is not supposed to be a three-dimensional matrix, right. So if we look at the definition of convolution we’re working with two dimensional matrices, if we have two dimensional convolution. So what we want to do is let’s just take the red channel and it doesn’t matter which one, because remember when we were looking at the red, green, and blue channels we pretty much saw the same image for every channel. Ok, so, we still get another warning and that’s that A is still in values of UINT8, so let’s change that to double. Ok, so, now let’s imshow(C) and see what we get. So it’s all white, and so what does white mean? That means all of the values are too high, right, so there’s too much intensity in this image. So, you have to play around with the values a little bit, so let’s try making a smaller filter. Ok, so, let’s say H is equal to my_gaussian 25 and then sigma is 5. So now the filter’s smaller and the sigma is smaller, so the blur is going to be less, but the intensity is still too high. So that could mean just the values are too high and we need to multiply by lower values. So let’s divide the filter values H x 1000, and imshow(C) again. So now we’re starting to see some black, right, so that’s good. Ok, so, I’m decreasing values and I’m starting to see the image. Okay, so, here’s the original image but blurred using a Gaussian of sigma equals 5. Now one thing I didn’t show you guys because I wanted you to go through the exercise of creating a two dimensional Gaussian filter yourself, is that we already have a function called fspecial in matlab that create filters for us, and so fspecial can create many different types of filters. So, in addition to the Gaussian it can create laplacian filters, an averaging filter which is another thing we’ve used, the Sobell filter which is useful for finding edges, so all different types of filters. So let’s try using fspecial instead. fspecial(‘gaussian’, 25, 5); Now let’s do our convolution. So I’m going to divide by a thousand, because I know I’m going to have to divide. So let’s just see what we see. Ok, and so it’s a little dark so I didn’t have to divide by a thousand, maybe I could had divided a lower number, but you can see the idea is that we’ve blurred the original image using the Gaussian filter given to us by fspecial instead of our own Gaussian filter that we built. Ok, so, like we did before I want to plot the filter that we created. So I’m going to go imshow little h, which is the filter we created with fspecial, and notice how it’s just all black. Remember that 0 is black and 1 is white. So what we could do if we want to look at what’s in h, let’s check the maximum value of h. So max, max because it’s two-dimensional. The maximum value is .0065, so of course when we plot that it’s going to be pretty close to black. So what we could do if we want to scale it by one, if we want the maximum value to be one, we could do imshow(h) divided by the maximum value. Ok, so, now we see what we expect to see which is white in the middle. So one question you might have is why is the value of h so small when we use fspecial? That’s because it does a thing called normalization. So if you have ever studied probability you know that a probability distribution has to sum to 1, so here it is a similar thing. So if we sum across both dimensions of h we get 1, and so that’s why the values of h are so small. And so now just for completion sake, I want to do sort of what’s the opposite of blurring, I want to do edge detection of the image. So I want to find all the edges and set those values to 1. So matlab has a method called edge that will do this, and it’s very simple to use. You pass in again the gray-scale image, right, so only a two-dimensional matrix that won’t work if you pass in the entire image matrix. Ok, so, edge just does all of that automatically for you. So if imshow(E) I can more or less see where all the sharpest edges are, and of course there are parameters you can pass into edge to make it more or less sensitive, but this is by default what it does.
