Question 2: Edges detection using intensity gradients (20 points) This question requires you to work with a real image. Use your cell phone or a digital camera to take an image of a scene that contains many objects, some of which have a long line structures. For example, the objects might be books, window frame, doorway, shelf, etc. Read the image into Matlab, and resize it with the builtin imresizefunction so that you can display it in full resolution. A typical iphone picture is 12 MP is too big to show on your laptop screen in full resolution, and which could take long to process anyhow. So, for example, you could resizse such a 3k x 4k image to 300x400. Decompose the image into RGB channels. Take the green channel, make a gray scale JPG image of it. and include this in your submission. You will work with just that channel. Filter the image with a Gaussian and take local differences (central as in the lectures) in the xand y directions. (This gives you the intensity gradient at each pixel.) Show the pair of filtered images in your PDF. Compute the gradient magnitude and the gradient orientation at each pixel. The orientation is an angle spanning a range of 2 radians. Mark each pixel as an edge if its gradient magnitude exceeds some large threshold. Choose the threshold by hand so that the computed edges roughly correspond to the edges that you perceive when you look at the image. Show the resulting binary image which indicates the edge positions. Repeat the filtering for two more values of that are 2x and 4x as big as what you used originally. Show the resulting edge maps and discuss any differences in the results. You don't need to show all previous images -just the edge maps. Question 2: Edges detection using intensity gradients (20 points) This question requires you to work with a real image. Use your cell phone or a digital camera to take an image of a scene that contains many objects, some of which have a long line structures. For example, the objects might be books, window frame, doorway, shelf, etc. Read the image into Matlab, and resize it with the builtin imresizefunction so that you can display it in full resolution. A typical iphone picture is 12 MP is too big to show on your laptop screen in full resolution, and which could take long to process anyhow. So, for example, you could resizse such a 3k x 4k image to 300x400. Decompose the image into RGB channels. Take the green channel, make a gray scale JPG image of it. and include this in your submission. You will work with just that channel. Filter the image with a Gaussian and take local differences (central as in the lectures) in the xand y directions. (This gives you the intensity gradient at each pixel.) Show the pair of filtered images in your PDF. Compute the gradient magnitude and the gradient orientation at each pixel. The orientation is an angle spanning a range of 2 radians. Mark each pixel as an edge if its gradient magnitude exceeds some large threshold. Choose the threshold by hand so that the computed edges roughly correspond to the edges that you perceive when you look at the image. Show the resulting binary image which indicates the edge positions. Repeat the filtering for two more values of that are 2x and 4x as big as what you used originally. Show the resulting edge maps and discuss any differences in the results. You don't need to show all previous images -just the edge maps