Lab 4: Edge Detection in Images

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Lab 4: Edge Detection in Images

A. Background Information

Edge detection is a common, but very important method for analyzing images. It can be used to help identify objects in a scene or features of an object. It is widely used in biometrics security applications to identify facial features or fingerprints. The example below shows the process of edge detection applied to a picture of a bike next to a brick wall. As can be seen, edge detection easily shows the outline of the bike, the bricks in the wall, and even the writing on the bike.

| | Edge Detection Example: Original Image (left) and Edge Detected Image (right) | So how does edge detection work? The first step in understanding the process is to understand what an edge is in an image. If you were to look at the values of in an image along a single row or column, you would see something that looks like this:
Bricks
Bricks
Grout Lines
Grout Lines

This is actually the graph of the pixel values from the first row of the original picture shown above. What you are seeing here are the individual dark colored bricks that make up the top border of the image. The bricks themselves are the lower and relatively flat sections, while the lighter colored grout between the bricks is represented by the sharp peaks. With this understanding, we can see that an edge (a sharp change in color) is represented by a large change in the pixel value.

In order to locate the areas that have these drastic changes in color, we need to apply a mathematical operation to the image. In this case, since we want to know the change in the color values, we will be using a derivative, since a derivative is large when there is a drastic change (an edge) and small when there is little change (not an edge).

So far, we’ve only been applying derivatives to 1-dimensional data using the 2 point and 3 point…...

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