## Documentation Center |

In an image, an edge is a curve that follows a path of rapid change in image intensity. Edges are often associated with the boundaries of objects in a scene. Edge detection is used to identify the edges in an image.

To find edges, you can use the `edge` function.
This function looks for places in the image where the intensity changes
rapidly, using one of these two criteria:

Places where the first derivative of the intensity is larger in magnitude than some threshold

Places where the second derivative of the intensity has a zero crossing

`edge` provides a number of derivative estimators,
each of which implements one of the definitions above. For some of
these estimators, you can specify whether the operation should be
sensitive to horizontal edges, vertical edges, or both. `edge` returns
a binary image containing 1's where edges are found and 0's elsewhere.

The most powerful edge-detection method that `edge` provides
is the Canny method. The Canny method differs from the other edge-detection
methods in that it uses two different thresholds (to detect strong
and weak edges), and includes the weak edges in the output only if
they are connected to strong edges. This method is therefore less
likely than the others to be fooled by noise, and more likely to detect
true weak edges.

This example shows the power of the Canny edge detector by comparing the results of applying the Sobel and Canny edge detectors to the same image:

Read image and display it.

```
I = imread('coins.png');
imshow(I)
```

Apply the Sobel and Canny edge detectors to the image and display them.

BW1 = edge(I,'sobel'); BW2 = edge(I,'canny'); imshow(BW1) figure, imshow(BW2)

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