In the previous chapter, we learned how it is possible to detect the edges of an image. In particular, we showed you that by applying a threshold on the gradient magnitude, a binary map of the main edges of an image can be obtained. Edges carry important visual information since they delineate the image elements. For this reason, they can be used, for example, in object recognition. However, simple binary edge maps suffer from two main drawbacks. First, the edges that are detected are unnecessarily thick; this makes the object's limit more difficult to identify. Second, and more importantly, it is often impossible to find a threshold that is sufficiently low in order to detect all important edges of an image and is, at the same time, sufficiently high in order to not include too many insignificant edges. This is a trade-off problem that the Canny algorithm tries to solve.