Section An Application of Compactness: Fractals
Introduced by Felix Hausdorff in the early 20th century as a way to measure the distance between sets, the Hausdorff metric (also called the Pompeiu-Hausdorff metric) has since been widely studied and has many applications. For example, the United States military has used the Hausdorff distance in target recognition procedures. In addition, the Hausdorff metric has been used in image matching and visual recognition by robots, medicine, image analysis, and astronomy.
The basic idea in these applications is that we need a way to compare two shapes. For example, if a manufacturer needs to mill a specific product based on a template, there is usually some tolerance that is allowed. So the manufacturer needs a way to compare the milled parts to the template to determine if the tolerance has been met or exceeded.
The Hausdorff metric is also familiar to fractal aficionados for describing the convergence of sequences of compact sets to their attractors in iterated function systems. The variety of applications of this metric make it one that is worth studying.
To define the Hausdorff metric, we begin with the distance from a point in a metric space to a subset of as
Since images will be represented as compact sets, we restrict ourselves to compact subsets of a metric space. In this case the infimum becomes a minimum and we have
We now extend that idea to define the distance from one subset of to another. Let and be nonempty compact subsets of To find the distance from the set to the set it seems reasonable to consider how far each point in is from the set Then the distance from to should measure how far we have to travel to get from any point in to
Activity 17.8.
(a)
A problem with as in Definition 17.16 is that is not symmetric. Find examples of compact subsets and of with the Euclidean metric such that
(b)
Even though the function in Definition 17.16 is not a metric, we can define the Hausdorff distance in terms of as follows.
Definition 17.17.
Let be the circle in centered at the origin with radius 1, let be the inscribed square, and let as shown in Figure 17.18.
(c)
It may be surprising that as in Definition 17.17 is actually a metric, but it is. The underlying space is the collection of nonempty compact subsets of which we denote at Prove the following theorem.
Theorem 17.19.
(d)
One fun application of the Hausdorff metric is in fractal geometry. You may be familiar with objects like the Sierpinski triangle or the Koch curve. These objects are limits of sequences of sets in We illustrate with the Sierpinski triangle. Start with three points and that form the vertices of an equilateral triangle For =1,2, or 3, let For =1,2, or 3, we define by
Then when applied to contracts by a factor of 2 and then translates the image of so that the vertex of and the th vertex of the image of coincide. Such a map is called a contraction mapping with contraction factor equal to Define to be Then is the set of all points half way between any point in and or is a triangle half the size of the original translated to the vertex of the original. Let Both and are shown in Figure 17.20. We can continue this procedure replacing with In other words, for = 1, 2, and 3, let Then let A picture of is shown in Figure 17.20. We can continue this procedure, each time replacing with A picture of is shown in Figure 17.20.
To continue this process, we need to take a limit. But the are sets in so the limit is taken with respect to the Hausdorff metric.
(i)
(ii)
The Sierpinski triangle will exist if the sequence converges to a set (which would be the Sierpinski triangle). The question of convergence is not a trivial one.
(A)
Consider the sequence where for Note that each is a rational number. Explain why the terms in this sequence get arbitrarily close together, but the sequence does not converge in Explain why the sequence converges in
(B)
A sequence in a metric space is a Cauchy sequence if given there exists such that whenever Every convergent sequence is a Cauchy sequence. A metric space is said to be complete if every Cauchy sequence in converges to an element in For example, is complete while is not. Although we will not prove it, the metric space is complete. Show that the sequence is a Cauchy sequence in The limit of this sequence is the famous Sierpinski triangle. The picture of in Figure 17.20 is a close approximation of the Sierpinski triangle.