Hausdorff Distance Proofs

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Then, use the definition of d(A,B) in terms of a(A,B) to show the full triangle inequality for d(A,B).In summary, given a compact set A\subset\Re^{n} and a point x\in\Re^{n}, the distance from x to A is defined as d(x, A)=inf({\left\|x-y\right\|: y\inA}). For two compact sets A, B \subset\Re^{n}, the Hausdorff distance between them is defined as d(A, B)=max(sup{d(x, B) : x\inA}, sup{d(x, A) : x\inB}). It is proved that
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diracy
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Homework Statement


Given a compact set A[itex]\subset[/itex][itex]\Re[/itex][itex]^{n}[/itex] and a point x[itex]\in[/itex][itex]\Re[/itex][itex]^{n}[/itex] define the distance from x to A as the quantity:

d(x, A)=inf({[itex]\left\|[/itex]x-y[itex]\right\|[/itex]: y[itex]\in[/itex]A})

Given two compact sets A, B [itex]\subset[/itex][itex]\Re[/itex][itex]^{n}[/itex], define the Hausdorff distance between them to be:

d(A, B)=max(sup{d(x, B) : x[itex]\in[/itex]A}, sup{d(x, A) : x[itex]\in[/itex]B})

a. For any compact set A, prove that the function f : [itex]\Re[/itex][itex]^{n}[/itex][itex]\rightarrow[/itex][itex]\Re[/itex] given by: f(x)=d(x, A) is continuous.

b. For any two compact sets A, B it's true that: d(A, B)<[itex]\infty[/itex]

c. For any two compact sets A, B it's true that d(A, B)=0 if and only if A=B.

Homework Equations


The Attempt at a Solution



I think I handled part a. I'm just completely lost on b and c. Any help?
 
Last edited:
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  • #2
The \left\ and \right\ should be norm.
 
  • #3
Edited for a typo.
 
  • #4
For (b), you need to show that

[tex]\sup_{x\in A}{d(x,B)}[/tex]

is bounded and equivalently that

[tex]\sup_{x\in B}{d(x,A)}[/tex]

is bounded. Now, can you use (a) to show this?
 
  • #5
micromass said:
For (b), you need to show that

[tex]\sup_{x\in A}{d(x,B)}[/tex]

is bounded and equivalently that

[tex]\sup_{x\in B}{d(x,A)}[/tex]

is bounded. Now, can you use (a) to show this?

I got it! I really appreciate the help. I have one more question.

How do I show the following:

For any three compact sets: A, B, C we have a triangle inequality: d(A, C)[itex]\leq[/itex] d(A,B) + d(B, C)
 
  • #6
Let

[tex]a(A,B)=\sup_{a\in A}{d(a,B)}[/tex]

(then [itex]d(A,B)=\max{a(A,B),a(B,A)}[/itex]).

Try to show first that

[tex]a(A,B)\leq a(A,C)+a(C,B)[/tex]

Thus, show the triangle inequality first for a.
 

1. What is Hausdorff distance and why is it important in scientific research?

Hausdorff distance is a mathematical concept used to measure the difference between two sets of points in a metric space. It is important in scientific research because it allows for the quantification of similarity or dissimilarity between two sets of data, which can be useful in various fields such as image processing, data analysis, and pattern recognition.

2. How is Hausdorff distance calculated and what are its properties?

Hausdorff distance is calculated by finding the maximum distance between any point in one set and its nearest point in the other set. Its properties include symmetry, non-negativity, and the ability to measure the distance between sets of different sizes.

3. What are some common applications of Hausdorff distance in scientific research?

Hausdorff distance has many applications in scientific research, including image registration, shape matching, object tracking, and clustering. It is also used in computer vision, medical imaging, and terrain analysis.

4. What are the limitations of using Hausdorff distance in data analysis?

One limitation of using Hausdorff distance is that it only considers the distance between the two sets of points and does not take into account the spatial arrangement or order of the points. Additionally, it may not be suitable for comparing sets with significantly different sizes or shapes.

5. How can Hausdorff distance proofs be validated and tested?

Hausdorff distance proofs can be validated and tested through mathematical analysis, simulations, and experiments. This may involve comparing the results of Hausdorff distance calculations with known solutions or using synthetic data with known distances between sets to validate the accuracy of the proof.

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