ibarroda@csr (Ian Barrodale) (05/02/91)
Subject: matching sets of points in 2space
Keywords: control points matching
Point Set Matching
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(Barrodale Computing Services Ltd, May 1991).
Problem Definition:
We are given two sets of points in the plane. These points could represent
two `simplified' images or output from some sensors. The first set
contains M points. The second set is similar to the first set, except
that some of the points from the first set are missing and some new
points, not in the first set, are present. The second set contains N
points. The positions of the points in the second set are, within a
given tolerance, the same as common points in the first set. However,
within this tolerance fairly large local distortions can occur.
The problem has three parts:
1. Find all the points in the first set which do not have a match in
the second set.
2. Find all points in the second set which do not have a match in
the first set.
3. For all points in the first set which have a common point in the
second set find the correct match.
Questions:
We are interested in hearing from anyone who has worked on the above
problem or has worked on related problems. We are also interested in
looking at the possibility of using artificial intelligence
techniques, like neural networks, for solving the problem. The
following is a list of questions that we have at this time:
1. Should we try to emulate human vision in solving the above problem
or use only simple geometric considerations?
2. If the solution emulates human vision (i.e. does not directly use
coordinate information), what type of feature or point relationships
should it use?
3. If the solution uses coordinate information, what type of geometric
feature or geometric relationship would it be based on?
4. Can the given data be transformed such that the problem becomes
simpler? For example, can the problem be restate as the solution of
two one dimensional comparisons?
5. Can this problem be formulated such that it can be solved using a
Hough transform approach?
6. Can this problem be formulated such that it can be solved using a
neural network approach?
7. Can this problem be formulated such that it can be solved using
genetic algorithms?
8. What other types of approach may lead to a solution of the above
problem.
Thank you.
Ian Barrodale.
ibarroda@csr.uvic.ca
(604) 721-2206
Suite 8 - 1560 Church Avenue,
Victoria, B.C,
Canada, V8P 2Hi