FINDING POINT CORRESPONDENCES USING SIMULATED ANNEALING

Identifying corresponding points between two recordings of a point set has always been an important problem in stereo vision applications. We describe this matching problem in terms of cost minimization and present an algorithm to approach the minimal cost mapping using simulated annealing. The algorithm calculates the costs to match all possible point pairs and tries to minimize the sum of the costs of all matched points. Starting from an initial mapping, it uses a random rearrangement scheme to alter the mapping towards the optimal (minimal cost) mapping.