Maarten Korsten

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We propose a technique that combines geometric hashing with stereo vision. The idea is to use the robustness of geometric hashing to spurious data to overcome the correspondence problem, while the stereo vision setup enables direct model matching using the 3-D object models. Furthermore, because the matching technique relies on the relative positions of(More)
We develop a model for predicting the probability of incorrect, random matches when using a geometric hashing based recognition scheme. To estimate the vote for random matches we approximate the voting function by a discrete function and use the bino-mial distribution. The resulting probability distribution of votes for random matches is compared with(More)
In this paper we compare our 3-D geometric hashing approach to object recognition to a 2-D geometric hashing approach developed by Gavrila and Groen. We apply both methods to same recognition task using real images. We found that the indexing technique used in geometric hashing is much more eecient in the 3-D case than it is in the 2-D case. Furthermore,(More)
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