Abstract
We propose a new relaxation scheme for graph matching in computer vision. The main distinguishing feature of our approach is that matching is formulated as a process of eliminating unlikely candidates rather than finding the best match directly. Bayesian development leads to a robust algorithm which can be implemented in a fast and efficient manner on a neural network architecture. We illustrate the utility of the technique through comparisons with its conventional counterpart on simulated and real-world data.
Original language | English |
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Pages (from-to) | 238-248 |
Number of pages | 11 |
Journal | Neural computing & applications |
Volume | 7 |
Issue number | 3 |
Publication status | Published - 1998 |
Keywords
- correlation matrix memories
- graph matching
- relaxation labelling
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