Abstract
This work deals with the clustering of information sources for epipole estimation in a multi-camera system. For this problem, each pair of matched visual features in the images can be considered as an elementary information source. The epipole is then estimated by combining these elementary sources taking into account their inadequacy, in particular large imprecision and presence of outliers, as well as the very large number of sources. We address the challenges introduced by a large number of sources with a strategy based on clustering and intra-cluster fusion using the Belief Functions framework. When evaluated on real data, the proposed algorithm exhibits more robustness in terms of accuracy and precision than the standard approaches which provide singular solutions.
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