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We address the problem of distributed matching of features in networks withvision systems. Every camera in the network has limited communicationcapabilities and can only exchange local matches with its neighbors. We proposea distributed algorithm that takes these local matches and computes globalcorrespondences by a proper propagation in the network. When the algorithmfinishes, each camera knows the global correspondences between its features andthe features of all the cameras in the network. The presence of spuriousintroduced by the local matcher may produce inconsistent globalcorrespondences, which are association paths between features from the samecamera. The contributions of this work are the propagation of the local matchesand the detection and resolution of these inconsistencies by deleting localmatches. Our resolution algorithm considers the quality of each local match,when this information is provided by the local matcher. We formally prove thatafter executing the algorithm, the network finishes with a global dataassociation free of inconsistencies. We provide a fully decentralized solutionto the problem which does not rely on any particular communication topology.Simulations and experimental results with real images show the performance ofthe method considering different features, matching functions and scenarios.

Distributed Multi-view Matching in Networks with Limited Communications