Scalable Extrinsic Calibration of Omni-Directional Image Networks
International Journal of Computer Vision
Looking up data in P2P systems
Communications of the ACM
Data-centric storage in sensornets
ACM SIGCOMM Computer Communication Review
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Determining vision graphs for distributed camera networks using feature digests
EURASIP Journal on Applied Signal Processing
Multiview registration of 3D scenes by minimizing error between coordinate frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Communications Magazine
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This paper describes a scalable method of estimating a vision graph, in which a pair of camera nodes are connected by an edge if the two nodes share the same field of view, based on local image feature correspondences. The proposed method is implemented in a distributed fashion, meanwhile avoiding the flooding of the image feature information since it can be a bottleneck in achieving scalability. The key idea is to partition the image feature space into a set of disjoint regions so that the correspondence search can be carried out within a partitioned region, with each region served by a different network node independently. Simulated results using real images show that the proposed method achieves reasonable estimation performance while improving the traffic amount and traffic balance greatly.