Self-calibration from multiple views with a rotating camera
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Extrinsic Calibration of Omni-Directional Image Networks
International Journal of Computer Vision
Multiview Registration of 3D Scenes by Minimizing Error between Coordinate Frames
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Accurate internal camera calibration using rotation, with analysis of sources of error
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Approximate initialization of camera sensor networks
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
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With the recent development of hardware technologies, vision sensor networks (VSNs) are widely deployed to communicate with environments. One of the key issues in a VSN is to build a topology graph and localize vision sensors in the network precisely and dynamically. This paper proposes a framework for estimating a topology graph for a VSN in a dynamic configuration and localizing vision sensors using the topology graph. In the paper, it is assumed that intrinsic parameters of each vision sensor are already known, only one vision sensor is localized, and each vision sensor is overlapped with at least one vision sensor. In order to determine the position and orientation of the rest of the vision sensors, localization information of the localized one in the network is propagated to the rest of vision sensors. The amount of arithmetic calculation needed for the method is small and hence can be adopted to low power processors. The accuracy and reliability of the method have been validated by performing Visual Sensor Network, Localization, Propagation, Dynamic Topology Estimation experiments with real images. The framework has been proven practical on a VSN.