Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Point reconstruction from noisy images
Journal of Mathematical Imaging and Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning to estimate scenes from images
Proceedings of the 1998 conference on Advances in neural information processing systems II
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust probabilistic inference in distributed systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed metric calibration of ad hoc camera networks
ACM Transactions on Sensor Networks (TOSN)
Determining vision graphs for distributed camera networks using feature digests
EURASIP Journal on Applied Signal Processing
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Distributed metric calibration of ad hoc camera networks
ACM Transactions on Sensor Networks (TOSN)
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Distributed consensus on camera pose
IEEE Transactions on Image Processing
Event prediction in a hybrid camera network
ACM Transactions on Sensor Networks (TOSN)
Rotation averaging with application to camera-rig calibration
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Quorum based image retrieval in large scale visual sensor networks
ADHOC-NOW'12 Proceedings of the 11th international conference on Ad-hoc, Mobile, and Wireless Networks
Collaborative localization in visual sensor networks
ACM Transactions on Sensor Networks (TOSN)
Hi-index | 0.00 |
We discuss how to obtain the accurate and globally consistent self-calibration of a distributed camera network, in which camera nodes with no centralized processor may be spread over a wide geographical area. We present a distributed calibration algorithm based on belief propagation, in which each camera node communicates only with its neighbors that image a suffcient number of scene points. The natural geometry of the system and the formulation of the estimation problem give rise to statistical dependencies that can be efficiently leveraged in a probabilistic framework. The camera calibration problem poses several challenges to information fusion, including overdetermined parameterizations and nonaligned coordinate systems. We suggest practical approaches to overcome these difficulties, and demonstrate the accurate and consistent performance of the algorithm using a simulated 30-node camera network with varying levels of noise in the correspondences used for calibration, as well as an experiment with 15 real images.