Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Localization and homing using combinations of model views
Artificial Intelligence - Special volume on computer vision
ACM Transactions on Graphics (TOG)
Complete Calibration of a Multi-camera Network
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Synchronization and Calibration of Camera Networks from Silhouettes
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Temporal Synchronization of Video Sequences in Theory and in Practice
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. Cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. We propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. The images are decomposed sequentially into layers where redundant information are discarded. Without the need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. The information sent by the network is then represented by a reduced and compact amount of data using a codification process. This framework allows structures to be retrieved and also the topology of the network. It can also provide the localization and trajectories of mobile objects. Experiments will present practical results in the case of a network containing 20 cameras observing a common scene.