Camera calibration based on receptive fields
Pattern Recognition
Estimation and decision fusion: A survey
Neurocomputing
Incremental learning method for unified camera calibration
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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This paper addresses multi-sensor data fusion with incremental learning ability. A new cost function is proposed for the receptive field weighted regression (RFWR) algorithm based on the idea of back propagation (BP), so that the computation efficiency and the learning strategy of the modified RFWR are much more applicable for multi-sensor data fusion problem. Thus a new fusion structure and algorithm with incremental learning ability is constructed by adopting the modified RFWR algorithm together with the weighted average algorithm. Experiments of a two-camera unified positioning system are implemented successfully to test the proposed computation structure and algorithms.