Uncertainty in Pose Estimation: A Bayesian Approach
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Dynamic learning of action patterns for object acquisition
International Journal of Intelligent Systems Technologies and Applications
Robust sequential view planning for object recognition using multiple cameras
Image and Vision Computing
Estimating pose through local geometry
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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Recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment. In this work an information-based utility measure is used to derive from a learned classification of shape models an efficient data collection strategy, specifically aimed at increasing classification confidence when recognizing uncertain shapes. Promising simulation results are presented and discussed.