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Active Object Recognition

F. Callari, F.P. Ferrie

In a typical scenario, an autonomous agent, capable of sensing visual information about its environment, is faced with the problem of recognizing objects from (geometrical) models of them. The sensors are affected by noise, the models are only approximate representations of the surrounding real scene, and both these sources of error cause uncertainty in the recovered models. Since the recognition of the objects is model-based, uncertainty in the models translates into ambiguity in the recognition: different objects may be equally likely to have produced the sensed data, given the available uncertain information. Yet a decision must be made about which objects are present in the scene and/or what further action must be taken. Building on previous results in the related field of active modeling, we propose a unified Bayesian methodology for actively inferring the objects from the data, and in so doing

We have shown that Extensive experiments performed have produced encouraging results, showing that the new active recognition technique outperforms a model-only based one, as well as simple ``random walk'' approaches.


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Next:Visual Modeling for CAD/CAM Up:Active Perception Previous:Optimal Sampling for Metrological
Annual Report

Fri Nov 26 23:00:32 GMT 1999