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From uncertainty to visual exploration

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2 Author(s)
P. Whaite ; Comput. & Robotics Lab., McGill Univ. Res. Center for Intelligent Machines, Montreal, Que., Canada ; F. P. Ferrie

The authors attempt to determine what can be inferred from ambiguity in processes of visual interpretation. They discuss this question in a specific context: the interpretation of scene geometry in the form of parametrized volumetric models. Ambiguity is described as a local probabilistic property of the misfit error surface in the parameter space of superellipsoid models, namely, as an ellipsoid of confidence in which there is a given probability that the true parameters can be found. The authors show how to project the ellipsoid of confidence back into 3D space to obtain the shell in which the true 3D surface most probably lies and introduce what they call the uncertainty as a local property of the fitted model's surface. They propose a technique that can use this information to plan a new direction of view that minimizes the ambiguity of subsequent interpretation

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:13 ,  Issue: 10 )