Viewpoint selection by navigation through entropy maps

In this paper, we show how entropy maps can be used to guide an active observer along an optimal trajectory, by which the identity and pose of objects in the world can be inferred with confidence, while minimizing the amount of data that must be gathered. Specifically we consider the case of active object recognition where entropy maps are used to encode prior knowledge about the discriminability of objects as a function of viewing position. The paper describes how these maps are computed using optical flow signatures as a case study, and how a gaze-planning strategy can be formulated by using entropy minimization as a basis for choosing a next best view. Experimental results are presented which show the strategy's effectiveness for active object recognition using a single monochrome television camera.