Viewpoint Selection - Planning Optimal Sequences of Views for Object Recognition

In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition. In this context there are several unique problems to be solved, like the fusion of several views and the selection of the best next viewpoint. In this paper we present an approach to solve the problem of choosing optimal views (viewpoint selection) and the fusion of these for an optimal 3D object recognition (viewpoint fusion). We formally define the selection of additional views as an optimization problem and we show how to use reinforcement learning for viewpoint training and selection in continuous state spaces without user interaction. We also present an approach for the fusion of multiple views based on recursive density propagation. The experimental results show that our viewpoint selection is able to select a minimal number of views and perform an optimal object recognition with respect to the classification.