Aspects of Optimal Viewpoint Selection and Viewpoint Fusion

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, such as the fusion of views and the selection of an optimal 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. In this context we focus on the modeling of the reinforcement learning reward. We also present an approach for the fusion of multiple views based on density propagation, and discuss the advantages and disadvantages of two approaches for the practical evaluation of these densities, namely Parzen estimation and density trees.