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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. One of them is how to fuse a series of images that might differ in their viewpoints. In this paper we present a well-founded approach for the fusion of multiple views based on a recursive density propagation method. It uses particle filters for solving the fusion in a continuous pose space. Furthermore we will show by means of a statistical object recognition system how to integrate such systems into our fusion approach. The experimental result will show, how the fusion can improve classification rates substantial, es-pecially for difficult conditions like heterogeneous background within real world environments.
In this paper we address the classification of 3–D objects that look similar from sev-eral sights and can only be distinguished from some certain viewpoints. For this purpose we combine a statistical appearance-based object recognition approach with an active viewpoint selection mechanism. For appearance-based object recognition lo-cal features are derived from wavelet multires-olution analysis. The recognition process is performed hierarchically in a statistical frame-work by a maximum likelihood estimation. Based on this result the active viewpoint se-lection mechanism chooses one further view that allows a reliable classification. Hereby the viewpoint selection mechanism can be trained unsupervised and represents the space of possible viewpoints continuously. Experimental results show that our approach is well suited for a reliable classifica-tion of similar looking objects only by one fur-ther view.