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Daily life objects reveal natural similarities, which cannot be resolved with the perception of a single view. In this paper, we present an approach for object separation using active methods and multi-view object representations. By actively rotating an object, the coherence between controlled path, inner models, and percept is observed and used to reject implausible object hypotheses. Using the resulting object hypotheses, pose and object correspondence are determined. The proposed approach allows for the separation of different object candidates, which have similar views to the current percept. With the benefit of active methods the perceptual task can be solved using even coarse features, which facilitates a compact multi-view object representation. Furthermore, the approach is independent from a specific visual feature descriptor and thus suitable for multi-modal object recognition.