| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
H. Borotschnig and M. {Paletta, L.and Prantl} and A. Pinz
ABSTRACT
One major goal of active object recognition systems is to extract useful information frommultiplemeasurements. We compare three frameworks for information fusion and view-planning using different uncertainty calculi: probability theory, possibility theory andDempster-Shafer theory of evidence. The system dynamically repositions the camera to capture additional views in order to improve the classifi- cation result obtained from a single view. The active recognition problem can be tackled successfully by all the considered approaches with sometimes only slight differences in performance. Extensive experiments confirm that recognition rates can be improved considerably by performing active steps. Random selection of the next action is much less efficient than planning, both in recognition rate and in the average number of steps required for recognition. As long as the rate of wrong object-pose classifications stays low the probabilistic implementation always outperforms the other approaches. If the outlier rate increases averaging fusion schemes outperform conjunctive approaches for information integration. We use an appearance based object representation,namely the parametric eigenspace, but the planning algorithm is actually independent of the details of the specific object recognition environment. |
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