Abstract
In this paper, knowledge-based recognition of objects in a bureau scene is studied and compared using two different systems on a common data set: In the first system active scene exploration is based on semantic networks and an A✻-control algorithm which uses color cues and 2-d image segmentation into regions. The other system is based on production nets and uses line extraction and views of 3-d polyhedral models. For the latter a new probabilistic foundation is given. In the experiments, wide-angle overviews are used to generate hypotheses. The active component then takes close-up views which are verified exploiting the knowledge bases, i.e. either the semantic network or the production net.
This work was partially supported under grand number NI 191/12 by Deutsche Forschungsgemeinschaft.
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Michaelsen, E., Ahlrichs, U., Stilla, U., Paulus, D., Niemann, H. (2001). Where Is the Hole Punch? Object Localization Capabilities on a Specific Bureau Task. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_45
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DOI: https://doi.org/10.1007/3-540-45404-7_45
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