Abstract
Model-based recognition methods generally search for geometrically consistent pairs of model and image features. The quality of an hypothesis is then measured using some function of the number of model features that are paired with image features. The most common approach is to simply count the number of pairs of consistent model and image features. However, this may yield a large number of feature pairs, due to a single model feature being consistent with several image features and vice versa. A better quality measure is provided by the size of a maximal bipartite matching, which eliminates the multiple counting of a given feature. Computing such a matching is computationally expensive, but under certain conditions it is well approximated by the number of distinct features consistent with a given hypothesis.
This report describes research done in part at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's research is provided in part by an ONR URI grant under contract N00014-86-K-0685, and in part by DARPA under Army contract number DACA76-85-C-0010 and under ONR contract N00014-85-K-0124. DPH is supported at Cornell University in part by NSF grant IRI-9057928 and matching funds from General Electric and Kodak, and in part by AFOSR under contract AFOSR-91-0328.
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© 1992 Springer-Verlag Berlin Heidelberg
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Huttenlocher, D.P., Cass, T.A. (1992). Measuring the quality of hypotheses in model-based recognition. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_87
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DOI: https://doi.org/10.1007/3-540-55426-2_87
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