Abstract
We address the problem of recognition of generic objects from a single intensity image. This precludes the use of purely geometric methods which assume that models are geometrically and precisely designed. Instead, we propose to use descriptions in terms of features and their qualitative geometric relationships. We propose to detect groups using perceptual organization criteria such as proximity, symmetry, parallelism, and closure. The detection of these features is performed in an efficient way using proximity indexing. Since many groups are created, we also perform selection of relevant groups by organizing them into sets of similar perceptual content. Finally we present an initial implementation of a recognition system using these sets as primitives. It is an efficient colored graph matching algorithm using the adjacency matrix representation of a graph. Using indexing, we retrieve matching hypotheses, which are verified against each other with respect to topological constraints. Groups of consistent hypotheses represent detected model instances in a scene. The complete system is illustrated on real images. We also discuss further extensions.
This research was supported by the Advanced Research Projects Agency of the Department of Defence and was monitored by the Air Force office of Scientific Research under Contract No. F49620-90-C-0078 and by a NSF Grant under award No. IRI-9024369.
Fridtjof Stein is currently with Daimler Benz, Germany.
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© 1994 Springer-Verlag Berlin Heidelberg
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Havaldar, P., Medioni, G., Stein, F. (1994). Extraction of groups for recognition. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57956-7_30
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DOI: https://doi.org/10.1007/3-540-57956-7_30
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