Abstract
Recognition of generic three-dimensional objects remains an unsolved problem. Scenes containing multiple nonpolyhedral 3D objects are particularly challenging. Conventional object models based on straight line segments and junctions are not suitable for this task. We have developed an appearanced-based 3D object model in which an object is represented by the features that can be most reliably detected in a training set of real images. For industrial objects with both flat and curved surfaces, holes, and threads, a set of useful features has been derived; and a recognition system utilizing these features and their interrelationships is being developed. The recognition system uses small relational subgraphs of features to index the database of models and to retrieve the appropriate 3D models in a hypothesize- and-test matching algorithm. This paper describes the new models, the matching algorithm, and our preliminary results.
This research was supported by the National Science Foundation under grant number IRI-9023977, by the Boeing Commercial Airplane Group, and by the Washington Technology Center.
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© 1995 Springer-Verlag Berlin Heidelberg
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Costa, M.S., Shapiro, L.G. (1995). Analysis of scenes containing multiple non-polyhedral 3D objects. In: Braccini, C., DeFloriani, L., Vernazza, G. (eds) Image Analysis and Processing. ICIAP 1995. Lecture Notes in Computer Science, vol 974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60298-4_270
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DOI: https://doi.org/10.1007/3-540-60298-4_270
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