Abstract
Introducing general CAD descriptions in object recognition systems has become a major field of research called CAD based vision (CBV). However, the major problem using free-form object descriptions is how to define recognizable features which can be extracted from sensor data. In this paper we propose new methods for an extraction of 3d space curves from CAD models and from range data. Object identification is performed by correlating feature vectors from significant subcurves. However, features relying on differential surface properties tend to be very vulnerable with respect to noise. To cope with erroneous data we propose to model the statistical behavior of the features using a Bayesian network. Thus, providing a robust and powerful CAD based 3d object recognition system.
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© 1997 Springer-Verlag Berlin Heidelberg
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Krebs, B., Burkhardt, M., Wahl, F.M. (1997). A Bayesian network for 3d object recognition in range data. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_138
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DOI: https://doi.org/10.1007/3-540-63460-6_138
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