Smoothing and Matching of 3-D Space Curves

André Guéziec 1 Nicholas Ayache 1, *
* Auteur correspondant
1 EPIDAURE - Medical imaging and robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We present a new approach to the problem of matching 3-D curves. The approach has a low algorithmic complexity in the number of models, and can operate in the presence of noise and partial occlusions. Our method builds upon the seminal work of Kishon et al. (1990), where curves are first smoothed using B-splines, with matching based on hashing using curvature and torsion measures. However, we introduce two enhancements: -- We make use of nonuniform B-spline approximations, which permits us to better retain information at highcurvature locations. The spline approximations are controlled (i.e., regularized) by making use of normal vectors to the surface in 3-D on which the curves lie, and by an explicit minimization of a bending energy. These measures allow a more accurate estimation of position, curvature, torsion, and Frtnet frames along the curve. -- The computational complexity of the recognition process is relatively independent of the number of models and is considerably decreased with explicit use of the Frtnet frame for hypotheses generation. As opposed to previous approaches, the method better copes with partial occlusion. Moreover, following a statistical study of the curvature and torsion covariances, we optimize the hash table discretization and discover improved invariants for recognition, different than the torsion measure. Finally, knowledge of invariant uncertainties is used to compute an optimal global transformation using an extended Kalman filter. We present experimental results using synthetic data and also using characteristic curves extracted from 3-D medical images. An earlier version of this article was presented at the 2nd European Conference on Computer Vision in Italy.
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Soumis le : mercredi 17 août 2011 - 23:22:36
Dernière modification le : samedi 27 janvier 2018 - 01:30:42
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André Guéziec, Nicholas Ayache. Smoothing and Matching of 3-D Space Curves. International Journal of Computer Vision, Springer Verlag, 1994, 12 (1), pp.79-104. 〈10.1007/BF01420985〉. 〈inria-00615051〉



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