SMOOTHING AND MATCHING OF 3-D SPACE-CURVES

被引:72
作者
GUEZIEC, A [1 ]
AYACHE, N [1 ]
机构
[1] INRIA,EPIDAURE PROJECT,F-06902 SOPHIA ANTIPOLIS,FRANCE
关键词
D O I
10.1007/BF01420985
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 high curvature 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 Frenet 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 Frenet 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.
引用
收藏
页码:79 / 104
页数:26
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