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首页> 外文会议>European Conference on Computer Vision(ECCV 2006) pt.3; 20060507-13; Graz(AT) >Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction
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Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction

机译：多点集和地图集构造的同时非刚性配准

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摘要

Estimating a meaningful average or mean shape from a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence under a non-rigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape from multiple unlabeled point-sets (represented by finite mixtures) and registering them nonrigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets represented as finite mixtures. We derive the analytic gradient of the cost function namely, the JS-divergence, in order to efficiently achieve the optimal solution. The cost function is fully symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on non-rigidly registering 2D as well as 3D real data (point sets).
机译：从未标记的点集表示的一组形状中估计有意义的平均或平均形状是一个具有挑战性的问题，因为通常这涉及求解非刚性运动设置下的点对应关系。在本文中，我们提出了一种新颖而强大的算法，该算法能够同时从多个未标记的点集（由有限混合表示）中计算平均形状并将其非刚性地注册到这个新兴的平均形状中。该算法通过最小化表示为有限混合的点集之间的Jensen-Shannon（JS）散度来避免对应问题。为了有效地获得最优解，我们导出了成本函数的解析梯度，即JS散度。成本函数是完全对称的，对于要记录的任何给定形状没有偏差，并且正在寻找其均值。我们的算法对于创建图像中存在的各种形状的地图集以及同时（刚性或非刚性）注册3D范围数据集而无需建立任何对应关系特别有用。我们介绍了非刚性注册2D和3D真实数据（点集）的实验结果。

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