基于潜根回归模型的颅面重建预测
Craniofacial reconstruction as a prediction problem using a Latent Root Regression model
摘要：
在本文中，我们提出了一种计算机辅助的面部重建方法。这种方法提供了一个估计的面部形状与身份不明的骨骼遗骸。目前使用统计框架的计算机辅助方法依赖于位于骨骼和软组织表面上的一组常见的提取点。大多数的面部重建方法，然后包括预测的软组织表面点的位置，当骨表面点的位置是已知的。我们建议使用潜根回归进行预测。然后将所得结果与主成分分析线性模型的结果进行比较。同时，我们评估了所使用的颅骨标志的数量的影响。解剖颅骨标志由位于测地线上的点迭代完成，测地线连接这些解剖标志，从而使我们能够人为地增加颅骨点的数量。面部点是使用一个共同的参考网格和个人的软组织表面网格之间的网格匹配算法。所提出的方法进行了验证的准确性，基于留一交叉验证测试应用于同质数据库。通过计算原始人脸表面与其重建表面之间的距离来获得精度度量。最后，这些结果进行了讨论，参考目前的计算机辅助重建面部技术。
英文摘要：
In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points located on the bone and soft-tissue surfaces. Most of the facial reconstruction methods then consist of predicting the position of the soft-tissue surface points, when the positions of the bone surface points are known. We propose to use Latent Root Regression for prediction. The results obtained are then compared to those given by Principal Components Analysis linear models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics which link these anatomical landmarks, thus enabling us to artificially increase the number of skull points. Facial points are obtained using a mesh-matching algorithm between a common reference mesh and individual soft-tissue surface meshes. The proposed method is validated in term of accuracy, based on a leave-one-out cross-validation test applied to a homogeneous database. Accuracy measures are obtained by computing the distance between the original face surface and its reconstruction. Finally, these results are discussed referring to current computer-assisted reconstruction facial techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
