Chen Jiun-Hung, Shapiro Linda G
Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5657-60. doi: 10.1109/IEMBS.2009.5333769.
Finding point correspondences plays an important role in automatically building statistical shape models from a training set of 3D surfaces. Davies et al. assumed the projected coefficients have a multivariate Gaussian distributions and derived an objective function for the point correspondence problem that uses minimum description length to balance the training errors and generalization ability. Recently, two-dimensional principal component analysis has been shown to achieve better performance than PCA in face recognition. Motivated by the better performance of 2DPCA, we generalize the MDL-based objective function to 2DPCA in this paper. We propose a gradient descent approach to minimize the objective function. We evaluate the generalization abilities of the proposed and original methods in terms of reconstruction errors. From our experimental results on different sets of 3D shapes of different human body organs, the proposed method performs significantly better than the original method.
寻找点对应关系在从一组三维表面训练集中自动构建统计形状模型中起着重要作用。戴维斯等人假设投影系数具有多元高斯分布，并推导了用于点对应问题的目标函数，该函数使用最小描述长度来平衡训练误差和泛化能力。最近，二维主成分分析已被证明在人脸识别中比主成分分析具有更好的性能。受二维主成分分析更好性能的启发，我们在本文中将基于最小描述长度的目标函数推广到二维主成分分析。我们提出了一种梯度下降方法来最小化目标函数。我们根据重建误差评估所提出方法和原始方法的泛化能力。从我们在不同人体器官的不同三维形状集上的实验结果来看，所提出的方法比原始方法表现得明显更好。
