Malcolm James, Rathi Yogesh, Tannenbaum Allen
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0250.
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008:1-8. doi: 10.1109/CVPR.2007.383404.
This paper proposes a novel method to apply the standard graph cut technique to segmenting multimodal tensor valued images. The Riemannian nature of the tensor space is explicitly taken into account by first mapping the data to a Euclidean space where non-parametric kernel density estimates of the regional distributions may be calculated from user initialized regions. These distributions are then used as regional priors in calculating graph edge weights. Hence this approach utilizes the true variation of the tensor data by respecting its Riemannian structure in calculating distances when forming probability distributions. Further, the non-parametric model generalizes to arbitrary tensor distribution unlike the Gaussian assumption made in previous works. Casting the segmentation problem in a graph cut framework yields a segmentation robust with respect to initialization on the data tested.
本文提出了一种将标准图割技术应用于多模态张量值图像分割的新方法。通过首先将数据映射到欧几里得空间，明确考虑了张量空间的黎曼性质，在该空间中可以根据用户初始化区域计算区域分布的非参数核密度估计。然后，这些分布在计算图边缘权重时用作区域先验。因此，这种方法在形成概率分布时通过在计算距离时尊重其黎曼结构来利用张量数据的真实变化。此外，与先前工作中所做的高斯假设不同，非参数模型可以推广到任意张量分布。将分割问题置于图割框架中，对于所测试的数据，其分割结果在初始化方面具有鲁棒性。
