Markov Random Field based microstructure reconstruction using the principal image moments
Markov Random Field based microstructure reconstruction using the principal image moments
复制标题
使用主图像矩进行基于马尔可夫随机场的微观结构重建
DOI：
10.1016/j.matchar.2021.111281
复制
发表时间：
2021
影响因子：
4.7
通讯作者：
M. Graef
中科院分区：
文献类型：
--
作者：
Arulmurugan Senthilnathan;P. Acar;M. Graef
目前的工作使用马尔可夫随机场 (MRF) 方法和像矩原理来解决锻造和增材制造材料的微观结构重建。对实验样品进行基于 MRF 的重建，以预测更大规模的微结构的空间演化。为了实现原始图像的高保真统计等效表示，根据合成样本的全局和局部特征对其进行评估。全局参数测量微观结构图像的平均特性。特别地，它们被定义为到图像质心的距离和归一化中心矩的协方差矩阵的特征值。局部特征与颗粒级属性相关，包括使用图像矩值计算的颗粒尺寸和形状。通过我们提出的方法，我们不仅展示了微结构空间重建的计算框架，而且保证了合成样本与原始微结构图像的统计等效性。
The present work addresses the microstructure reconstruction of forged and additively manufactured materials using Markov Random Field (MRF) approach and the principal of image moments. The MRF based reconstruction is performed for the experimental samples to predict the spatial evolution of the microstructures on a larger scale. To achieve a high-fidelity statistically-equivalent representation for the original image, the synthesized samples are assessed according to their global and local level features. The global parameters measure the averaged properties of the microstructure image. In particular, they are defined as the distance to the image centroid and the eigenvalues of the covariance matrix of the normalized central moments. The local features are associated with the grain-level properties, including grain size and shape which are computed using the image moment values. With our presented approach, we not only demonstrate a computational framework for the spatial reconstruction of microstructures but also guarantee the statistical equivalency of the synthesized samples to the original microstructure images.