作者：Alex Keilmann Michael Godehardt Ali Moghiseh Claudia Redenbach Katja Schladitz
细长各向异性高斯滤波器用于纤维的取向估计。在计算机断层扫描图像具有噪声、粗略分辨率和低对比度的情况下，即使仅在虚拟2D切片中有效，它们也是首选方法。然而，各向异性高斯滤波器中的微小不准确性可以延续到方位估计。因此，我们提出了一种二维各向异性高斯滤波器的改进算法，并表明这提高了它们的精度。应用于纤维束的合成图像，它对噪声更准确、更鲁棒。最后，我们通过将我们的方法应用于片材模塑化合物的真实世界图像来证明我们的方法的有效性。
Elongated anisotropic Gaussian filters are used for the orientation estimation of fibers. In cases where computed tomography images are noisy, roughly resolved, and of low contrast, they are the method of choice even if being efficient only in virtual 2D slices. However, minor inaccuracies in the anisotropic Gaussian filters can carry over to the orientation estimation. Therefore, we propose a modified algorithm for 2D anisotropic Gaussian filters and show that this improves their precision. Applied to synthetic images of fiber bundles, it is more accurate and robust to noise. Finally, we demonstrate the effectiveness of our approach by applying it to real-world images of sheet molding compounds.
论文链接：http://arxiv.org/pdf/2303.13278v1
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