Improved Anisotropic Gaussian Filters
This work addresses orientation estimation challenges in fiber analysis for materials science, but it is incremental as it refines an existing method.
The paper tackled the problem of minor inaccuracies in anisotropic Gaussian filters affecting orientation estimation in noisy, low-contrast computed tomography images, proposing a modified algorithm that improves precision and robustness to noise in synthetic fiber bundle images and real-world sheet molding compounds.
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, this paper proposes a modified algorithm for 2D anisotropic Gaussian filters and shows that this improves their precision. Applied to synthetic images of fiber bundles, it is more accurate and robust to noise. Finally, the effectiveness of the approach is shown by applying it to real-world images of sheet molding compounds.