Direct estimation of austenitic grain dimensions in heat affected zones of a martensitic steel from EBSD images.

In the context of automated analyses of electron-backscattered-diffraction images, we present in this paper a novel method to automatically extract morphological properties of prior austenitic grains in martensitic steels based on raw crystallographic orientation maps. This quantification includes the estimation of the mean chord length in specific directions, with in addition the reconstruction of the mean shape of austenitic grains inducing anisotropic shape properties. The approach is based on the morphological measure of covariance on a decision curve of grain fidelity per disorientation angle.

Imaging and 3D morphological analysis of collagen fibrils.

The recent booming of multiphoton imaging of collagen fibrils by means of second harmonic generation microscopy generates the need for the development and automation of quantitative methods for image analysis. Standard approaches sequentially analyse two-dimensional (2D) slices to gain knowledge on the spatial arrangement and dimension of the fibrils, whereas the reconstructed three-dimensional (3D) image yields better information about these characteristics. In this work, a 3D analysis method is proposed for second harmonic generation images of collagen fibrils, based on a recently developed 3D fibre quantification method.

Random-walk-based stochastic modeling of three-dimensional fiber systems.

For the simulation of fiber systems, there exist several stochastic models: systems of straight nonoverlapping fibers, systems of overlapping bending fibers, or fiber systems created by sedimentation. However, there is a lack of models providing dense, nonoverlapping fiber systems with a given random orientation distribution and a controllable level of bending. We introduce a new stochastic model in this paper that generalizes the force-biased packing approach to fibers represented as chains of balls.