Investigating local orientation methods to segment microstructure with 3D solid texture

Abstract

This study investigates local orientation-based approaches to the complex problem of pattern segmentation in threedimensional (3D) texture image. The current problem focuses on the extraction of so-called lamellar colonies in titanium alloy, which, from the materials science and engineering point of view, are microstructural features that play a fundamental role on crack propagation and bifurcation during mechanical loading. Methods based on local orientation estimation extend the notion of using local gradient to reveal variation of semi-planar pattern orientation in the 3D image. The study introduces a computational approach that accelerates the calculation of the eigenvectors from the local matrices of inertia of all voxels composing the 3D image. Then different paths are proposed to segment colonies or inter-colony boundaries, i.e. polar orientation map and minimum scalar product map, in order to delimitate regions of similar orientations. The investigated segmentation methods have been compared with other methods that are mainly based on the popular solution of filter banks. Tests, which have been performed on both synthetic and real 3D images, show that the proposed local orientation-based methods better delineate object boundaries than the counterparts.

Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process

“…Interesting feature is that the reconstructed images based analysis outperform both compared methods for low flow rates while is giving worse results for more dynamic flow regimes while the other two gain more accuracy for higher flow rates. It will be interesting to verify performance of the proposed model for a truly large data sample, especially of mixed origins of different experimental installations [23] [24]. On the other hand, it is noticeable that CDPM beats direct estimation based on raw data records by a small difference yet with much lower variance as well.…”