Material phase classification by means of Support Vector Machines
Abstract
The pixel's classification of images obtained from random heterogeneous materials is a relevant step to compute their physical properties, like Effective Transport Coefficients (ETC), during a characterization process as stochastic reconstruction. A bad classification will impact on the computed properties; however, the literature on the topic discusses mainly the correlation functions or the properties formulae, giving little or no attention to the classification; authors mention either the use of a threshold or, in few cases, the use of Otsu's method. This paper presents a classification approach based on Support Vector Machines (SVM) and a comparison with the Otsu's-based approach, based on accuracy and precision. The data used for the SVM training are the key for a better classification; these data are the grayscale value, the magnitude and direction of pixels gradient. For instance, in the case study, the accuracy of the pixel's classification is 77.6% for the SVM method and 40.9% for Otsu's method. Finally, a discussion about the impact on the correlation functions is presented in order to show the benefits of the proposal.
- Publication:
-
arXiv e-prints
- Pub Date:
- December 2017
- DOI:
- 10.48550/arXiv.1712.04550
- arXiv:
- arXiv:1712.04550
- Bibcode:
- 2017arXiv171204550O
- Keywords:
-
- Physics - Computational Physics;
- Condensed Matter - Materials Science