Center-environment feature models for materials image segmentation based on machine learning.

ABSTRACT: Materials properties depend not only on their compositions but also their microstructures under various processing conditions. So far, the analyses of complex microstructure images rely mostly on human experience, lack of automatic quantitative characterization methods. Machine learning provides an emerging vital tool to identify various complex materials phases in an intelligent manner. In this work, we propose a "center-environment segmentation" (CES) feature model for image segmentation based on machine learning method with environment features and the annotation input of domain knowledge. The CES model introduces the information of neighbourhood as the features of a given pixel, reflecting the relationships between the studied pixel and its surrounding environment. Then, an iterative i

SUBMITTER: Han Y 

PROVIDER: S-EPMC9334618 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature