MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model

arXiv:2401.05638v2 Announce Type: replace
Abstract: Efficient and accurate extraction of microstructures in micrographs of materials is essential in process optimization and the exploration of structure-property relationships. Deep learning-based image segmentation techniques that rely on manual annotation are laborious and time-consuming and hardly meet the demand for model transferability and generalization on various source images. Segment Anything Model (SAM), a large visual model with powerful deep feature representation and zero-shot generalization capabilities, has provided new solutions for image segmentation. In this …

abstract annotation arxiv cs.cv deep learning demand exploration extraction image materials optimization process process optimization property relationships segmentation type via visual