SAM2 ile Tens r Ayri stirma Tabanli rneksiz Imge B l tleme Kullanarak Manyetik Zenginle stirme Sirasinda Metalik Nikel Cevherine Ait Mikroskobik Verilerin Analizi

The accurate separation and classification of minerals are crucial for integrating deep learning-based computer vision approaches into mineral enrichment processes. In this work, labeled microscopic mineral images were processed using various tensor decomposition algorithms and then segmented with the SAM2 model using a zero-shot prediction. The main goal of this study is to understand how different tensor decomposition schemes influence segmentation performance and to support further research in mineral enrichment. The results shed light on the impact of these algorithms on segmentation accuracy, providing useful guidance for choosing the most effective methods in the related field.