Indention mark segmentation data

<p>This dataset includes 1120 electron microscopy images&nbsp;containing an indentation mark&nbsp;and their ground-truth masks to detect the indent marks.&nbsp;Both images and masks are in png format and have a (W=1024, H=768) size. The dataset can be used for semantic segmentation. The images were taken from different scanning electron microscopes (SEMs) and have different degrees of brightness and contrast.&nbsp;</p> <p>Indentation marks are used to evaluate the mechanical properties (hardness)&nbsp;of materials.</p> <p>This<a href="https://github.com/oekosheri/line_detection_around_indent_marks"> Github repository</a>&nbsp;uses the attached data to segment away the indentation&nbsp;mark and detect lines, using <em>OpenCV,</em> on its sides.</p> <p>&nbsp;</p> <p><em>Acknowledgement:&nbsp;</em></p> <p>The authors gratefully acknowledge the German Federal Ministry of Education and Research (BMBF) and the government of Nordrhein-Westfalen and the Hessian ministry for supporting this work/project as part of the NHR funding. This work was supported by the German research foundation (DFG) within the Collaborative Research Centre SFB 1394 &lsquo;&lsquo;Structural and Chemical Atomic Complexity&mdash;From Defect Phase Diagrams to Materials Properties&rdquo; (Project ID 409476157), project A05 and C02.&nbsp;This project has received funding from the European Research Council (ERC) under the European Union&rsquo;s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 852096 FunBlocks).</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>