{"paper":{"title":"CobWeb - a toolbox for automatic tomographic image analysis based on machine learning techniques: application and examples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"2), 5), Bingen, Darmstadt, Frieder Enzmann (2), Germany), Germany (2) Institute for Geosciences, Germany (3) Federal Institute for Geosciences, Germany (4) APS Antriebs-, Germany (5) igem - Institute for Geothermal Ressource Management, G\\\"ottingen-Rosdorf, Hannover, Ingo Sass (1), Johannes Gutenberg-University, Kathleen Sell (2, Mainz, Michael Kersten (2) ((1) Institute of Applied Geosciences, Natural Resources (BGR), Pr\\\"uf- und Steuertechnik GmbH, Swarup Chauhan (1, Thorsten Wille (4), University of Technology, Wolfram R\\\"uhaak (3)","submitted_at":"2018-03-29T13:13:57Z","abstract_excerpt":"In this study, we introduce CobWeb 1.0 which is a graphical user interface tailored explicitly for accurate image segmentation and representative elementary volume analysis of digital rock images derived from high resolution tomography. The CobWeb code is a work package deployed as a series of windows executable binaries which use image processing and machine learning libraries of MATLAB. The user-friendly interface enables image segmentation and cross-validation employing K-means, Fuzzy C-means, least square support vector machine, and ensemble classification (bragging and boosting) segmentat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.11046","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}