{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/u-slads-unsupervised-learning-approach-for","title":"U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling","arxiv_id":"1807.02233","date":"2018-07-06","proceeding":null,"authors":["Yan Zhang","Xiang Huang","Nicola Ferrier","Emine B. Gulsoy","Charudatta Phatak"],"abstract":"Novel data acquisition schemes have been an emerging need for scanning\nmicroscopy based imaging techniques to reduce the time in data acquisition and\nto minimize probing radiation in sample exposure. Varies sparse sampling\nschemes have been studied and are ideally suited for such applications where\nthe images can be reconstructed from a sparse set of measurements. Dynamic\nsparse sampling methods, particularly supervised learning based iterative\nsampling algorithms, have shown promising results for sampling pixel locations\non the edges or boundaries during imaging. However, dynamic sampling for\nimaging skeleton-like objects such as metal dendrites remains difficult. Here,\nwe address a new unsupervised learning approach using Hierarchical Gaussian\nMixture Mod- els (HGMM) to dynamically sample metal dendrites. This technique\nis very useful if the users are interested in fast imaging the primary and\nsecondary arms of metal dendrites in solidification process in materials\nscience.","url_abs":"http://arxiv.org/abs/1807.02233v1","url_pdf":"http://arxiv.org/pdf/1807.02233v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"u-slads-unsupervised-learning-approach-for","repo_url":"https://github.com/yatagarasu50469/slads","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}