{"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/segmentation-of-scanning-tunneling-microscopy","title":"Segmentation of Scanning Tunneling Microscopy Images Using Variational Methods and Empirical Wavelets","arxiv_id":"1804.08890","date":"2018-04-24","proceeding":null,"authors":["Bui Kevin","Fauman Jacob","Kes David","Torres Mandiola Leticia","Ciomaga Adina","Salazar Ricardo","Bertozzi L. Andrea","Gilles Jerome","Guttentag I. Andrew","Weiss S. Paul"],"abstract":"In the fields of nanoscience and nanotechnology, it is important to be able\nto functionalize surfaces chemically for a wide variety of applications.\nScanning tunneling microscopes (STMs) are important instruments in this area\nused to measure the surface structure and chemistry with better than molecular\nresolution. Self-assembly is frequently used to create monolayers that redefine\nthe surface chemistry in just a single-molecule-thick layer. Indeed, STM images\nreveal rich information about the structure of self-assembled monolayers since\nthey convey chemical and physical properties of the studied material.\n  In order to assist in and to enhance the analysis of STM and other images, we\npropose and demonstrate an image-processing framework that produces two image\nsegmentations: one is based on intensities (apparent heights in STM images) and\nthe other is based on textural patterns. The proposed framework begins with a\ncartoon+texture decomposition, which separates an image into its cartoon and\ntexture components. Afterward, the cartoon image is segmented by a modified\nmultiphase version of the local Chan-Vese model, while the texture image is\nsegmented by a combination of 2D empirical wavelet transform and a clustering\nalgorithm. Overall, our proposed framework contains several new features,\nspecifically in presenting a new application of cartoon+texture decomposition\nand of the empirical wavelet transforms and in developing a specialized\nframework to segment STM images and other data. To demonstrate the potential of\nour approach, we apply it to actual STM images of cyanide monolayers on\nAu\\{111\\} and present their corresponding segmentation results.","url_abs":"http://arxiv.org/abs/1804.08890v1","url_pdf":"http://arxiv.org/pdf/1804.08890v1.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":"segmentation-of-scanning-tunneling-microscopy","repo_url":"https://github.com/kbui1993/Microscopy-Codes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}