{"url":"/method/intgauss","slug":"intgauss","name":"intgauss","full_name":"integrated Gaussian kernel","full_name_withheld":false,"description_markdown":"The integrated Gaussian kernel is obtained by integrating the values of the Gaussian kernel over each pixel support region. In this way, some of the severe artefacts of sampling the Gaussian kernel at too fine scales can be reduced.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2311.11317v7","title":"Discrete approximations of Gaussian smoothing and Gaussian derivatives","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Sequential","area_id":"sequential","collection":"Multi-scale analysis","url":"/methods/category/multi-scale-analysis","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/discrete-approximations-of-gaussian-smoothing","title":"Discrete approximations of Gaussian smoothing and Gaussian derivatives","date":"2023-11-19","arxiv_id":"2311.11317","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[{"year":"2023","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/intgauss"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}