{"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/faster-and-better-a-machine-learning-approach","title":"Faster and better: a machine learning approach to corner detection","arxiv_id":"0810.2434","date":"2008-10-14","proceeding":null,"authors":["Edward Rosten","Reid Porter","Tom Drummond"],"abstract":"The repeatability and efficiency of a corner detector determines how likely\nit is to be useful in a real-world application. The repeatability is importand\nbecause the same scene viewed from different positions should yield features\nwhich correspond to the same real-world 3D locations [Schmid et al 2000]. The\nefficiency is important because this determines whether the detector combined\nwith further processing can operate at frame rate.\n  Three advances are described in this paper. First, we present a new heuristic\nfor feature detection, and using machine learning we derive a feature detector\nfrom this which can fully process live PAL video using less than 5% of the\navailable processing time. By comparison, most other detectors cannot even\noperate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize\nthe detector, allowing it to be optimized for repeatability, with little loss\nof efficiency. Third, we carry out a rigorous comparison of corner detectors\nbased on the above repeatability criterion applied to 3D scenes. We show that\ndespite being principally constructed for speed, on these stringent tests, our\nheuristic detector significantly outperforms existing feature detectors.\nFinally, the comparison demonstrates that using machine learning produces\nsignificant improvements in repeatability, yielding a detector that is both\nvery fast and very high quality.","url_abs":"http://arxiv.org/abs/0810.2434v1","url_pdf":"http://arxiv.org/pdf/0810.2434v1.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":"faster-and-better-a-machine-learning-approach","repo_url":"https://github.com/cran/image.CornerDetectionF9","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=0810.2434","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}