Ellipse-Fit Alignment for Robust 2D Shape Recognition

Ellipse-Fit Alignment for Robust 2D Shape Recognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ellipse-Fit Alignment for Robust 2D Shape Recognition Mehmet Fidan, Semih Ergin, Mehmet Koç, Mehmet Bilginer Gülmezoğlu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5716650/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study proposes a novel approach for robust 2D shape recognition through ellipse-fit alignment. The method aligns shapes using an ellipse fitting algorithm, addressing issues of rotation, translation, and scaling commonly observed in shape databases. Following alignment, shapes are classified using various similarity measures. Experimental results on the Kimia and TARI databases demonstrate the effectiveness of the proposed method. In the Kimia database, metrics such as Threat Score, Accuracy, F1 Score, Matthews Correlation Coefficient, Fowlkes-Mallows index, and Vote achieved perfect recognition rates. The code and databases can be accessed via https://doi.org/10.5281/zenodo.14423922 . While the TARI database yielded slightly lower recognition rates, with Accuracy and Markedness contributing to an 85.5% recognition rate, the results highlight the potential and limitations of the approach. The study introduces a significant advancement in 2D shape recognition by combining ellipse fitting, affine transformation, and similarity measurement, offering a robust solution for applications in computer vision and image retrieval. Object detection 2D shape recognition Ellipse fitting Affine transformation Rotation Translation Scaling Similarity measure Full Text Additional Declarations No competing interests reported. Supplementary Files README.md CONTRIBUTING.md EllipseFitAllignment.m TARI.zip Kimia99DB.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5716650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395363639,"identity":"d102373d-5bf2-4ac8-8d7a-8430860dbe9e","order_by":0,"name":"Mehmet 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The method aligns shapes using an ellipse fitting algorithm, addressing issues of rotation, translation, and scaling commonly observed in shape databases. Following alignment, shapes are classified using various similarity measures. Experimental results on the Kimia and TARI databases demonstrate the effectiveness of the proposed method. In the Kimia database, metrics such as Threat Score, Accuracy, F1 Score, Matthews Correlation Coefficient, Fowlkes-Mallows index, and Vote achieved perfect recognition rates. The code and databases can be accessed via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14423922\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14423922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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