Accelerated Border Tracking in Binary Images with GPUs

Accelerated Border Tracking in Binary Images with GPUs | 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 Accelerated Border Tracking in Binary Images with GPUs Pedro Alonso-Jordá, Roberto Díaz-Cano, Enrique S. Quintana-Ortí, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7694436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This work presents an optimized algorithm for contour detection and extraction (i.e., border tracking) in binary images, aiming to improve performance in computer vision scenarios that require real-time processing. The proposed method is based on a parallel adaptation of the Suzuki algorithm, widely used in libraries such as OpenCV, but contributes a variant with the advantage of running efficiently on the GPU. The approach divides the image into rectangular blocks, processing each block in parallel to extract “triads” (structures representing three interconnected and ordered points). Subsequently, the triads are connected both within each block and between adjacent blocks to form complete, closed contours. The algorithm is composed of three stages, each implemented as CUDA kernels. The main goal of the proposed algorithm is to avoid costly data transfers between the CPU and GPU, which is especially beneficial when the algorithm is part of industrial workflows with high efficiency requirements. border tracking binary images GPU CUDA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Dec, 2025 Reviews received at journal 14 Nov, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 26 Sep, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 23 Sep, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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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-7694436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527254986,"identity":"43ab8482-8259-4790-8456-fe3a78ffe77f","order_by":0,"name":"Pedro Alonso-Jordá","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFAC5gYQKUeKFkawFmPStSQ2EK3BnP1g4+OCPzbpG273GDB8+EOEFsuexGbjmW1puRvunDFgnNlGhBaDA4lt0rwNh3M33EhLYOYlxnkG5x+2SfP8OZxuANLyhxiHGdwA2sLDdjjB4EbyAWYGNiK0WM542GzM25ZmOPPO4QMHe4nxizl/8sHHPH9s5PluNzY++EGUw+AsCQaGA0RoQNMyCkbBKBgFowArAADzVDlsaR/pUwAAAABJRU5ErkJggg==","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Alonso-Jordá","suffix":""},{"id":527254987,"identity":"4168fc5f-2eca-45d5-9a0a-833d6112ada4","order_by":1,"name":"Roberto Díaz-Cano","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Díaz-Cano","suffix":""},{"id":527254988,"identity":"8cb6d1d4-e1e0-44ad-8163-9083bdccea94","order_by":2,"name":"Enrique S. 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