Accelerating Dictionary Indexing of Electron Backscatter Diffraction Patterns with PCA and Quantization

Accelerating Dictionary Indexing of Electron Backscatter Diffraction Patterns with PCA and Quantization | 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 Article Accelerating Dictionary Indexing of Electron Backscatter Diffraction Patterns with PCA and Quantization Zachary T. Varley, Gregory S. Rohrer, Marc Graef This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7428965/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Dictionary indexing (DI) for Electron Backscatter Diffraction (EBSD) has conventionally traded computational speed for superior robustness to pattern noise when compared to traditional Hough indexing methods. To alleviate this computational bottleneck, we propose a novel Principal Component Analysis Dictionary Indexing (PCA-DI) approach that uses dimensionality reduction and numerical precision optimization for accelerated pattern matching. A combination of synthetic benchmarks and benchmarks on a standard Nickel dataset with varying noise levels demonstrate significant speedup factors over conventional DI that scale with image resolution while maintaining or improving indexing accuracy, particularly under high-noise conditions where dimensionality reduction suppresses noise-dominated high-frequency components. Implementation with reduced numerical precision (FP16 and dynamic INT8 quantization) provides additional performance improvements without compromising indexing quality. The resulting approach enables substantially faster dictionary indexing on consumer-grade hardware, addressing scalability challenges for modern high-resolution EBSD systems and low-symmetry materials. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 27 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 21 Aug, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7428965","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":514667019,"identity":"0252f609-a9ab-4b9e-b556-1dfac4b8fd53","order_by":0,"name":"Zachary T. 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To alleviate this computational bottleneck, we propose a novel Principal Component Analysis Dictionary Indexing (PCA-DI) approach that uses dimensionality reduction and numerical precision optimization for accelerated pattern matching. A combination of synthetic benchmarks and benchmarks on a standard Nickel dataset with varying noise levels demonstrate significant speedup factors over conventional DI that scale with image resolution while maintaining or improving indexing accuracy, particularly under high-noise conditions where dimensionality reduction suppresses noise-dominated high-frequency components. Implementation with reduced numerical precision (FP16 and dynamic INT8 quantization) provides additional performance improvements without compromising indexing quality. 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