Foreground and Background Separation Using Primary Components Variance Classification

Foreground and Background Separation Using Primary Components Variance Classification | 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 Foreground and Background Separation Using Primary Components Variance Classification Sotirios N. Raptis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7833378/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 The paper copes with a mainstream problem of using PCA~to separate foreground from background on a still image. The approach here is inspired by the use of Primary Component Analysis on video analysis applications where the concept is to track the foreground as the most varying part of the image while the background is the relatively constant part. Matrix factorization is the main methodology used to split the background~from the foreground and this paper chooses the Primary Components using variance splitting then mapping to the suitable Primary Components. This avoids the use of optimization to compute the lower rank matrices uses Primary Components as well and yields image parts of varying variance is here approached as a classification problem of the projections onto the Primary Components into into number of classes that divide the total image variance into intervals finally setting the image blocks with the most varying Primary Components as foreground blocks and the rest as background blocks. The tested image is a Cryo-EM image of insect flight muscle's actin-villin filaments that it is trivial to process or to capture these filaments as the foreground while keeping the cytoplasm and the noise as the background. The parameter to tune for best results is the number of Primary Component classes that represent variance classes for the cytoplasm and the size of image building blocks but no explicit knowledge of the filament structure or of the cytoplasm is used employing, thus, only class statistical separability criteria. The results show that the filaments as foreground rather span more variance classes and the cytoplam as background can be more easily approached (fewer classes). On tests the results for consistency using (1) the Kullback-Leibler and Kolmogorov-Smirnov tests between same class blocks, and (2) the comparison of the binary maps of background/foreground pixels for all classification parameters. The method can also be used to segment video or image flows. Applied Statistics Biomedical Engineering Background foreground statistical tests Hierarchical Clustering K-Means Hierarchical Clustering Full Text Additional Declarations The authors declare no competing interests. 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. 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-7833378","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528019339,"identity":"c0f348aa-3d83-4eff-b23c-9875fba29b5e","order_by":0,"name":"Sotirios N. Raptis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie2QsUrEQBBA5ziwimx3jCjuL2w4UIv7mB1s9TiwuSqszaWStBE/weYgkDohhU2CbcQmYKvidSlSuFmiVRItBfexOzvFPGZmASyWP0oCosukvswkqzFh+q1MVKscqFYRI4ZWvpi0xSASE4cF7gdptl55cOoXD1XVwHL+eE8flQDOZkmvIvIM0lxkcJQvSdEGrk7Ktwj1YO7tnexX8BxSpYdBuHAVKaC4LLatIsVzv8JDo3iA7NVVsgGKwiKqxxQojTIFRN1F7gFt2U082kXvIrSSOYjvFNIGKSz34zMpcHAX7l+/7FTjHSO7THZ1s6AgKKKner3g7HBgsA7HHNC/ACi75Gec7mXJb6otFovlH/EJs95g6CTRXCUAAAAASUVORK5CYII=","orcid":"","institution":"(Alumni) National Technical University of Athens(NTUA)","correspondingAuthor":true,"prefix":"","firstName":"Sotirios","middleName":"N.","lastName":"Raptis","suffix":""}],"badges":[],"createdAt":"2025-10-11 08:24:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7833378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7833378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93487739,"identity":"0d09c0f1-9323-485e-8d67-8948d6f74840","added_by":"auto","created_at":"2025-10-14 11:36:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3028972,"visible":true,"origin":"","legend":"","description":"","filename":"sbm.8065553407d24b219e402f88cc42a366.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7833378/v1_covered_75b8075e-b0f0-471c-88fe-ae69dcb753cd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForeground and Background Separation Using Primary Components Variance Classification\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"N/A","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Background, foreground, statistical tests, Hierarchical Clustering, K-Means, Hierarchical Clustering","lastPublishedDoi":"10.21203/rs.3.rs-7833378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7833378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe paper copes with a mainstream problem of using PCA~to separate foreground from background on a still image. The approach here is inspired by the use of Primary Component Analysis on video analysis applications where the concept is to track the foreground as the most varying part of the image while the background is the relatively constant part. Matrix factorization is the main methodology used to split the background~from the foreground and this paper chooses the Primary Components using variance splitting then mapping to the suitable Primary Components. This avoids the use of optimization to compute the lower rank matrices uses Primary Components as well and yields image parts of varying variance is here approached as a classification problem of the projections onto the Primary Components into into number of classes that divide the total image variance into intervals finally setting the image blocks with the most varying Primary Components as foreground blocks and the rest as background blocks. The tested image is a Cryo-EM image of insect flight muscle's actin-villin filaments that it is trivial to process or to capture these filaments as the foreground while keeping the cytoplasm and the noise as the background. The parameter to tune for best results is the number of Primary Component classes that represent variance classes for the cytoplasm and the size of image building blocks but no explicit knowledge of the filament structure or of the cytoplasm is used employing, thus, only class statistical separability criteria. The results show that the filaments as foreground rather span more variance classes and the cytoplam as background can be more easily approached (fewer classes). On tests the results for consistency using (1) the Kullback-Leibler and Kolmogorov-Smirnov tests between same class blocks, and (2) the comparison of the binary maps of background/foreground pixels for all classification parameters. The method can also be used to segment video or image flows.\u003c/p\u003e","manuscriptTitle":"Foreground and Background Separation Using Primary Components Variance Classification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 11:27:57","doi":"10.21203/rs.3.rs-7833378/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f768e18f-fb25-4852-9ef7-c1b2fecd87a2","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56186481,"name":"Applied Statistics"},{"id":56186482,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2025-10-14T11:27:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 11:27:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7833378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7833378","identity":"rs-7833378","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}