A Order based Content-Based 3D Model Retrieval proposal

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This study proposes a 3D content-based retrieval system using an Extended Lexicographical Order similarity function, which outperformed traditional distance and order functions with spectral and harmonic spherical descriptors.

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This preprint studies a prototype 3D content-based retrieval (CBR) system for comparing geometrical mesh models. Instead of using distance functions between feature vectors, it proposes an order-based similarity approach using an Extended Lexicographical Order (ELO) in R^n, and evaluates it against standard distance-based methods and other order relations such as lexicographical and revlex, using spectral (compressed sensing-based) and harmonic spherical (SPHARM-like) descriptors. The authors report that the prototype performed better than traditional techniques across the tested setups, though a stated caveat is that the work is a preprint and not peer reviewed. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

In computer graphics, Content-Based Retrieval (CBR) is a database system that consists of receiving an object as input and returning a list of similar objects. Although originally conceived for images, CBR systems can work with objects of another nature such as sounds, and three-dimensional models , represented as geometrical meshes, transporting CBR systems to contexts other than images To assess the proximity between the meshes, some techniques consist of performing mathematical transformations of the meshes into feature vectors and calculating the distance relationship between these vectors. In this work, we built a prototype of a 3D CBR system that uses an order relation, instead of a distance function, as a similarity function between the objects. Furthermore , we propose the use of an order in R n that we call Extended Lexicographical Order (ELO), which seeks to take into account all the information present in the vectors to be compared. We compared our prototype not only with the traditional distance functions but also with more classical R n order relations such as lexicographical and revlex. We also use two types of descrip-tors, a spectral descriptor based on compressed sensing theory and a harmonic spherical-based descriptor. In all cases, our prototype performed better when compared to traditional techniques.
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A Order based Content-Based 3D Model Retrieval proposal | 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 A Order based Content-Based 3D Model Retrieval proposal Thiago Kobashigawa Amorim, Helton Hideraldo Biscaro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1975005/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 In computer graphics, Content-Based Retrieval (CBR) is a database system that consists of receiving an object as input and returning a list of similar objects. Although originally conceived for images, CBR systems can work with objects of another nature such as sounds, and three-dimensional models , represented as geometrical meshes, transporting CBR systems to contexts other than images To assess the proximity between the meshes, some techniques consist of performing mathematical transformations of the meshes into feature vectors and calculating the distance relationship between these vectors. In this work, we built a prototype of a 3D CBR system that uses an order relation, instead of a distance function, as a similarity function between the objects. Furthermore , we propose the use of an order in R n that we call Extended Lexicographical Order (ELO), which seeks to take into account all the information present in the vectors to be compared. We compared our prototype not only with the traditional distance functions but also with more classical R n order relations such as lexicographical and revlex. We also use two types of descrip-tors, a spectral descriptor based on compressed sensing theory and a harmonic spherical-based descriptor. In all cases, our prototype performed better when compared to traditional techniques. Content-Based Retrieval (CBR) Spectral Descriptor SPHARM Order Full Text Additional Declarations No competing interests reported. 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. 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