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Relevant bibliographies by topics / Clustering 3D

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Academic literature on the topic 'Clustering 3D'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Clustering 3D"

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Manjunath, Mohith, Yi Zhang, Yeonsung Kim, et al. "ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data." PeerJ Computer Science 4 (May 21, 2018): e155. http://dx.doi.org/10.7717/peerj-cs.155.

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Background Clustering is one of the most common techniques in data analysis and seeks to group together data points that are similar in some measure. Although there are many computer programs available for performing clustering, a single web resource that provides several state-of-the-art clustering methods, interactive visualizations and evaluation of clustering results is lacking. Methods ClusterEnG (acronym for Clustering Engine for Genomics) provides a web interface for clustering data and interactive visualizations including 3D views, data selection and zoom features. Eighteen clustering

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Lin, Guoting, Zexun Zheng, Lin Chen, Tianyi Qin, and Jiahui Song. "Multi-Modal 3D Shape Clustering with Dual Contrastive Learning." Applied Sciences 12, no. 15 (2022): 7384. http://dx.doi.org/10.3390/app12157384.

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3D shape clustering is developing into an important research subject with the wide applications of 3D shapes in computer vision and multimedia fields. Since 3D shapes generally take on various modalities, how to comprehensively exploit the multi-modal properties to boost clustering performance has become a key issue for the 3D shape clustering task. Taking into account the advantages of multiple views and point clouds, this paper proposes the first multi-modal 3D shape clustering method, named the dual contrastive learning network (DCL-Net), to discover the clustering partitions of unlabeled 3

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Soliman, Mona M., Aboul Ella Hassanien, and Hoda M. Onsi. "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods." International Journal of Computer Vision and Image Processing 3, no. 2 (2013): 43–53. http://dx.doi.org/10.4018/ijcvip.2013040104.

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Blind and robust watermarking of 3D mesh aims to embed message into a 3D mesh model such that the mesh is not visually distorted from the original model. An essential condition is that the message should be securely extracted even after the mesh model was processed. This paper explores use of artificial intelligence techniques to build blind and robust 3D-watermarking approach. It is based on clustering 3D vertices into appropriate or inappropriate candidates for watermark insertion using K-means clustering and Self Organization Map (SOM) clustering algorithms. The watermark insertion were per

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guojing, FENG Jun, YE Haosheng, and ZHOU Gang. "Retrieval-angle clustering histogram and clustering for 3D model retrieval." Journal of Image and Graphics 15, no. 11 (2010): 1644. http://dx.doi.org/10.11834/jig.20101101.

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Al-Funjan, Amera, Farid Meziane, and Rob Aspin. "Describing Pulmonary Nodules Using 3D Clustering." Advanced Engineering Research 22, no. 3 (2022): 261–71. http://dx.doi.org/10.23947/2687-1653-2022-22-3-261-271.

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Introduction. Determining the tumor (nodule) characteristics in terms of the shape, location, and type is an essential step after nodule detection in medical images for selecting the appropriate clinical intervention by radiologists. Computer-aided detection (CAD) systems efficiently succeeded in the nodule detection by 2D processing of computed tomography (CT)-scan lung images; however, the nodule (tumor) description in more detail is still a big challenge that faces these systems.Materials and Methods. In this paper, the 3D clustering is carried out on volumetric CT-scan images containing th

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Yonggao Yang, J. X. Chen, and Woosung Kim. "Gene expression clustering and 3D visualization." Computing in Science & Engineering 5, no. 5 (2003): 37–43. http://dx.doi.org/10.1109/mcise.2003.1225859.

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Sim, Kelvin, Ghim-Eng Yap, David R. Hardoon, Vivekanand Gopalkrishnan, Gao Cong, and Suryani Lukman. "Centroid-Based Actionable 3D Subspace Clustering." IEEE Transactions on Knowledge and Data Engineering 25, no. 6 (2013): 1213–26. http://dx.doi.org/10.1109/tkde.2012.37.

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Sim, Kelvin, Vivekanand Gopalkrishnan, Clifton Phua, and Gao Cong. "3D Subspace Clustering for Value Investing." IEEE Intelligent Systems 29, no. 2 (2014): 52–59. http://dx.doi.org/10.1109/mis.2012.24.

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Li, Ailin, Anyong Qin, Zhaowei Shang, and Yuan Yan Tang. "Spectral-Spatial Sparse Subspace Clustering Based on Three-Dimensional Edge-Preserving Filtering for Hyperspectral Image." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (2019): 1955003. http://dx.doi.org/10.1142/s0218001419550036.

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Integrating spatial information into the sparse subspace clustering (SSC) models for hyperspectral images (HSIs) is an effective way to improve clustering accuracy. Since HSI is a three-dimensional (3D) cube datum, 3D spectral-spatial filtering becomes a simple method for extracting the spectral-spatial information. In this paper, a novel spectral-spatial SSC framework based on 3D edge-preserving filtering (EPF) is proposed to improve the clustering accuracy of HSI. First, the initial sparse coefficient matrix is obtained in the sparse representation process of the classical SSC model. Then, a

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Li, Wei, Ranran Deng, Yingjie Zhang, Zhaoyun Sun, Xueli Hao, and Ju Huyan. "Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering." Mathematical Problems in Engineering 2019 (November 23, 2019): 1–15. http://dx.doi.org/10.1155/2019/4302805.

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Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is perf

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Dissertations / Theses on the topic "Clustering 3D"

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Petrov, Anton Igorevich. "RNA 3D Motifs: Identification, Clustering, and Analysis." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1333929629.

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Wiberg, Benjamin. "Automatic Clustering of 3D Objects for Hierarchical Level-of-Detail." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150534.

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This report describes an algorithm for computing 3D object hierarchies fit for hlod optimization. The algorithm is used as a pre-processing stage in an hlod pipeline that automatically optimizes 3D models containing multiple meshes. The algorithm for generating hierarchies groups together meshes in a hierarchical tree using operations on bounding spheres of the meshes. The algorithm prioritizes grouping close objects together in the early stages, and relaxes its constraints toward the end, resulting in a tree structure with a single root node. The hierarchical tree is then used by computing pr

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Abu, Almakarem Amal S. "Base Triples in RNA 3D Structures: Identifying, Clustering and Classifying." Bowling Green State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1308783522.

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Borke, Lukas. "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18307.

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Mit der wachsenden Popularität von GitHub, dem größten Online-Anbieter von Programm-Quellcode und der größten Kollaborationsplattform der Welt, hat es sich zu einer Big-Data-Ressource entfaltet, die eine Vielfalt von Open-Source-Repositorien (OSR) anbietet. Gegenwärtig gibt es auf GitHub mehr als eine Million Organisationen, darunter solche wie Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly und viele mehr. GitHub verfügt über eine umfassende REST API, die es Forschern ermöglicht, wertvolle Informationen über die Entwicklungszyklen von Software und Forschung abzurufen. Unsere Arbei

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Hasnat, Md Abul. "Unsupervised 3D image clustering and extension to joint color and depth segmentation." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4013/document.

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L'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la se

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Gianfrotta, Coline. "Modélisation, analyse et classification de motifs structuraux d'ARN à partir de leur contexte, par des méthodes d'algorithmique de graphes." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG056.

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Dans cette thèse, nous étudions le contexte structural de motifs structuraux d'ARN dans le but de progresser vers leur prédiction. En effet, certains motifs d'ARN, sous-structures apparaissant de façon récurrente dans les structures d'ARN, restent difficiles à prédire, en raison de la présence d'interactions non canoniques dans ces motifs, et en raison de la distance sur la séquence primaire séparant les différentes parties de ces motifs. Nous modélisons ainsi par des graphes le contexte structural topologique de ces motifs, et comparons les contextes des différentes occurrences en utilisant p

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Borke, Lukas [Verfasser], Wolfgang Karl [Gutachter] Härdle, and Stefan [Gutachter] Lessmann. "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA / Lukas Borke ; Gutachter: Wolfgang Karl Härdle, Stefan Lessmann." Berlin : Humboldt-Universität zu Berlin, 2017. http://d-nb.info/1189428857/34.

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Yu, En. "Social Network Analysis Applied to Ontology 3D Visualization." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1206497854.

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Nawaf, Mohamad Motasem. "3D structure estimation from image stream in urban environment." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4024/document.

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Dans le domaine de la vision par ordinateur, l’estimation de la structure d’une scène 3D à partir d’images 2D constitue un problème fondamental. Parmi les applications concernées par cette problématique, nous nous sommes intéressés dans le cadre de cette thèse à la modélisation d’un environnement urbain. Nous nous sommes intéressés à la reconstruction de scènes 3D à partir d’images monoculaires générées par un véhicule en mouvement. Ici, plusieurs défis se posent à travers les différentes étapes de la chaine de traitement inhérente à la reconstruction 3D. L’un de ces défis vient du fait de l’a

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Kéchichian, Razmig. "Structural priors for multiobject semi-automatic segmentation of three-dimensional medical images via clustering and graph cut algorithms." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00967381.

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We develop a generic Graph Cut-based semiautomatic multiobject image segmentation method principally for use in routine medical applications ranging from tasks involving few objects in 2D images to fairly complex near whole-body 3D image segmentation. The flexible formulation of the method allows its straightforward adaption to a given application.\linebreak In particular, the graph-based vicinity prior model we propose, defined as shortest-path pairwise constraints on the object adjacency graph, can be easily reformulated to account for the spatial relationships between objects in a given pro

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Book chapters on the topic "Clustering 3D"

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Khattab, Dina, Hala M. Ebeid, Ashraf S. Hussein, and Mohamed F. Tolba. "3D Mesh Segmentation Based on Unsupervised Clustering." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48308-5_57.

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Azri, Suhaibah, Alias Abdul Rahman, Uznir Ujang, François Anton, and Darka Mioc. "3D Crisp Clustering of Geo-Urban Data." In Encyclopedia of GIS. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23519-6_1610-1.

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Niu, Jianwei, Zhizhong Li, and Song Xu. "Block Division for 3D Head Shape Clustering." In Digital Human Modeling. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02809-0_8.

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Azri, Suhaibah, Alias Abdul Rahman, Uznir Ujang, François Anton, and Darka Mioc. "3D Crisp Clustering of Geo-Urban Data." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_1610.

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Khalidov, Vasil, Florence Forbes, Miles Hansard, Elise Arnaud, and Radu Horaud. "Audio-Visual Clustering for 3D Speaker Localization." In Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85853-9_8.

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Castillo, Myrna, Mahtab Dahaghin, Matteo Toso, and Alessio Del Bue. "Contrastive Gaussian Clustering for Weakly Supervised 3D Scene Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78347-0_8.

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Abstract3D scene segmentation is a crucial task in Computer Vision, with applications in autonomous driving, augmented reality, and robotics. Traditional methods often struggle to provide consistent and accurate segmentation across different viewpoints. To address this, we look at the growing field of novel view synthesis. Methods like NeRF and 3DGS take a set of images and implicitly learn a multi-view consistent representation of the geometry of the scene; the same strategy can be extended to learn a 3D segmentation of the scene that is consistent with the 2D segmentation of an initial train

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Choi, Sung-Ja, and Gang-Soo Lee. "3D Viewer Platform of Cloud Clustering Management System: Google Map 3D." In Communication and Networking. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17604-3_25.

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Wang, Fengsui, Zhengnan Liu, Haiying Cheng, et al. "Hierarchical Clustering-Based Video Summarization." In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3863-6_4.

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Frühwirth, Rudolf, and Are Strandlie. "Vertex Finding." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_7.

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AbstractVertex finding is the search for clusters of tracks that originate at the same point in space. The chapter discusses a variety of methods for finding primary vertices, first in one and then in three dimensions. Details are given on model-based clustering, the EM algorithm and clustering by deterministic annealing in 1D, and greedy clustering, iterated estimators, topological vertex finding, and a vertex finder based on medical imaging in 3D.

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Abakumov, Pavel, and Andrey Koucheryavy. "Clustering Algorithm for 3D Wireless Mobile Sensor Network." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23126-6_31.

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Conference papers on the topic "Clustering 3D"

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Neelamraju, Pavan Mohan, and Inbarasan Muniraj. "SLICE: Combined Super Linear Iterative Clustering and Earth Mover’s Distance for Brain Tumour Classification." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/3d.2024.dw1h.3.

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We demonstrate that the combination of Super Linear Iterative Clustering and Earth Mover’s Distance efficiently segments tumours from the MRI dataset. Despite using a smaller training dataset our approach achieves an accuracy of 86.2%.

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Panaintescu, Adrian Valentin, Vlad Constantin Craciun, Radu Marian Lefter, and Luca Mihaela. "Clustering 3D Tracking in Zebrafish Laboratory Trials." In 2024 E-Health and Bioengineering Conference (EHB). IEEE, 2024. https://doi.org/10.1109/ehb64556.2024.10805745.

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Baber, Junaid, and Olivier Aycard. "3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive Clustering and 3D Point Spatial Histograms." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00092.

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Das, Nirmal, Steven Mills, Lech Szymanski, Xinyu Jiang, and Tapabrata Chakraborti. "Investigating Feature Clustering for Generalised 3D Point Cloud Part Segmentation." In 2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2024. https://doi.org/10.1109/ivcnz64857.2024.10794456.

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Ji, Houfeng, Xianhui Liu, Weidong Zhao, Aihua Su, Li Wang, and Hongxin Liu. "An Efficient 3D Model Retrieval Method Based on Adaptive Clustering." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033295.

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Bollenbach, Tobias. "3d Supernovae Collapse Calculations." In EXOTIC CLUSTERING: 4th Catania Relativistic Ion Studies CRIS 2002. AIP, 2002. http://dx.doi.org/10.1063/1.1523196.

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Zhang, Yongrnei, and Bo Li. "Study on a 3D Clustering Algorithm." In Sixth International Conference on Intelligent Systems Design and Applications]. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.258.

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Robert, Damien, Hugo Raguet, and Loic Landrieu. "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering." In 2024 International Conference on 3D Vision (3DV). IEEE, 2024. http://dx.doi.org/10.1109/3dv62453.2024.00135.

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Taubner, Felix, Florian Tschopp, Tonci Novkovic, Roland Siegwart, and Fadri Furrer. "LCD – Line Clustering and Description for Place Recognition." In 2020 International Conference on 3D Vision (3DV). IEEE, 2020. http://dx.doi.org/10.1109/3dv50981.2020.00101.

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Krahn, Maximilian, Florian Bernard, and Vladislav Golyanik. "Convex Joint Graph Matching and Clustering via Semidefinite Relaxations." In 2021 International Conference on 3D Vision (3DV). IEEE, 2021. http://dx.doi.org/10.1109/3dv53792.2021.00129.

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Reports on the topic "Clustering 3D"

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Mohapatra, Sucheta. Dynamic Through-Silicon Via Clustering in 3D IC Floorplanning for Early Performance Optimization. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7437.

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Wiscombe, Warren, Alexander Marshak, Yuri Knyazikhin, and Christine Chiu. Studies of 3D-cloud optical depth from small to very large values, and of the radiation and remote sensing impacts of larger-drop clustering. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/1087121.

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