Thematische Bibliographien / Clustering 3D

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Buchteile
  4. Konferenzberichte
  5. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Clustering 3D“

Autor: Grafiati
Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Clustering 3D"

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Manjunath, Mohith, Yi Zhang, Yeonsung Kim, Steve H. Yeo, Omar Sobh, Nathan Russell, Christian Followell, Colleen Bushell, Umberto Ravaioli und Jun S. Song. „ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data“. PeerJ Computer Science 4 (21.05.2018): 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 validation measures are also presented to aid the user in selecting a suitable algorithm for their dataset. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides tutorials that demonstrate potential pitfalls of each algorithm. Conclusions The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner. The validation measures facilitate the process of choosing a suitable clustering algorithm among the available options. ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.
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Lin, Guoting, Zexun Zheng, Lin Chen, Tianyi Qin und Jiahui Song. „Multi-Modal 3D Shape Clustering with Dual Contrastive Learning“. Applied Sciences 12, Nr. 15 (22.07.2022): 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 3D shapes. First, by simultaneously performing cross-view contrastive learning within multi-view modality and cross-modal contrastive learning between the point cloud and multi-view modalities in the representation space, a representation-level dual contrastive learning module is developed, which aims to capture discriminative 3D shape features for clustering. Meanwhile, an assignment-level dual contrastive learning module is designed by further ensuring the consistency of clustering assignments within the multi-view modality, as well as between the point cloud and multi-view modalities, thus obtaining more compact clustering partitions. Experiments on two commonly used 3D shape benchmarks demonstrate the effectiveness of the proposed DCL-Net.
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Soliman, Mona M., Aboul Ella Hassanien und Hoda M. Onsi. „A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods“. International Journal of Computer Vision and Image Processing 3, Nr. 2 (April 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 performed only on set of selected vertices come out from clustering technique. These vertices are used as candidates for watermark carriers that will hold watermark bits stream. Through the simulations, the authors prove that the proposed approach is robust against various kinds of geometrical attacks such as mesh smoothing, noise addition and mesh cropping.
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Al-Funjan, Amera, Farid Meziane und Rob Aspin. „Describing Pulmonary Nodules Using 3D Clustering“. Advanced Engineering Research 22, Nr. 3 (13.10.2022): 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 the nodule and its structures to describe the nodule progress through the consecutive slices of the lung in CT images.Results. This paper combines algorithms to cluster and define nodule’s features in 3D visualization. Applying some 3D functions to the objects, clustered using the K-means technique of CT lung images, provides a 3D visual exploration of the nodule shape and location. This study mainly focuses on clustering in 3D to discover complex information for a case missed in the radiologist’s report. In addition, the 3D-Density-based spatial clustering of applications with noise (DBSCAN) method and another 3D application (plotly) have been applied to evaluate the proposed system in this work. The proposed method has discovered a complicated case in data and automatically provides information about the nodule types (spherical, juxta-pleural, and pleural-tail). The algorithm is validated on the standard data consisting of the lung computed tomography scans with nodules greater and less than 3mm in size.Discussion and Conclusions. Based on the proposed model, it is possible to cluster lung nodules in volumetric CT scan and determine a set of characteristics such as the shape, location and type.
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Yonggao Yang, J. X. Chen und Woosung Kim. „Gene expression clustering and 3D visualization“. Computing in Science & Engineering 5, Nr. 5 (September 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 und Suryani Lukman. „Centroid-Based Actionable 3D Subspace Clustering“. IEEE Transactions on Knowledge and Data Engineering 25, Nr. 6 (Juni 2013): 1213–26. http://dx.doi.org/10.1109/tkde.2012.37.

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Sim, Kelvin, Vivekanand Gopalkrishnan, Clifton Phua und Gao Cong. „3D Subspace Clustering for Value Investing“. IEEE Intelligent Systems 29, Nr. 2 (März 2014): 52–59. http://dx.doi.org/10.1109/mis.2012.24.

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Peng, Bo, Yuxuan Yao, Qunxia Li, Xinyu Li, Guoting Lin, Lin Chen und Jianjun Lei. „Clustering information-constrained 3D U-Net subspace clustering for hyperspectral image“. Remote Sensing Letters 13, Nr. 11 (10.10.2022): 1131–41. http://dx.doi.org/10.1080/2150704x.2022.2132122.

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Li, Ailin, Anyong Qin, Zhaowei Shang und 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, Nr. 03 (19.02.2019): 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 3D EPF is conducted on the initial sparse coefficient matrix to obtain a more accurate coefficient matrix by solving an optimization problem based on ADMM, which is used to build the similarity graph. Finally, the clustering result of HSI data is achieved by applying the spectral clustering algorithm to the similarity graph. Specifically, the filtered matrix can not only capture the spectral-spatial information but the intensity differences. The experimental results on three real-world HSI datasets demonstrated that the potential of including the proposed 3D EPF into the SSC framework can improve the clustering accuracy.
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Li, Wei, Ranran Deng, Yingjie Zhang, Zhaoyun Sun, Xueli Hao und Ju Huyan. „Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering“. Mathematical Problems in Engineering 2019 (23.11.2019): 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 performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.
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Dissertationen zum Thema "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 proxy meshes, i.e. simplified stand-in meshes, for the inner nodes of the hierarchy. Finally, the resulting proxy meshes, together with the generated hierarchy and the original meshes, are used to render the model using a tree-traversing hlod switching algorithm that renders deeper parts of the tree containing more detailed meshes when more detail is needed. In addition, a minor change to the clustering algorithm is proposed. By swapping the bounding spheres to AABBs (Axis-Aligned Bounding Boxes) in the clustering stage, hierarchies with different properties are generated. This change is shown to generate hierarchies with similar rendering performance as the hierarchies made with bounding spheres, while at the same time resulting in lower space requirements for all proxy meshes. Overall, the proposed automatic hlod pipeline is shown to increase rendering performance for all evaluated scenes in most frames, while never yielding noticeably worse performance than the original model as well.
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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 Arbeit verfolgt zwei Hauptziele: (I) ein automatisches OSR-Kategorisierungssystem für Data Science Teams und Softwareentwickler zu ermöglichen, das Entdeckbarkeit, Technologietransfer und Koexistenz fördert. (II) Visuelle Daten-Exploration und thematisch strukturierte Navigation innerhalb von GitHub-Organisationen für reproduzierbare Kooperationsforschung und Web-Applikationen zu etablieren. Um Mehrwert aus Big Data zu generieren, ist die Speicherung und Verarbeitung der Datensemantik und Metadaten essenziell. Ferner ist die Wahl eines geeigneten Text Mining (TM) Modells von Bedeutung. Die dynamische Kalibrierung der Metadaten-Konfigurationen, TM Modelle (VSM, GVSM, LSA), Clustering-Methoden und Clustering-Qualitätsindizes wird als "Smart Clusterization" abgekürzt. Data-Driven Documents (D3) und Three.js (3D) sind JavaScript-Bibliotheken, um dynamische, interaktive Datenvisualisierung zu erzeugen. Beide Techniken erlauben Visuelles Data Mining (VDM) in Webbrowsern, und werden als D3-3D abgekürzt. Latent Semantic Analysis (LSA) misst semantische Information durch Kontingenzanalyse des Textkorpus. Ihre Eigenschaften und Anwendbarkeit für Big-Data-Analytik werden demonstriert. "Smart clusterization", kombiniert mit den dynamischen VDM-Möglichkeiten von D3-3D, wird unter dem Begriff "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA" zusammengefasst.
With the growing popularity of GitHub, the largest host of source code and collaboration platform in the world, it has evolved to a Big Data resource offering a variety of Open Source repositories (OSR). At present, there are more than one million organizations on GitHub, among them Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly and many more. GitHub provides an extensive REST API, which enables scientists to retrieve valuable information about the software and research development life cycles. Our research pursues two main objectives: (I) provide an automatic OSR categorization system for data science teams and software developers promoting discoverability, technology transfer and coexistence; (II) establish visual data exploration and topic driven navigation of GitHub organizations for collaborative reproducible research and web deployment. To transform Big Data into value, in other words into Smart Data, storing and processing of the data semantics and metadata is essential. Further, the choice of an adequate text mining (TM) model is important. The dynamic calibration of metadata configurations, TM models (VSM, GVSM, LSA), clustering methods and clustering quality indices will be shortened as "smart clusterization". Data-Driven Documents (D3) and Three.js (3D) are JavaScript libraries for producing dynamic, interactive data visualizations, featuring hardware acceleration for rendering complex 2D or 3D computer animations of large data sets. Both techniques enable visual data mining (VDM) in web browsers, and will be abbreviated as D3-3D. Latent Semantic Analysis (LSA) measures semantic information through co-occurrence analysis in the text corpus. Its properties and applicability for Big Data analytics will be demonstrated. "Smart clusterization" combined with the dynamic VDM capabilities of D3-3D will be summarized under the term "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA".
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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 segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parole
Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis
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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 plusieurs algorithmes de graphes. Nous classifions ensuite les occurrences de motif selon leurs similarités de contexte topologique et selon leurs similarités de contexte 3D, à l'aide d'un algorithme de clustering recouvrant.Dans un premier temps, nous montrons sur un jeu de données de trois motifs structuraux que les similarités observées entre les contextes topologiques sont cohérentes avec les similarités entre les contextes 3D. Cela indique que le contexte topologique peut être suffisant pour déterminer le contexte 3D pour ces trois motifs.Dans un deuxième temps, nous étudions plusieurs classifications d'occurrences du motif A-minor, selon des similarités de contexte 3D. Nous y observons que des similarités de contexte 3D existent entre occurrences non homologues, ce qui pourrait être le signe d'un phénomène de convergence évolutive. De plus, nous observons que certaines parties du contexte 3D semblent mieux conservées que d'autres entre occurrences non homologues.Dans un troisième temps, nous étudions la capacité de prédiction du contexte topologique commun à des occurrences de motif A-minor, partageant des contextes 3D similaires, ainsi que la capacité de prédiction d'un signal de séquence sur ces mêmes occurrences. Pour cela, nous étudions la fréquence d'apparition de cette topologie et de ces séquences dans des structures d'ARN en l'absence de motifs A-minor. Nous en concluons que la topologie et la séquence associées représentent un bon signal pour la majorité des classes d'occurrences homologues étudiées
In this thesis, we study the structural context of RNA structural motifs in order to make progress in their prediction. Indeed, some RNA motifs, which are substructures appearing recurrently in RNA structures, remain difficult to predict, because of the presence of non-canonical interactions in these motifs, and because of the distance on the primary sequence between the different parts of these motifs. We therefore model the topological structural context of these motifs by graphs, and compare the contexts of the different occurrences using several graph algorithms. We then classify the motif occurrences according to their topological context similarities and according to their 3D context similarities, using an overlapping clustering algorithm.First, we show on a dataset of three structural motifs that the observed similarities between the topological contexts are consistent with the similarities between the 3D contexts. This indicates that the topological context may be sufficient to determine the 3D context for these three motifs.In a second step, we study several classifications of occurrences of the A-minor motif, according to 3D context similarities. We observe that 3D context similarities exist between non-homologous occurrences, which could be a sign of an evolutionary convergence phenomenon. Moreover, we observe that some parts of the 3D context seem to be better conserved than others between non-homologous occurrences.In a third step, we study the predictive ability of the common topological context of A-minor motif occurrences, sharing similar 3D contexts, as well as the predictive ability of a sequence signal on these same occurrences. To this end, we study the occurrence of this topology and sequence in RNA structures in the absence of A-minor motifs. We conclude that the topology and the sequence represent a good signal for the majority of the studied classes
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Borke, Lukas [Verfasser], Wolfgang Karl [Gutachter] Härdle und 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’absence de zones suffisamment texturées dans certaines scènes urbaines, d’où une reconstruction 3D (un nuage de points 3D) trop éparse. De plus, du fait du mouvement du véhicule, d’une image à l’autre il n’y a pas toujours un recouvrement suffisant entre différentes vues consécutives d’une même scène. Dans ce contexte, et ce afin de lever les verrous ci-dessus mentionnés, nous proposons d’estimer, de reconstruire, la structure d’une scène 3D par morceaux en se basant sur une hypothèse de planéité. Nous proposons plusieurs améliorations à la chaine de traitement associée à la reconstruction 3D. D’abord, afin de structurer, de représenter, la scène sous la forme d’entités planes nous proposons une nouvelle méthode de reconstruction 3D, basée sur le regroupement de pixels similaires (superpixel segmentation), qui à travers une représentation multi-échelle pondérée fusionne les informations de couleur et de mouvement. Cette méthode est basée sur l’estimation de la probabilité de discontinuités locales aux frontières des régions calculées à partir du gradient (gradientbased boundary probability estimation). Afin de prendre en compte l’incertitude liée à l’estimation du mouvement, une pondération par morceaux est appliquée à chaque pixel en fonction de cette incertitude. Cette méthode génère des regroupements de pixels (superpixels) non contraints en termes de taille et de forme. Pour certaines applications, telle que la reconstruction 3D à partir d’une séquence d’images, des contraintes de taille sont nécessaires. Nous avons donc proposé une méthode qui intègre à l’algorithme SLIC (Simple Linear Iterative Clustering) l’information de mouvement. L’objectif étant d’obtenir une reconstruction 3D plus dense qui estime mieux la structure de la scène. Pour atteindre cet objectif, nous avons aussi introduit une nouvelle distance qui, en complément de l’information de mouvement et de données images, prend en compte la densité du nuage de points. Afin d’augmenter la densité du nuage de points utilisé pour reconstruire la structure de la scène sous la forme de surfaces planes, nous proposons une nouvelle approche qui mixte plusieurs méthodes d’appariement et une méthode de flot optique dense. Cette méthode est basée sur un système de pondération qui attribue un poids pré-calculé par apprentissage à chaque point reconstruit. L’objectif est de contrôler l’impact de ce système de pondération, autrement dit la qualité de la reconstruction, en fonction de la précision de la méthode d’appariement utilisée. Pour atteindre cet objectif, nous avons appliqué un processus des moindres carrés pondérés aux données reconstruites pondérées par les calculés par apprentissage, qui en complément de la segmentation par morceaux de la séquence d’images, permet une meilleure reconstruction de la structure de la scène sous la forme de surfaces planes. Nous avons également proposé un processus de gestion des discontinuités locales aux frontières de régions voisines dues à des occlusions (occlusion boundaries) qui favorise la coplanarité et la connectivité des régions connexes. L’ensemble des modèles proposés permet de générer une reconstruction 3D dense représentative à la réalité de la scène. La pertinence des modèles proposés a été étudiée et comparée à l’état de l’art. Plusieurs expérimentations ont été réalisées afin de démontrer, d’étayer, la validité de notre approche
In computer vision, the 3D structure estimation from 2D images remains a fundamental problem. One of the emergent applications is 3D urban modelling and mapping. Here, we are interested in street-level monocular 3D reconstruction from mobile vehicle. In this particular case, several challenges arise at different stages of the 3D reconstruction pipeline. Mainly, lacking textured areas in urban scenes produces low density reconstructed point cloud. Also, the continuous motion of the vehicle prevents having redundant views of the scene with short feature points lifetime. In this context, we adopt the piecewise planar 3D reconstruction where the planarity assumption overcomes the aforementioned challenges.In this thesis, we introduce several improvements to the 3D structure estimation pipeline. In particular, the planar piecewise scene representation and modelling. First, we propose a novel approach that aims at creating 3D geometry respecting superpixel segmentation, which is a gradient-based boundary probability estimation by fusing colour and flow information using weighted multi-layered model. A pixel-wise weighting is used in the fusion process which takes into account the uncertainty of the computed flow. This method produces non-constrained superpixels in terms of size and shape. For the applications that imply a constrained size superpixels, such as 3D reconstruction from an image sequence, we develop a flow based SLIC method to produce superpixels that are adapted to reconstructed points density for better planar structure fitting. This is achieved by the mean of new distance measure that takes into account an input density map, in addition to the flow and spatial information. To increase the density of the reconstructed point cloud used to performthe planar structure fitting, we propose a new approach that uses several matching methods and dense optical flow. A weighting scheme assigns a learned weight to each reconstructed point to control its impact to fitting the structure relative to the accuracy of the used matching method. Then, a weighted total least square model uses the reconstructed points and learned weights to fit a planar structure with the help of superpixel segmentation of the input image sequence. Moreover, themodel handles the occlusion boundaries between neighbouring scene patches to encourage connectivity and co-planarity to produce more realistic models. The final output is a complete dense visually appealing 3Dmodels. The validity of the proposed approaches has been substantiated by comprehensive experiments and comparisons with state-of-the-art methods
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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 problem instance. The segmentation algorithm can be tailored to the runtime requirements of the application and the online storage capacities of the computing platform by an efficient and controllable Voronoi tessellation clustering of the input image which achieves a good balance between cluster compactness and boundary adherence criteria. Qualitative and quantitative comprehensive evaluation and comparison with the standard Potts model confirm that the vicinity prior model brings significant improvements in the correct segmentation of distinct objects of identical intensity, the accurate placement of object boundaries and the robustness of segmentation with respect to clustering resolution. Comparative evaluation of the clustering method with competing ones confirms its benefits in terms of runtime and quality of produced partitions. Importantly, compared to voxel segmentation, the clustering step improves both overall runtime and memory footprint of the segmentation process up to an order of magnitude virtually without compromising the segmentation quality.
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Buchteile zum Thema "Clustering 3D"

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Khattab, Dina, Hala M. Ebeid, Ashraf S. Hussein und Mohamed F. Tolba. „3D Mesh Segmentation Based on Unsupervised Clustering“. In Advances in Intelligent Systems and Computing, 598–607. Cham: 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 und Darka Mioc. „3D Crisp Clustering of Geo-Urban Data“. In Encyclopedia of GIS, 1–9. Cham: 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 und Song Xu. „Block Division for 3D Head Shape Clustering“. In Digital Human Modeling, 64–71. Berlin, Heidelberg: 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 und Darka Mioc. „3D Crisp Clustering of Geo-Urban Data“. In Encyclopedia of GIS, 1–9. Cham: 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 und Radu Horaud. „Audio-Visual Clustering for 3D Speaker Localization“. In Machine Learning for Multimodal Interaction, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85853-9_8.

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Choi, Sung-Ja, und Gang-Soo Lee. „3D Viewer Platform of Cloud Clustering Management System: Google Map 3D“. In Communication and Networking, 218–22. Berlin, Heidelberg: 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, Linjun Fu, Jingang Chen, Qisheng Wang, Furong Liu und Chao Han. „Hierarchical Clustering-Based Video Summarization“. In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology, 27–34. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3863-6_4.

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Frühwirth, Rudolf, und Are Strandlie. „Vertex Finding“. In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 131–41. Cham: 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, und Andrey Koucheryavy. „Clustering Algorithm for 3D Wireless Mobile Sensor Network“. In Lecture Notes in Computer Science, 343–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23126-6_31.

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Suter, Susanne K., Bo Ma und Alireza Entezari. „Visual Analysis of 3D Data by Isovalue Clustering“. In Advances in Visual Computing, 313–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_30.

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Konferenzberichte zum Thema "Clustering 3D"

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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, und 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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Taubner, Felix, Florian Tschopp, Tonci Novkovic, Roland Siegwart und 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 und 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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En-Ya, Shen, Wang Wen-Ke, Li Si-Kun und Cai Xun. „Interactive Continuous Erasing and Clustering in 3D“. In 2012 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2012. http://dx.doi.org/10.1109/icvrv.2012.21.

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Li, Tianjian, Yan Han, Xiaoyao Liang, Hsien-Hsin S. Lee und Li Jiang. „Fault clustering technique for 3D memory BISR“. In 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2017. http://dx.doi.org/10.23919/date.2017.7927050.

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Hinojosa, Carlos A., Jorge Bacca und Henry Arguello. „Spectral Imaging Subspace Clustering with 3-D Spatial Regularizer“. In 3D Image Acquisition and Display: Technology, Perception and Applications. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/3d.2018.jw5e.7.

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Wagner, Patrick, Jakob Paul Morath, Arturo Zychlinsky, Klaus-Robert Muller und Wojciech Samek. „Rotation Invariant Clustering of 3D Cell Nuclei Shapes *“. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856734.

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Atchaya, V., und R. Vanitha. „An Optimal centroid based actionable 3D subspace clustering“. In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7033862.

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Magnusson, Martin, Tomasz Piotr Kucner, Saeed Gholami Shahbandi, Henrik Andreasson und Achim J. Lilienthal. „Semi-supervised 3D place categorisation by descriptor clustering“. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8202216.

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Berichte der Organisationen zum Thema "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, Januar 2000. http://dx.doi.org/10.15760/etd.7437.

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