Literatura científica selecionada sobre o tema "Clustering 3D"
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Artigos de revistas sobre o assunto "Clustering 3D"
Manjunath, Mohith, Yi Zhang, Yeonsung Kim, Steve H. Yeo, Omar Sobh, Nathan Russell, Christian Followell, Colleen Bushell, Umberto Ravaioli e Jun S. Song. "ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data". PeerJ Computer Science 4 (21 de maio de 2018): e155. http://dx.doi.org/10.7717/peerj-cs.155.
Texto completo da fonteLin, Guoting, Zexun Zheng, Lin Chen, Tianyi Qin e Jiahui Song. "Multi-Modal 3D Shape Clustering with Dual Contrastive Learning". Applied Sciences 12, n.º 15 (22 de julho de 2022): 7384. http://dx.doi.org/10.3390/app12157384.
Texto completo da fonteSoliman, Mona M., Aboul Ella Hassanien e Hoda M. Onsi. "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods". International Journal of Computer Vision and Image Processing 3, n.º 2 (abril de 2013): 43–53. http://dx.doi.org/10.4018/ijcvip.2013040104.
Texto completo da fonteAl-Funjan, Amera, Farid Meziane e Rob Aspin. "Describing Pulmonary Nodules Using 3D Clustering". Advanced Engineering Research 22, n.º 3 (13 de outubro de 2022): 261–71. http://dx.doi.org/10.23947/2687-1653-2022-22-3-261-271.
Texto completo da fonteYonggao Yang, J. X. Chen e Woosung Kim. "Gene expression clustering and 3D visualization". Computing in Science & Engineering 5, n.º 5 (setembro de 2003): 37–43. http://dx.doi.org/10.1109/mcise.2003.1225859.
Texto completo da fonteSim, Kelvin, Ghim-Eng Yap, David R. Hardoon, Vivekanand Gopalkrishnan, Gao Cong e Suryani Lukman. "Centroid-Based Actionable 3D Subspace Clustering". IEEE Transactions on Knowledge and Data Engineering 25, n.º 6 (junho de 2013): 1213–26. http://dx.doi.org/10.1109/tkde.2012.37.
Texto completo da fonteSim, Kelvin, Vivekanand Gopalkrishnan, Clifton Phua e Gao Cong. "3D Subspace Clustering for Value Investing". IEEE Intelligent Systems 29, n.º 2 (março de 2014): 52–59. http://dx.doi.org/10.1109/mis.2012.24.
Texto completo da fontePeng, Bo, Yuxuan Yao, Qunxia Li, Xinyu Li, Guoting Lin, Lin Chen e Jianjun Lei. "Clustering information-constrained 3D U-Net subspace clustering for hyperspectral image". Remote Sensing Letters 13, n.º 11 (10 de outubro de 2022): 1131–41. http://dx.doi.org/10.1080/2150704x.2022.2132122.
Texto completo da fonteLi, Ailin, Anyong Qin, Zhaowei Shang e 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, n.º 03 (19 de fevereiro de 2019): 1955003. http://dx.doi.org/10.1142/s0218001419550036.
Texto completo da fonteLi, Wei, Ranran Deng, Yingjie Zhang, Zhaoyun Sun, Xueli Hao e Ju Huyan. "Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering". Mathematical Problems in Engineering 2019 (23 de novembro de 2019): 1–15. http://dx.doi.org/10.1155/2019/4302805.
Texto completo da fonteTeses / dissertações sobre o assunto "Clustering 3D"
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.
Texto completo da fonteWiberg, 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.
Texto completo da fonteAbu, 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.
Texto completo da fonteBorke, 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.
Texto completo da fonteWith 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".
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.
Texto completo da fonteAccess 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
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.
Texto completo da fonteIn 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
Borke, Lukas [Verfasser], Wolfgang Karl [Gutachter] Härdle e 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.
Texto completo da fonteYu, En. "Social Network Analysis Applied to Ontology 3D Visualization". Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1206497854.
Texto completo da fonteNawaf, Mohamad Motasem. "3D structure estimation from image stream in urban environment". Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4024/document.
Texto completo da fonteIn 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
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.
Texto completo da fonteCapítulos de livros sobre o assunto "Clustering 3D"
Khattab, Dina, Hala M. Ebeid, Ashraf S. Hussein e 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.
Texto completo da fonteAzri, Suhaibah, Alias Abdul Rahman, Uznir Ujang, François Anton e 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.
Texto completo da fonteNiu, Jianwei, Zhizhong Li e 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.
Texto completo da fonteAzri, Suhaibah, Alias Abdul Rahman, Uznir Ujang, François Anton e 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.
Texto completo da fonteKhalidov, Vasil, Florence Forbes, Miles Hansard, Elise Arnaud e 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.
Texto completo da fonteChoi, Sung-Ja, e 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.
Texto completo da fonteWang, Fengsui, Zhengnan Liu, Haiying Cheng, Linjun Fu, Jingang Chen, Qisheng Wang, Furong Liu e 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.
Texto completo da fonteFrühwirth, Rudolf, e 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.
Texto completo da fonteAbakumov, Pavel, e 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.
Texto completo da fonteSuter, Susanne K., Bo Ma e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Clustering 3D"
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.
Texto completo da fonteZhang, Yongrnei, e 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.
Texto completo da fonteTaubner, Felix, Florian Tschopp, Tonci Novkovic, Roland Siegwart e 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.
Texto completo da fonteKrahn, Maximilian, Florian Bernard e 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.
Texto completo da fonteEn-Ya, Shen, Wang Wen-Ke, Li Si-Kun e 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.
Texto completo da fonteLi, Tianjian, Yan Han, Xiaoyao Liang, Hsien-Hsin S. Lee e 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.
Texto completo da fonteHinojosa, Carlos A., Jorge Bacca e 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.
Texto completo da fonteWagner, Patrick, Jakob Paul Morath, Arturo Zychlinsky, Klaus-Robert Muller e 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.
Texto completo da fonteAtchaya, V., e 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.
Texto completo da fonteMagnusson, Martin, Tomasz Piotr Kucner, Saeed Gholami Shahbandi, Henrik Andreasson e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Clustering 3D"
Mohapatra, Sucheta. Dynamic Through-Silicon Via Clustering in 3D IC Floorplanning for Early Performance Optimization. Portland State University Library, janeiro de 2000. http://dx.doi.org/10.15760/etd.7437.
Texto completo da fonte