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Artículos de revistas sobre el tema "Clustering 3D"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Peng, Bo, Yuxuan Yao, Qunxia Li, et al. "Clustering information-constrained 3D U-Net subspace clustering for hyperspectral image." Remote Sensing Letters 13, no. 11 (2022): 1131–41. http://dx.doi.org/10.1080/2150704x.2022.2132122.

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12

Lu, Xiaohu, Jian Yao, Jinge Tu, Kai Li, Li Li, and Yahui Liu. "PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 201–8. http://dx.doi.org/10.5194/isprsannals-iii-3-201-2016.

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In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discov

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13

Lu, Xiaohu, Jian Yao, Jinge Tu, Kai Li, Li Li, and Yahui Liu. "PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 201–8. http://dx.doi.org/10.5194/isprs-annals-iii-3-201-2016.

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In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discov

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14

Niu, Jianwei, Zhizhong Li, and Gavriel Salvendy. "Alignment Influence on 3D Anthropometric Data Clustering." Ergonomics Open Journal 1, no. 1 (2008): 62–66. http://dx.doi.org/10.2174/1875934300801010062.

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15

Zhang, Jie, Kangneng Zhou, Yan Luximon, Ping Li, and Hassan Iftikhar. "3D-guided facial shape clustering and analysis." Multimedia Tools and Applications 81, no. 6 (2022): 8785–806. http://dx.doi.org/10.1007/s11042-022-12190-x.

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16

Chahhou, Mohamed, Lahcen Moumoun, Mohamed El Far, and Taoufiq Gadi. "Segmentation of 3D Meshes Usingp-Spectral Clustering." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 8 (2014): 1687–93. http://dx.doi.org/10.1109/tpami.2013.2297314.

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17

Xu, Huaxun, Zhi-Quan Cheng, Ralph R. Martin, and Sikun Li. "3D flow features visualization via fuzzy clustering." Visual Computer 27, no. 6-8 (2011): 441–49. http://dx.doi.org/10.1007/s00371-011-0577-8.

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18

Asorey, Jacobo, Martin Crocce, Enrique Gaztañaga, and Antony Lewis. "Recovering 3D clustering information with angular correlations." Monthly Notices of the Royal Astronomical Society 427, no. 3 (2012): 1891–902. http://dx.doi.org/10.1111/j.1365-2966.2012.21972.x.

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19

Nakagawa, M., T. Yamamoto, S. Tanaka, M. Shiozaki, and T. Ohhashi. "TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 13–18. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-13-2015.

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We focus on a region-based point clustering to extract a polygon from a massive point cloud. In the region-based clustering, RANSAC is a suitable approach for estimating surfaces. However, local workspace selection is required to improve a performance in a surface estimation from a massive point cloud. Moreover, the conventional RANSAC is hard to determine whether a point lies inside or outside a surface. In this paper, we propose a method for panoramic rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point cloud clustering in modeling after

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20

Li, Xin Wu. "A New 3D Medical Data Field Segmentation Algorithm Based on Improved K_Means Clustering." Advanced Materials Research 108-111 (May 2010): 69–73. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.69.

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Direct 3D volume segmentation is one of the difficult and hot research fields in 3D medical data field processing. Using K-means clustering techniques, a new clustering segmentation algorithm is presented. Firstly, According to the physical means of the medical data, the data field is preprocessed to speed up succeed processing. Secondly, the paper deduces and analyzes the clustering and segmentation algorithm and presents some methods to increase the process speed, including improving cluster seed selection, improving calculation flow, and amending pixel processing and operational principle o

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21

Kamburov, Atanas, Michael S. Lawrence, Paz Polak, et al. "Comprehensive assessment of cancer missense mutation clustering in protein structures." Proceedings of the National Academy of Sciences 112, no. 40 (2015): E5486—E5495. http://dx.doi.org/10.1073/pnas.1516373112.

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Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known

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22

Lipnickas, Arūnas, and Vidas Raudonis. "Contour Representation by Clustering Curvatures of the 3D Objects." Solid State Phenomena 147-149 (January 2009): 633–38. http://dx.doi.org/10.4028/www.scientific.net/ssp.147-149.633.

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The purpose of this work is to segment large size triangulated surfaces and the contours extraction of the 3D object by the use of the object curvature value. The curvatures values allow categorizing the type of the local surface of the 3D object. In present work the curvature was estimated for the free-form surfaces obtained by the 3D range scanner. A free-form surface is the surface such that the surface normal is defined and continuous everywhere, except at sharp corners and edges [2, 5]. Two types of distance measurements functions based on Euclidian distance, bounded box and topology of s

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23

Liu, Yongfan, Sen Du, and Youyong Kong. "Supervoxel Clustering with a Novel 3D Descriptor for Brain Tissue Segmentation." International Journal of Machine Learning and Computing 10, no. 3 (2020): 501–6. http://dx.doi.org/10.18178/ijmlc.2020.10.3.964.

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24

Zhang, Wenbo, Lu Zhang, Ping Hu, Liqian Ma, Yunzhi Zhuge, and Huchuan Lu. "Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10166–75. https://doi.org/10.1609/aaai.v39i10.33103.

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Injecting semantics into 3D Gaussian Splatting (3DGS) has recently garnered significant attention. While current approaches typically distill 3D semantic features from 2D foundational models (e.g., CLIP and SAM) to facilitate novel view segmentation and semantic understanding, their heavy reliance on 2D supervision can undermine cross-view semantic consistency and necessitate complex data preparation processes, therefore hindering view-consistent scene understanding. In this work, we present FreeGS, an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene underst

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25

Mahdaoui, Abdelaaziz, and El Hassan Sbai. "3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering." Advances in Multimedia 2020 (July 15, 2020): 1–10. http://dx.doi.org/10.1155/2020/8825205.

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While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is

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26

Xie, Zhong Ping. "Simulation Analysis on Dynamics Clustering Algorithm Based on 3D Imaging Technology." Applied Mechanics and Materials 556-562 (May 2014): 4994–97. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4994.

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In this paper, we use 3D imaging technique to conduct in-depth research in the football training, and obtain the 3D space image of the best football team. We use FPGA hardware platform to design the control program of 3D image, and judge the performance of synthetic parameters, and test process curve and schematic diagram of 3D imaging. Combined with Kmeans algorithm we design the clustering algorithm mathematical model of 3D image, and give the control programming. Finally, based on the 3D synthesis image and optimization of display technology, using the image acquisition and skill of physica

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27

Vallet, B., B. Soheilian, and M. Brédif. "Combinatorial clustering and Its Application to 3D Polygonal Traffic Sign Reconstruction From Multiple Images." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (August 7, 2014): 165–72. http://dx.doi.org/10.5194/isprsannals-ii-3-165-2014.

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The 3D reconstruction of similar 3D objects detected in 2D faces a major issue when it comes to grouping the 2D detections into clusters to be used to reconstruct the individual 3D objects. Simple clustering heuristics fail as soon as similar objects are close. This paper formulates a framework to use the geometric quality of the reconstruction as a hint to do a proper clustering. We present a methodology to solve the resulting combinatorial optimization problem with some simplifications and approximations in order to make it tractable. The proposed method is applied to the reconstruction of 3

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28

Jinming, Chen. "Obstacle Detection Based on 3D Lidar Euclidean Clustering." Applied Science and Innovative Research 5, no. 3 (2021): p39. http://dx.doi.org/10.22158/asir.v5n3p39.

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Environment perception is the basis of unmanned driving and obstacle detection is an important research area of environment perception technology. In order to quickly and accurately identify the obstacles in the direction of vehicle travel and obtain their location information, combined with the PCL (Point Cloud Library) function module, this paper designed a euclidean distance based Point Cloud clustering obstacle detection algorithm. Environmental information was obtained by 3D lidar, and ROI extraction, voxel filtering sampling, outlier point filtering, ground point cloud segmentation, Eucl

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29

Hu, Ruizhen, Lubin Fan, and Ligang Liu. "Co-Segmentation of 3D Shapes via Subspace Clustering." Computer Graphics Forum 31, no. 5 (2012): 1703–13. http://dx.doi.org/10.1111/j.1467-8659.2012.03175.x.

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30

Ben Hamza, A. "Graph regularized sparse coding for 3D shape clustering." Knowledge-Based Systems 92 (January 2016): 92–103. http://dx.doi.org/10.1016/j.knosys.2015.10.019.

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31

Horváth, Gergely, and Gábor Erdős. "Object localization utilizing 3D point cloud clustering approach." Procedia CIRP 93 (2020): 508–13. http://dx.doi.org/10.1016/j.procir.2020.04.132.

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32

Lacko, Daniël, Toon Huysmans, Jochen Vleugels, et al. "Product sizing with 3D anthropometry andk-medoids clustering." Computer-Aided Design 91 (October 2017): 60–74. http://dx.doi.org/10.1016/j.cad.2017.06.004.

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33

Naveen, J., P. J. A. Alphonse, and Sivaraj Chinnasamy. "3D grid clustering scheme for wireless sensor networks." Journal of Supercomputing 76, no. 6 (2018): 4199–211. http://dx.doi.org/10.1007/s11227-018-2306-9.

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34

Munshi, D., G. Pratten, P. Valageas, P. Coles, and P. Brax. "Galaxy clustering in 3D and modified gravity theories." Monthly Notices of the Royal Astronomical Society 456, no. 2 (2015): 1627–44. http://dx.doi.org/10.1093/mnras/stv2724.

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35

Stockman, George, and Juan Carlos Esteva. "3D object pose form clustering with multiple views." Pattern Recognition Letters 3, no. 4 (1985): 279–86. http://dx.doi.org/10.1016/0167-8655(85)90008-x.

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36

TORRA, VICENÇ, and SADAAKI MIYAMOTO. "HIERARCHICAL SPHERICAL CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 02 (2002): 157–72. http://dx.doi.org/10.1142/s0218488502001399.

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This work introduces an alternative representation for large dimensional data sets. Instead of using 2D or 3D representations, data is located on the surface of a sphere. Together with this representation, a hierarchical clustering algorithm is defined to analyse and extract the structure of the data. The algorithm builds a hierarchical structure (a dendrogram) in such a way that different cuts of the structure lead to different partitions of the surface of the sphere. This can be seen as a set of concentric spheres, each one being of different granularity. Also, to obtain an initial assignmen

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37

Hong, Qi, and Gai Dong Han. "Research on FCM Algorithm in the 3D Visualization System of Medical Images." Applied Mechanics and Materials 727-728 (January 2015): 839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.839.

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FCM is a fuzzy segmentation based on overall situation, is typically applied in data mining and pattern recognition. In this paper, the segmentation of brain CT is achieved through FCM clustering algorithm in three-dimensional medical image visualization system, the organization in brain CT processed with FCM clustering can be well identified.However, the connectivity of brain organization is severely damaged. In view of this situation, it is proposed that the object in the brain image through clustering be judged by classification of its neighbor domain. The result shows that this method brin

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38

Bunnik, Evelien M., Aarthi Venkat, Jianlin Shao, et al. "Comparative 3D genome organization in apicomplexan parasites." Proceedings of the National Academy of Sciences 116, no. 8 (2019): 3183–92. http://dx.doi.org/10.1073/pnas.1810815116.

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The positioning of chromosomes in the nucleus of a eukaryotic cell is highly organized and has a complex and dynamic relationship with gene expression. In the human malaria parasite Plasmodium falciparum, the clustering of a family of virulence genes correlates with their coordinated silencing and has a strong influence on the overall organization of the genome. To identify conserved and species-specific principles of genome organization, we performed Hi-C experiments and generated 3D genome models for five Plasmodium species and two related apicomplexan parasites. Plasmodium species mainly sh

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39

Nguyen, Duy M. H., Hoang Nguyen, Truong T. N. Mai, et al. "Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14426–35. http://dx.doi.org/10.1609/aaai.v37i12.26687.

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Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally

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Borowska, Marta, Tomasz Jasiński, Sylwia Gierasimiuk, et al. "Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets." Sensors 23, no. 21 (2023): 8940. http://dx.doi.org/10.3390/s23218940.

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Dental diagnostic imaging has progressed towards the use of advanced technologies such as 3D image processing. Since multidetector computed tomography (CT) is widely available in equine clinics, CT-based anatomical 3D models, segmentations, and measurements have become clinically applicable. This study aimed to use a 3D segmentation of CT images and volumetric measurements to investigate differences in the surface area and volume of equine incisors. The 3D Slicer was used to segment single incisors of 50 horses’ heads and to extract volumetric features. Axial vertical symmetry, but not horizon

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41

Garzon Dasgupta, Andrei K., Alexey A. Martyanov, Aleksandra A. Filkova, Mikhail A. Panteleev, and Anastasia N. Sveshnikova. "Development of a Simple Kinetic Mathematical Model of Aggregation of Particles or Clustering of Receptors." Life 10, no. 6 (2020): 97. http://dx.doi.org/10.3390/life10060097.

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The process of clustering of plasma membrane receptors in response to their agonist is the first step in signal transduction. The rate of the clustering process and the size of the clusters determine further cell responses. Here we aim to demonstrate that a simple 2-differential equation mathematical model is capable of quantitative description of the kinetics of 2D or 3D cluster formation in various processes. Three mathematical models based on mass action kinetics were considered and compared with each other by their ability to describe experimental data on GPVI or CR3 receptor clustering (2

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42

Suo, Xuesong, Chenwei Hou, Lei Sun, and Zi Liu. "3D Reconstruction Optimization Algorithm Based on Dynamic Clustering in Transformer Substation." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 248–51. http://dx.doi.org/10.1166/jctn.2017.6156.

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The visual substation model construction is paid more and more attention. In order to build the substation 3D model without increasing the workload, researchers in related fields often make 3D modeling by transforming the 2D images into 3D model. This paper proposes a reconstruction algorithm based on dynamic clustering algorithm which is used in reconstruction of transformer substation. According to this method, a dynamic cluster center array can be established, and the different shapes of the same device can be divided, and the information can be extracted and matched with the 3D model libra

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43

Mezghani, Neila, Rayan Soltana, Youssef Ouakrim, et al. "Healthy Knee Kinematic Phenotypes Identification Based on a Clustering Data Analysis." Applied Sciences 11, no. 24 (2021): 12054. http://dx.doi.org/10.3390/app112412054.

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The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69

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44

Li, Miaopeng, Zimeng Zhou, and Xinguo Liu. "3D hypothesis clustering for cross-view matching in multi-person motion capture." Computational Visual Media 6, no. 2 (2020): 147–56. http://dx.doi.org/10.1007/s41095-020-0171-y.

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Abstract We present a multiview method for markerless motion capture of multiple people. The main challenge in this problem is to determine cross-view correspondences for the 2D joints in the presence of noise. We propose a 3D hypothesis clustering technique to solve this problem. The core idea is to transform joint matching in 2D space into a clustering problem in a 3D hypothesis space. In this way, evidence from photometric appearance, multiview geometry, and bone length can be integrated to solve the clustering problem efficiently and robustly. Each cluster encodes a set of matched 2D joint

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45

Du, Weinan, Jinghua Li, Fei Wu, Yanfeng Sun, and Yongli Hu. "Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction." Applied Sciences 9, no. 8 (2019): 1559. http://dx.doi.org/10.3390/app9081559.

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As a fundamental and challenging problem, non-rigid structure-from-motion (NRSfM) has attracted a large amount of research interest. It is worth mentioning that NRSfM has been applied to dynamic scene understanding and motion segmentation. Especially, a motion segmentation approach combining NRSfM with the subspace representation has been proposed. However, the current subspace representation for non-rigid motions clustering do not take into account the inherent sequential property, which has been proved vital for sequential data clustering. Hence this paper proposes a novel framework to segme

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46

Zhao, Zhidong, Duoshui Shi, Guohua Hui, and Xiaohong Zhang. "An Energy-Optimization Clustering Routing Protocol Based on Dynamic Hierarchical Clustering in 3D WSNs." IEEE Access 7 (2019): 80159–73. http://dx.doi.org/10.1109/access.2019.2923882.

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47

Andronov, Leonid, Jonathan Michalon, Khalid Ouararhni, et al. "3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations." Bioinformatics 34, no. 17 (2018): 3004–12. http://dx.doi.org/10.1093/bioinformatics/bty200.

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48

Jiang, Wuhua, Chuanzheng Song, Hai Wang, Ming Yu, and Yajie Yan. "Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach." Machines 11, no. 1 (2023): 54. http://dx.doi.org/10.3390/machines11010054.

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For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we p

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49

Marcatili, Paolo, Konstantinos Mochament, Andreas Agathangelidis, et al. "Automated Clustering Analysis of Immunoglobulin Sequences in Chronic Lymphocytic Leukemia Based on 3D Structural Descriptors." Blood 128, no. 22 (2016): 4365. http://dx.doi.org/10.1182/blood.v128.22.4365.4365.

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Abstract Immunoglobulins (Igs) are crucial for the defense against pathogens, but they are also important in many clinical and biotechnological applications. Their characteristics, and ultimately their function, depend on their three-dimensional (3D) structure; however, the procedures to experimentally determine it are extremely laborious and demanding. Hence, the ability to gain insight into the structure of Igs at large relies on the availability of tools and algorithms for producing accurate Ig structural models based on their primary sequence alone. These models can then be used to determi

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50

Emadi, Seyyedbehrad, and Marco Limongiello. "Optimizing 3D Point Cloud Reconstruction Through Integrating Deep Learning and Clustering Models." Electronics 14, no. 2 (2025): 399. https://doi.org/10.3390/electronics14020399.

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Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step methodology: first, a variational autoencoder reduces features, followed by clustering models to assess and mitigate noise in the point clouds. This study evaluates four clustering methods—k-means, agglomerative clustering, Spectral clustering, and Gaussian mixture model—based on photogrammetric para

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