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Artículos de revistas sobre el tema "Clustering 3D"

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Publicado: 25 de mayo de 2024

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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 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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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 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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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 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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4

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 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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5

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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6

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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7

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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8

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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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 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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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 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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11

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 discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.
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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/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 discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.
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13

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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14

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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15

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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16

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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17

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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18

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 point cloud registration. First, we propose a point cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point cloud from some viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point cloud clustering from a complex indoor environment.
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19

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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20

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 surface were used for the curvature estimation. Clustering technique has been involved to cluster the values of the curvature for 3D object contour representation. The described technique was applied to the 3D objects with free-form surfaces such as the human foot and cube.
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21

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 of algorithm. Finally, the experimental results show that the algorithm has high accuracy when used to segment 3D medical tissue and can improve process speed greatly.
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22

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 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg2+, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.
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23

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, Euclide clustering and other processing were carried out to achieve a complete PCL based 3D point cloud obstacle detection method. The experimental results show that the vehicle can effectively identify the obstacles in the area and obtain their location information.
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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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 used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.
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32

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 assignment of the data on the surface of the sphere, a method based on Sammon's mapping has been developed.
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33

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 physical body, finally we get the best offensive and defensive football team. It provides the theory reference for the training of football players.
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34

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 showed clustering of centromeres, telomeres, and virulence genes. In P. falciparum, the heterochromatic virulence gene cluster had a strong repressive effect on the surrounding nuclear space, while this was less pronounced in Plasmodium vivax and Plasmodium berghei, and absent in Plasmodium yoelii. In Plasmodium knowlesi, telomeres and virulence genes were more dispersed throughout the nucleus, but its 3D genome showed a strong correlation with gene expression. The Babesia microti genome showed a classical Rabl organization with colocalization of subtelomeric virulence genes, while the Toxoplasma gondii genome was dominated by clustering of the centromeres and lacked virulence gene clustering. Collectively, our results demonstrate that spatial genome organization in most Plasmodium species is constrained by the colocalization of virulence genes. P. falciparum and P. knowlesi, the only two Plasmodium species with gene families involved in antigenic variation, are unique in the effect of these genes on chromosome folding, indicating a potential link between genome organization and gene expression in more virulent pathogens.
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35

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 brings a significant improvement in the problem of organization connectivity brought by FCM clustering. Judging the brain image through FCM clustering by classification of its neighbor domain, a brain CT image of better organization integrity and connectivity can be got.
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36

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 3D traffic signs from their 2D detections to demonstrate its capacity to solve ambiguities.
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37

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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38

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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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, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.
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40

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 segment the complex and non-rigid motion via an ordered subspace representation method for the reconstructed 3D data, where the sequential property is properly formulated in the procedure of learning the affinity matrix for clustering with simultaneously recovering the 3D non-rigid motion by a monocular camera with 2D point tracks. Experiment results on three public sequential action datasets, BU-4DFE, MSR and UMPM, verify the benefits of method presented in this paper for classical complex non-rigid motion analysis and outperform state-of-the-art methods with lowest subspace clustering error (SCE) rates and highest normalized mutual information (NMI) in subspace clustering and motion segmentation fields.
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41

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 horizontal, of the incisors was evidenced. The surface area and volume differed significantly between temporary and permanent incisors, allowing for easy eruption-related clustering of the CT-based 3D images with an accuracy of >0.75. The volumetric features differed partially between center, intermediate, and corner incisors, allowing for moderate location-related clustering with an accuracy of >0.69. The volumetric features of mandibular incisors’ equine odontoclastic tooth resorption and hypercementosis (EOTRH) degrees were more than those for maxillary incisors; thus, the accuracy of EOTRH degree-related clustering was >0.72 for the mandibula and >0.33 for the maxilla. The CT-based 3D images of equine incisors can be successfully segmented using the routinely achieved multidetector CT data sets and the proposed data-processing approaches.
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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 library to complete the 3D model building. The verification results show that the proposed method has higher precision and recall. At last this paper gives a simple example and a complicated example to verify the validity of the method.
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43

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 (2D) and albumin or platelet aggregation (3D) in response to activation. The models were able to successfully describe experimental data without losing accuracy after switching between complex and simple models. However, additional restrictions on parameter values are required to match a single set of parameters for the given experimental data. The extended clustering model captured several properties of the kinetics of cluster formation, such as the existence of only three typical steady states for this system: unclustered receptors, receptor dimers, and clusters. Therefore, a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering in response to activation both in 2D and in 3D.
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44

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 biomechanical parameters of interest extracted from the 3D kinematic curves. The data representations are followed by a clustering process, based on the BIRCH (balanced iterative reducing and clustering using hierarchies) discriminant model, to separate 3D knee kinematics into homogeneous groups or clusters. Phenotypes were obtained by averaging those groups. We validated the clusters using inter-cluster correlation and statistical hypothesis tests. The simulation results showed that the global approach is more efficient, and it allows the identification of three descriptive 3D kinematic phenotypes within a healthy knee population.
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45

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 joints for the same person across different views, from which the 3D joints can be effectively inferred. We then assemble the inferred 3D joints to form full-body skeletons for all persons in a bottom–up way. Our experiments demonstrate the robustness of our approach even in challenging cases with heavy occlusion, closely interacting people, and few cameras. We have evaluated our method on many datasets, and our results show that it has significantly lower estimation errors than many state-of-the-art methods.
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46

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 proposed an adaptive neighborhood search radius clustering algorithm, based on the analysis of the relationship between the clustering radius and point cloud spatial distribution, adopting the point cloud pitch angle and the horizontal angle resolution of the lidar, to determine the clustering threshold. Finally, an autonomous vehicle platform and the offline autonomous driving KITTI dataset were used to conduct multi-scene comparative experiments between the proposed method and a Euclidean clustering method. The multi-scene real vehicle experimental results showed that our method improved clustering accuracy by 6.94%, and the KITTI dataset experimental results showed that the F1 score increased by 0.0629.
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47

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 determine structural and eventually functional similarities between different Igs. An example of such a task is the clustering of Igs based on their structure to determine meaningful common features such as the possible existence of common molecular targets (antigens). Several approaches have been proposed in order to achieve an optimal solution to this task yet their results were hindered mainly due to the lack of efficient clustering methods based on the similarity of 3D structure descriptors. Here, we present a novel workflow for robust Ig 3D modeling and automated clustering. We validated our protocol in chronic lymphocytic leukemia (CLL), where the clonotypic Igs are critically implicated in the disease ontogeny and evolution. Indeed, immunogenetic studies on the clonotypic Igs have strongly implicated antigen selection in the pathogenesis of CLL, while also providing robust prognostic information. In the present study, we used the structure prediction tools PIGS and I-TASSER for creating the 3D models and the TM-align algorithm to superpose them. The innovation of the current methodology resides in the usage of methods adapted from 3D content-based search methodologies to determine the local structural similarity between the 3D models. The Fast Point Feature Histograms descriptors derived from the structurally aligned parts are used to compute a distance matrix, which is then used as input for the clustering procedure. Clustering analysis on the data is performed through the application of the agglomerative and density-based clustering approaches. The first method is unsupervised whereas the second belongs to the semi-supervised type, i.e. requires a predefined number of clusters. To evaluate the quality of the herein described workflow, we performed a supervised analysis of 125 Ig 3D models originating from 5 CLL stereotyped subsets i.e. subgroups sharing (quasi) identical IGs, namely subsets #1, #2, #4, #6, #8. The reasoning behind this choice was that (i) homologous Ig primary sequences can be reasonably anticipated to be reflected in overall similar 3D structures, hence providing a reference for evaluating the developed workflow; and, (ii) these subsets are well characterized at both the clinical and biological levels. Subset size distribution was as follows: subset #1 (IGHV clan I/IGKV1(D)-39), n=37; subset #2 (IGHV3-21/IGLV3-21), n=43; subset #4 (IGHV4-34/IGKV2-30), n=22; subset #6 (IGHV1-69/IGKV3-20), n=12; and, subset #8 (IGHV4-39/IGKV1(D)-39), n=11. Overall, we obtained a high level of clustering accuracy i.e. Ig 3D model clusters matched to a very high degree the subsets defined by Ig primary sequence similarity. In detail, 5 Ig 3D model clusters were produced by: (i) cluster 1 containing 37/37 (100%) subset #1 models and one (8.3%) subset #6 model, (ii) cluster 2 containing 43/43 (100%) subset #2 models, (iii) cluster 3 containing 21/22 (95.5%) subset #4 models, (iv) cluster 4 containing 11/12 (91.7%) #6 models, and, (v) cluster 5 containing 11/11 (100%) subset #8 models along with a single (4.5%) subset #4 model (subsets #4 and #8 concern IgG CLL, in itself a rarity for CLL). These findings support that the innovative workflow described here enables robust clustering of 3D models produced from Ig sequences from patients with CLL. Furthermore, they indicate that CLL classification based on stereotypy of Ig primary sequences is likely also verified at the Ig 3D structural level. Studies are ongoing for both addressing the minor discrepancies observed here and producing the unsupervised 3D clustering of the IGs from a large series of both stereotyped and non-stereotyped CLL cases. Disclosures Rosenquist: Gilead Sciences: Speakers Bureau. Stamatopoulos:Gilead: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria, Other: Travel expenses; Janssen: Honoraria, Other: Travel expenses, Research Funding; Novartis: Honoraria, Research Funding.
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48

Horache, S., F. Goulette, J. E. Deschaud, T. Lejars, and K. Gruel. "AUTOMATIC CLUSTERING OF CELTIC COINS BASED ON 3D POINT CLOUD PATTERN ANALYSIS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 973–80. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-973-2020.

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Abstract. The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is very tedious. In this paper, we propose a method to automatically cluster dies, based on 3D scans of coins. It is based on three steps: registration, comparison and graph-based clustering. Experimental results on 90 coins coming from a Celtic treasury from the II-Ith century BC show a clustering quality equivalent to expert’s work.
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49

Suhaibah, A., U. Uznir, F. Anton, D. Mioc, and A. A. Rahman. "3D PARTITION-BASED CLUSTERING FOR SUPPLY CHAIN DATA MANAGEMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 9–17. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-9-2015.

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Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partitionbased clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.
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Azri, S., U. Ujang, and A. Abdul Rahman. "DENDROGRAM CLUSTERING FOR 3D DATA ANALYTICS IN SMART CITY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (October 30, 2018): 247–53. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-247-2018.

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<p><strong>Abstract.</strong> Smart city is a connection of physical and social infrastructure together with the information technology to leverage the collective intelligence of the city. Cities will build huge data centres. These data are collected from sensors, social media, and legacy data sources. In order to be smart, cities needs data analysis to identify infrastructure that needs to be improved, city planning and predictive analysis for citizen safety and security. However, no matter how much smart city focus on the updated technology, data do not organize themselves in a database. Such tasks require a sophisticated database structure to produce informative data output. Furthermore, increasing number of smart cities and generated data from smart cities contributes to current phenomenon called big data. These large and complex data collections would be difficult to process using regular database management tools or traditional data processing applications. There are multiple challenges for big data, including visualization, mining, analysis, capture, storage, search, and sharing. Efficient data analysis mechanisms are necessary to search and extract valuable patterns and knowledge through the big data of smart cities. In this paper, we present a technique of three-dimensional data analytics using dendrogram clustering approach. Data will be organized using this technique and several output and analyses are carried out to proof the efficiency of the structure for three – dimensional data analytics in smart city.</p>
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