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Journal articles on the topic 'Segmentation multivariée'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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1

Klose, J. "Binary Segmentation for Multivariate Polynomials." Journal of Complexity 11, no. 3 (1995): 330–43. http://dx.doi.org/10.1006/jcom.1995.1015.

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2

Neubauer, Jakob, Konrad Wilhelm, Christian Gratzke, Fabian Bamberg, Marco Reisert, and Elias Kellner. "Effect of surface-partial-volume correction and adaptive threshold on segmentation of uroliths in computed tomography." PLOS ONE 18, no. 6 (2023): e0286016. http://dx.doi.org/10.1371/journal.pone.0286016.

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Computed tomography (CT) is used to diagnose urolithiasis, a prevalent condition. In order to establish the strongest foundation for the quantifiability of urolithiasis, this study aims to develop semi-automated urolithiasis segmentation methods for CT images that differ in terms of surface-partial-volume correction and adaptive thresholding. It also examines the diagnostic accuracy of these methods in terms of volume and maximum stone diameter. One hundred and one uroliths were positioned in an anthropomorphic phantom and prospectively examined in CT. Four different segmentation methods were

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3

Esteban, Oscar, Gert Wollny, Subrahmanyam Gorthi, et al. "MBIS: Multivariate Bayesian Image Segmentation tool." Computer Methods and Programs in Biomedicine 115, no. 2 (2014): 76–94. http://dx.doi.org/10.1016/j.cmpb.2014.03.003.

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Medl, Matthias, Dianne Cook, and Ursula Laa. "Demonstrating the Capabilities of the lionfish Software for Interactive Visualization of Market Segmentation Partitions." Austrian Journal of Statistics 54, no. 3 (2025): 71–99. https://doi.org/10.17713/ajs.v54i3.2058.

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Market segmentation partitions multivariate data using some clustering algorithm, resulting in some number of homogeneousclusters of consumers for marketing purposes. Often this type of data has no clear cluster structure, that is, no separations or gapsbetween clusters of points exist, which is why this is considered partitioning rather than clustering. Understanding the differencesbetween the clusters is typically done by examining single features. However, this can be inconclusive as multiple clusters might share similar characteristics on individual features and the market segmentation par

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Portillo-García, J., I. Trueba-Santander, G. de Miguel-Vela, and C. Alberola-López. "Efficient multispectral texture segmentation using multivariate statistics." IEE Proceedings - Vision, Image, and Signal Processing 145, no. 5 (1998): 357. http://dx.doi.org/10.1049/ip-vis:19982315.

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6

Lim, Hyunki, Heeseung Choi, Yeji Choi, and Ig-Jae Kim. "Memetic algorithm for multivariate time-series segmentation." Pattern Recognition Letters 138 (October 2020): 60–67. http://dx.doi.org/10.1016/j.patrec.2020.06.022.

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7

Hallac, David, Peter Nystrup, and Stephen Boyd. "Greedy Gaussian segmentation of multivariate time series." Advances in Data Analysis and Classification 13, no. 3 (2018): 727–51. http://dx.doi.org/10.1007/s11634-018-0335-0.

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8

Sudbury, Lynn, and Peter Simcock. "A multivariate segmentation model of senior consumers." Journal of Consumer Marketing 26, no. 4 (2009): 251–62. http://dx.doi.org/10.1108/07363760910965855.

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Laksono, Bagaskoro Cahyo, and Ika Yuni Wulansari. "Estimating Customer Lifetime Value in the E-Commerce Industry Using Multivariate Analysis." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (2022): 507–18. http://dx.doi.org/10.34123/icdsos.v2021i1.161.

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Companies can develop their business using big data to support decision-making. Big data in the e-commerce industry that includes size and speed of high transactions can be used to analyze customer behaviour and predict customer value. Nowadays, companies are starting to develop customer-oriented rather than product-oriented business interests. One way that can be used to determine customer value is by calculating Customer Lifetime Value (CLV). By knowing CLV at the individual level, it will be useful to help decision-makers to develop customer segmentation and resource allocation. It is impor

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Omranian, Nooshin, Sebastian Klie, Bernd Mueller-Roeber, and Zoran Nikoloski. "Network-Based Segmentation of Biological Multivariate Time Series." PLoS ONE 8, no. 5 (2013): e62974. http://dx.doi.org/10.1371/journal.pone.0062974.

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11

Ip, Barry, and Gabriel Jacobs. "Segmentation of the games market using multivariate analysis." Journal of Targeting, Measurement and Analysis for Marketing 13, no. 3 (2005): 275–87. http://dx.doi.org/10.1057/palgrave.jt.5740154.

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Noordam, J. C., W. H. A. M. van den Broek, and L. M. C. Buydens. "Unsupervised segmentation of predefined shapes in multivariate images." Journal of Chemometrics 17, no. 4 (2003): 216–24. http://dx.doi.org/10.1002/cem.794.

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13

Guo, Hongyue, Xiaodong Liu, and Lixin Song. "Dynamic programming approach for segmentation of multivariate time series." Stochastic Environmental Research and Risk Assessment 29, no. 1 (2014): 265–73. http://dx.doi.org/10.1007/s00477-014-0897-0.

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14

Wang, Ling, Kang Li, Qian Ma, and YanRong Lu. "Hybrid dynamic learning mechanism for multivariate time series segmentation." Statistical Analysis and Data Mining: The ASA Data Science Journal 13, no. 2 (2020): 165–77. http://dx.doi.org/10.1002/sam.11448.

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15

K. Naveen Kumar,. "Multivariate Approach for Texture Segmentation using Probabilistic Statistical Model." Journal of Electrical Systems 20, no. 2 (2024): 2381–85. http://dx.doi.org/10.52783/jes.2003.

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The analysis of the regions of the image is of the prerogatives in the fields of medical and global systems meant for location identification. This analysis is strongly associated with partitions of regions of interest such as segmentation. For an effective strategy of analyzing the regions of interest, texture of the image plays a major concern. The texture is generally characterized using signal processing methods namely Discrete Cosine Transformation coefficients and their specific insights leading to feature vector selection. Further, to identify regions, a statistical model needs to be id

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16

Makhambayeva, I. U. "ADAPTIVE SEGMENTATION OF MULTI-WIRE TRANSMISSION LINES." ТЕХНИКА ҒЫЛЫМДАРЫ ЖӘНЕ ТЕХНОЛОГИЯ 8, no. 4 (2024): 11–17. https://doi.org/10.52081/tst.2024.v04.i8.044.

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The article discusses a comparison of adaptive segmentation methods for multi-wire transmission lines in quasi-static analysis by the method of moments. A two-dimensional version of the method of moments for transmission lines is presented and an overview of the adaptive iterative choice of optimal segmentation of the boundaries of conductors and dielectrics in electrostatics problems is presented, as well as an overview of the joint use of quasi-statics and electrodynamics with the method of moments. The construction of the grid affects the properties of the resulting system of linear algebra

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17

Castro-López, Claudio, Purificación Vicente-Galindo, Purificación Galindo-Villardón, and Oscar Borrego-Hernández. "TAID-LCA: Segmentation Algorithm Based on Ternary Trees." Mathematics 10, no. 4 (2022): 560. http://dx.doi.org/10.3390/math10040560.

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In this work, a statistical method for the segmentation of samples and/or populations is presented, which is based on a ternary tree structure. This approach overcomes known limitations of other segmentation methods such as CHAID, concerning the multivariate response and the non-symmetric relationship between explanatory and response variables. The multivariate response segmentation problem is handled through latent class models, while the factorial decomposition of the explanatory capability of variables is based on the Non-Symmetrical Correspondence Analysis. Stop criteria based on the CATAN

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18

Lei, Tao, Yi Wang, and Weiwei Luo. "Multivariate Self-Dual Morphological Operators Based on Extremum Constraint." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/596348.

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Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation; they treat the image foreground and background identically. However, it is difficult to extend SDMO to multichannel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Furthermore, utilizing extremum constraint to opt

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19

Cao, Haoyin, Andrea Morotti, Federico Mazzacane, et al. "External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage." Journal of Clinical Medicine 12, no. 12 (2023): 4005. http://dx.doi.org/10.3390/jcm12124005.

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Background: The objective of this study was to assess the performance of the first publicly available automated 3D segmentation for spontaneous intracerebral hemorrhage (ICH) based on a 3D neural network before and after retraining. Methods: We performed an independent validation of this model using a multicenter retrospective cohort. Performance metrics were evaluated using the dice score (DSC), sensitivity, and positive predictive values (PPV). We retrained the original model (OM) and assessed the performance via an external validation design. A multivariate linear regression model was used

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20

Noyel, Guillaume, Jesus Angulo, Dominique Jeulin, Daniel Balvay, and Charles-André Cuenod. "MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES." Image Analysis & Stereology 34, no. 1 (2014): 1. http://dx.doi.org/10.5566/ias.1109.

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We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is perf

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21

ANJUM, ARFA, SEEMA JAGGI, SHWETANK LALL, et al. "Segmentation of genomic data through multivariate statistical approaches: comparative analysis." Indian Journal of Agricultural Sciences 92, no. 7 (2022): 892–96. http://dx.doi.org/10.56093/ijas.v92i7.118040.

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Segmenting a series of measurements along a genome into regions with distinct characteristics is widely used toidentify functional components of a genome. The majority of the research on biological data segmentation focuses on the statistical problem of identifying break or change-points in a simulated scenario using a single variable. Despite the fact that various strategies for finding change-points in a multivariate setup through simulation are available, work on segmenting actual multivariate genomic data is limited. This is due to the fact that genomic data is huge in size and contains a

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22

Li, Min, and Yu-Mei Huang. "An $L_0$-Norm Regularized Method for Multivariate Time Series Segmentation." East Asian Journal on Applied Mathematics 12, no. 2 (2022): 353–66. http://dx.doi.org/10.4208/eajam.180921.050122.

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23

Somasekhar, G., K. Srinivasa Krishna, Ashok Kumar Reddy, T. Kishore Kumar, and G. Somasekhar. "Shopper Segmentation Using Multivariate Risk Analysis for Innovative Marketing Strategies." International Journal of Asian Business and Information Management 12, no. 1 (2021): 60–74. http://dx.doi.org/10.4018/ijabim.20210101.oa4.

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Shopper buying behaviour is essential for the retailers to segment the shoppers in accordance to their disruptive attitude and perception for better innovative strategies which may lead to higher profits. The major purpose of this study to categorize the shoppers into distinct groups based on their risk-based perception for the organized retail outlets in Bangladesh. Seven hundred eighty-five respondents were responding on 21 variables related to store which influence their buying behaviour. In the present study, the shoppers were classified into three segments such as value seekers and disrup

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24

Hosseini, Jamshid C., Robert R. Harmon, and Martin Zwick. "An Information Theoretic Framework for Exploratory Multivariate Market Segmentation Research." Decision Sciences 22, no. 3 (1991): 663–77. http://dx.doi.org/10.1111/j.1540-5915.1991.tb01289.x.

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25

Piccolboni, Antonio. "Multivariate Segmentation in the Analysis of Transcription Tiling Array Data." Journal of Computational Biology 15, no. 7 (2008): 845–56. http://dx.doi.org/10.1089/cmb.2007.0141.

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26

Zhuang, Xiahai. "Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (2019): 2933–46. http://dx.doi.org/10.1109/tpami.2018.2869576.

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27

Willse, Alan, and Bonnie Tyler. "Poisson and Multinomial Mixture Models for Multivariate SIMS Image Segmentation." Analytical Chemistry 74, no. 24 (2002): 6314–22. http://dx.doi.org/10.1021/ac025561i.

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28

Noordam, J. C., W. H. A. M. van den Broek, and L. M. C. Buydens. "Multivariate image segmentation with cluster size insensitive Fuzzy C-means." Chemometrics and Intelligent Laboratory Systems 64, no. 1 (2002): 65–78. http://dx.doi.org/10.1016/s0169-7439(02)00052-7.

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29

Lambert, Christian, Antoine Lutti, Gunther Helms, Richard Frackowiak, and John Ashburner. "Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians." NeuroImage: Clinical 2 (2013): 684–94. http://dx.doi.org/10.1016/j.nicl.2013.04.017.

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30

Hazel, G. G. "Multivariate Gaussian MRF for multispectral scene segmentation and anomaly detection." IEEE Transactions on Geoscience and Remote Sensing 38, no. 3 (2000): 1199–211. http://dx.doi.org/10.1109/36.843012.

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31

Li, Min, Yumei Huang null, and Youwei Wen. "A Total Variation Based Method for Multivariate Time Series Segmentation." Advances in Applied Mathematics and Mechanics 15, no. 2 (2023): 300–321. http://dx.doi.org/10.4208/aamm.oa-2021-0209.

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32

Picard, F., E. Lebarbier, E. Budinskà, and S. Robin. "Joint segmentation of multivariate Gaussian processes using mixed linear models." Computational Statistics & Data Analysis 55, no. 2 (2011): 1160–70. http://dx.doi.org/10.1016/j.csda.2010.09.015.

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33

Lu, Chang, Jiyou Fei, Xing Zhao, and Xiongfei Shao. "Laser Cutting Thermal Error Prediction Method Based on Multivariate Segmentation." Journal of Physics: Conference Series 2541, no. 1 (2023): 012027. http://dx.doi.org/10.1088/1742-6596/2541/1/012027.

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Abstract With the increasing accuracy of manufacturing processing, laser cutting has become one of the best ways to cut thin-walled parts. A multivariate segmented thermal error prediction method is proposed for thermal errors in laser cutting. Based on actual processing data, we analyzed the thermal expansion of the material during the cutting process. Modeling simulation and heat deformation prediction are performed. W18Cr4V material was used as the study object for computational verification. The results show that multiple-segmented regression can achieve data accuracy of more than 90.4%. F

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34

UR, Orth, and J. Turečková. "Segmenting the tourism market using perceptual and attitudinal mapping." Agricultural Economics (Zemědělská ekonomika) 48, No. 1 (2012): 36–48. http://dx.doi.org/10.17221/5286-agricecon.

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Decreasing numbers of tourists to the Czech Republic point at a weakening competitive position of Czech destinations during the most recent years. For many communities, tourism may be a short-lived economic dream when understanding of tourists&acute; perceptions and travel motives is lacking. The two objectives pursued in this study are 1) an identification of the positions of competing destinations and 2) an a-posteriori segmentation with psychographic variables. Market segmentation becomes the crucial factor in the strategic design process of target marketing. Like many other markets, to

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35

Mishulina, O. A., and I. N. Sukonkin. "Multivariate time series segmentation for generalized description of dynamic systems operation." Optical Memory and Neural Networks 21, no. 2 (2012): 94–104. http://dx.doi.org/10.3103/s1060992x12020038.

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36

Ma, Yi, Harm Derksen, Wei Hong, and John Wright. "Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 9 (2007): 1546–62. http://dx.doi.org/10.1109/tpami.2007.1085.

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37

Schenone, A., F. Firenze, F. Acquarone, M. Gambaro, F. Masulli, and L. Andreucci. "Segmentation of multivariate medical images via unsupervised clustering with “adaptive resolution”." Computerized Medical Imaging and Graphics 20, no. 3 (1996): 119–29. http://dx.doi.org/10.1016/0895-6111(96)00008-0.

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38

Liu, Zhe, Yu-Qing Song, Jian-Mei Chen, Cong-Hua Xie, and Feng Zhu. "Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials." Neural Computing and Applications 21, no. 4 (2011): 801–11. http://dx.doi.org/10.1007/s00521-011-0538-1.

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39

Abonyi, Janos, Balazs Feil, Sandor Nemeth, and Peter Arva. "Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series." Fuzzy Sets and Systems 149, no. 1 (2005): 39–56. http://dx.doi.org/10.1016/j.fss.2004.07.008.

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40

Wang, Ling, Hui Zhu, and Gaofeng Jia. "Adaptive G–G clustering for fuzzy segmentation of multivariate time series." Stochastic Environmental Research and Risk Assessment 34, no. 9 (2020): 1353–67. http://dx.doi.org/10.1007/s00477-020-01817-w.

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41

Tanatavikorn, Harakhun, and Yoshiyuki Yamashita. "Batch Process Monitoring Based on Fuzzy Segmentation of Multivariate Time-Series." Journal of Chemical Engineering of Japan 50, no. 1 (2017): 53–63. http://dx.doi.org/10.1252/jcej.16we193.

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42

Han, Minyeon, and F. C. Park. "DTI Segmentation and Fiber Tracking Using Metrics on Multivariate Normal Distributions." Journal of Mathematical Imaging and Vision 49, no. 2 (2013): 317–34. http://dx.doi.org/10.1007/s10851-013-0466-z.

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43

Qin, A. K., and David A. Clausi. "Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty." IEEE Transactions on Image Processing 19, no. 8 (2010): 2157–70. http://dx.doi.org/10.1109/tip.2010.2045708.

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44

Lazar, Cosmin, Andrei Doncescu, and Nabil Kabbaj. "Non Negative Matrix Factorisation clustering capabilities; application on multivariate image segmentation." International Journal of Business Intelligence and Data Mining 5, no. 3 (2010): 285. http://dx.doi.org/10.1504/ijbidm.2010.033363.

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45

Ding, Mingtao, Lihan He, David Dunson, and Lawrence Carin. "Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process." Bayesian Analysis 7, no. 4 (2012): 813–40. http://dx.doi.org/10.1214/12-ba727.

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46

Noordam, J. C., and W. H. A. M. van den Broek. "Multivariate image segmentation based on geometrically guided fuzzy C-means clustering." Journal of Chemometrics 16, no. 1 (2002): 1–11. http://dx.doi.org/10.1002/cem.656.

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47

Wang, Ling, and Peng Shen. "Memetic segmentation based on variable lag aware for multivariate time series." Information Sciences 657 (February 2024): 120003. http://dx.doi.org/10.1016/j.ins.2023.120003.

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48

Dai, Bing, Yingjie Peng, Ning Lin, and Peng Wang. "Bearing Fault Diagnosis Based on Prime Mean Spectral Segmentation Kurtogram." Journal of Physics: Conference Series 2419, no. 1 (2023): 012080. http://dx.doi.org/10.1088/1742-6596/2419/1/012080.

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Abstract The spectrum-based fault feature extraction method for industrial equipment can avoid the problems of modal aliasing and end effects caused by mode decomposition in the time domain. This paper proposes a kurtogram constructed based on prime mean spectral segmentation. The preset framework realizes fast spectral segmentation, and the multivariate segmentation mode provides a more reasonable distribution of center frequency and bandwidth. The precise location and diagnosis of faults can be achieved by scanning shocks in each frequency band by spectral negentropy. Simulation signals and

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49

Zhang, Changrui, and Jia Wang. "PIS-Net: Efficient Medical Image Segmentation Network with Multivariate Downsampling for Point-of-Care." Entropy 26, no. 4 (2024): 284. http://dx.doi.org/10.3390/e26040284.

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Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In thi

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Gaugel, Stefan, and Manfred Reichert. "Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles." Sensors 23, no. 7 (2023): 3636. http://dx.doi.org/10.3390/s23073636.

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Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and has received much attention in recent industrial research. This paper focuses on the problem of time series segmentation and presents the first in-depth research on transfer learning for deep learning-based time series segmentation on the industrial use case of end-of-line pump testing. In particular, we investigate whether the performance of deep learning models can be increased by pretraining

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