Relevant bibliographies by topics / Segmentation multivariée

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Segmentation multivariée'

Author: Grafiati
Published: 22 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Segmentation multivariée"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Segmentation multivariée"

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Asseraf, Mounir. "Extension et optimisation pour la segmentation de la distance de Kolmogorov-Smirnov." Paris 9, 1998. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1998PA090026.

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La segmentation est une méthode qui entre dans le cadre de l'analyse des données multidimensionnelles ; elle se distingue des autres méthodes lorsqu'on passe à la phase descriptive des résultats, telle que la lisibilité des règles de décision. La segmentation peut être vue, d'une part, comme une méthode exploratoire et descriptive permettant de résumer et structurer, sous la forme d'un arbre binaire, un ensemble d'observations multidimensionnelles. D'autre part, comme un outil décisionnel et inférentiel visant à produire une règle de classement sur les objets appartenant à une partition connue
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Ghandi, Sanaa. "Analysis of network delay measurements : Data mining methods for completion and segmentation." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2023. http://www.theses.fr/2023IMTA0382.

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La croissance exponentielle d'Internet nécessite une supervision régulière des métriques réseau. Cette thèse se concentre sur les délais aller-retour et la possibilité de résoudre les problèmes de données manquantes et de segmentation multivariée. La première contribution comprend l'orchestration de campagnes de mesure des délais, ainsi que le développement d'un simulateur qui génère des traces de délais de bout en bout. La deuxième contribution de cette thèse est l’introduction de deux méthodes de complétion de données manquantes. La première méthode repose sur la factorisation de matrices no
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Rzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /." Online version of thesis, 1994. http://hdl.handle.net/1850/12200.

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Templeton, William James. "Consumer interests as market segmentation variables." Thesis, London Business School (University of London), 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312926.

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Rye, Morten Beck. "Image segmentation and multivariate analysis in two-dimensional gel electrophoresis." Doctoral thesis, Norwegian University of Science and Technology, Department of Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1744.

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<p>The topic of this thesis is data-analysis on images from two-dimensional electrophoretic gels. Because of the complexity of these images, there are numerous steps and approaches to such an analysis, and no “golden standard” has yet been established on how to produce the desired output. In this thesis focus is put on two essential fields concerning 2D-gel analysis; registration of images by segregation and protein spot identification, and data-analysis on the output of such a registration by multivariate methods. Image segmentation is mainly concerned with the task of identifying individual
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Lu, Jiang. "Transforms for multivariate classification and application in tissue image segmentation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052195.

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Hosseini-Chaleshtari, Jamshid. "Segment Congruence Analysis: An Information Theoretic Approach." PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/797.

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When there are several possible segmentation variables, marketers must investigate the ramifications of their potential interactions. These include their mutual association, the identification of the best (the distinguished) segmentation variable and its predictability by a set of descriptor variables, and the structure of the multivariate system(s) obtained from the segmentation and descriptor variables. This procedure has been defined as segment congruence analysis (SCA). This study utilizes the information theoretic and the log-linear/logit approaches to address a variety of research questi
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On, Vu Ngoc Minh. "A new minimum barrier distance for multivariate images with applications to salient object detection, shortest path finding, and segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS454.

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Les représentations hiérarchiques d’images sont largement utilisées dans le traitement d’images pour modéliser le contenu d’une image par un arbre. Une hiérarchie bien connue est l’arbre des formes (AdF) qui encode la relation d’inclusion entre les composants connectés à partir de différents niveaux de seuil. Ce genre d’arbre est auto-duale et invariant de changement de contraste, ce qu’il est utilisé dans de nombreuses applications de vision par ordinateur. En raison de ses propriétés, dans cette thèse, nous utilisons cette représentation pour calculer la nouvelle distance qui appartient au d
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Liggett, Rachel Esther. "Multivariate Approaches for Relating Consumer Preference to Sensory Characteristics." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282868174.

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Johansson, David. "Automatic Device Segmentation for Conversion Optimization : A Forecasting Approach to Device Clustering Based on Multivariate Time Series Data from the Food and Beverage Industry." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81476.

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This thesis investigates a forecasting approach to clustering device behavior based on multivariate time series data. Identifying an equitable selection to use in conversion optimization testing is a difficult task. As devices are able to collect larger amounts of data about their behavior it becomes increasingly difficult to utilize manual selection of segments in traditional conversion optimization systems. Forecasting the segments can be done automatically to reduce the time spent on testing while increasing the test accuracy and relevance. The thesis evaluates the results of utilizing mult
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Books on the topic "Segmentation multivariée"

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Kleinbaum, Robert M. Multivariate time series forecasts of market share. Marketing Science Institute, 1988.

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Kleinbaum, Robert M. Multivariate time series forecasts of market share. Marketing Science Institute, 1988.

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Book chapters on the topic "Segmentation multivariée"

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Küppers, Fabian, Anselm Haselhoff, Jan Kronenberger, and Jonas Schneider. "Confidence Calibration for Object Detection and Segmentation." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_8.

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AbstractCalibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-kn
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Hanselmann, Michael, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren, and Fred A. Hamprecht. "Multivariate Watershed Segmentation of Compositional Data." In Discrete Geometry for Computer Imagery. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04397-0_16.

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Maya, Shigeru, Akihiro Yamaguchi, Kaneharu Nishino, and Ken Ueno. "Lag-Aware Multivariate Time-Series Segmentation." In Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.70.

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Ermshaus, Arik, Patrick Schäfer, and Ulf Leser. "Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSP." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77066-1_2.

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Raj, Jobin, and Govindan V.K. "Unsupervised Color Image Segmentation by Clustering into Multivariate Gaussians." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22786-8_80.

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Lim, Meng-Hui, and Andrew Beng Jin Teoh. "Non-user-Specific Multivariate Biometric Discretization with Medoid-Based Segmentation." In Biometric Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_35.

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Gruchalla, Kenny, Mark Rast, Elizabeth Bradley, and Pablo Mininni. "Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions." In Advances in Visual Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_57.

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Zhuang, Xiahai. "Multivariate Mixture Model for Cardiac Segmentation from Multi-Sequence MRI." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46723-8_67.

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Harańczyk, Grzegorz. "Change Points Detection in Multivariate Signal Applied to Human Activity Segmentation." In Advanced Analytics and Learning on Temporal Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_2.

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Imani, Shima, and Harsh Shrivastava. "tGLAD: A Sparse Graph Recovery Based Approach for Multivariate Time Series Segmentation." In Advanced Analytics and Learning on Temporal Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_12.

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Conference papers on the topic "Segmentation multivariée"

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Fomo, Desmond, and Aki-Hiro Sato. "Enhancing Big Data Analysis: A Recursive Window Segmentation Strategy for Multivariate Longitudinal Data." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825192.

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Velasco-Forero, Santiago, Maider Marin-McGee, and Miguel Velez-Reyes. "Multivariate diffusion tensor and induced segmentation." In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2013. http://dx.doi.org/10.1109/whispers.2013.8080638.

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Frecon, Jordan, Nelly Pustelnik, Herwig Wendt, and Patrice Abry. "Multivariate optimization for multifractal-based texture segmentation." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351750.

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Leeuwen, Frederique van. "Utilizing Multivariate Time Series for Semantic Segmentation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006112.

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Colucci, Domenico, José Manuel Prats-Montalban, Davide Fissore, and Alberto Ferrer Riquelme. "Application of Multivariate Image Analysis to Thermal Images for in line Monitoring of the Freeze-Drying Process." In 21st International Drying Symposium. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7558.

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A new Process Analytical Technology (PAT) has been developed and tested for on-line process monitoring of a vacuum freeze-drying process. The sensor uses an infrared camera to obtain thermal images of the ongoing process and multivariate image analysis (MIA) to extract the information after automatic detection and segmentation of the region corresponding to the product in every vial. A reference model was built, using the information of six batches, and different kind of anomalous events, involving either single vials in the batch or the whole batch, were simulated to test the capacity of the
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Zhang, Hui-Juan, and Jia-Cheng Huang. "A segmentation technology for multivariate contextual time series." In 2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2017. http://dx.doi.org/10.1109/iscmi.2017.8279600.

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Lei, Tianhu, and Jayaram K. Udupa. "Multivariate segmentation of fMRI for human brain mapping." In Medical Imaging 2000, edited by Chin-Tu Chen and Anne V. Clough. SPIE, 2000. http://dx.doi.org/10.1117/12.383410.

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Alexandra Constantin, A., B. Ruzena Bajcsy, and C. Sarah Nelson. "Unsupervised segmentation of brain tissue in multivariate MRI." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490406.

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Yang, Zhi, Pengfei Li, Yanxiang Bao, and Xiao Huang. "Speeding Up Multivariate Time Series Segmentation Using Feature Extraction." In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2020. http://dx.doi.org/10.1109/itnec48623.2020.9085218.

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Li, Zhengxin, Jia Liu, and Xiaofeng Zhang. "Similarity Measure of Multivariate Time Series Based on Segmentation." In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. ACM, 2020. http://dx.doi.org/10.1145/3383972.3384071.

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