Letteratura scientifica selezionata sul tema "Multivariate segmentation"
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Articoli di riviste sul tema "Multivariate segmentation":
Klose, J. "Binary Segmentation for Multivariate Polynomials". Journal of Complexity 11, n. 3 (settembre 1995): 330–43. http://dx.doi.org/10.1006/jcom.1995.1015.
Esteban, Oscar, Gert Wollny, Subrahmanyam Gorthi, María-J. Ledesma-Carbayo, Jean-Philippe Thiran, Andrés Santos e Meritxell Bach-Cuadra. "MBIS: Multivariate Bayesian Image Segmentation tool". Computer Methods and Programs in Biomedicine 115, n. 2 (luglio 2014): 76–94. http://dx.doi.org/10.1016/j.cmpb.2014.03.003.
Portillo-García, J., I. Trueba-Santander, G. de Miguel-Vela e C. Alberola-López. "Efficient multispectral texture segmentation using multivariate statistics". IEE Proceedings - Vision, Image, and Signal Processing 145, n. 5 (1998): 357. http://dx.doi.org/10.1049/ip-vis:19982315.
Lim, Hyunki, Heeseung Choi, Yeji Choi e Ig-Jae Kim. "Memetic algorithm for multivariate time-series segmentation". Pattern Recognition Letters 138 (ottobre 2020): 60–67. http://dx.doi.org/10.1016/j.patrec.2020.06.022.
Hallac, David, Peter Nystrup e Stephen Boyd. "Greedy Gaussian segmentation of multivariate time series". Advances in Data Analysis and Classification 13, n. 3 (22 agosto 2018): 727–51. http://dx.doi.org/10.1007/s11634-018-0335-0.
Sudbury, Lynn, e Peter Simcock. "A multivariate segmentation model of senior consumers". Journal of Consumer Marketing 26, n. 4 (26 giugno 2009): 251–62. http://dx.doi.org/10.1108/07363760910965855.
Omranian, Nooshin, Sebastian Klie, Bernd Mueller-Roeber e Zoran Nikoloski. "Network-Based Segmentation of Biological Multivariate Time Series". PLoS ONE 8, n. 5 (7 maggio 2013): e62974. http://dx.doi.org/10.1371/journal.pone.0062974.
Ip, Barry, e Gabriel Jacobs. "Segmentation of the games market using multivariate analysis". Journal of Targeting, Measurement and Analysis for Marketing 13, n. 3 (aprile 2005): 275–87. http://dx.doi.org/10.1057/palgrave.jt.5740154.
Noordam, J. C., W. H. A. M. van den Broek e L. M. C. Buydens. "Unsupervised segmentation of predefined shapes in multivariate images". Journal of Chemometrics 17, n. 4 (2003): 216–24. http://dx.doi.org/10.1002/cem.794.
Laksono, Bagaskoro Cahyo, e 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, n. 1 (4 gennaio 2022): 507–18. http://dx.doi.org/10.34123/icdsos.v2021i1.161.
Tesi sul tema "Multivariate segmentation":
Rzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /". Online version of thesis, 1994. http://hdl.handle.net/1850/12200.
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.
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.
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 protein spots in a gel-image. This has generally been the natural starting point of all methods and procedures developed since the introduction of 2D-gels in the mid-seventies, simply because this best reproduces the results created by a human analyst, who manually identify protein-spot entities. The amount of data produced in a 2D-gel experiment can be quite large, especially in multiple gels where the human analyst is dependent on additional statistical data-analytical tools to produce results. Because of the correlated nature of most gel-data, analysis by multivariate methods is natural choice, and are therefore adopted in this thesis. The goal of this thesis is to introduce the above mentioned procedures at different stages in the analysis pipeline where they are not yet fully exploited, rather than to improve already existing algorithms. In this way new insight and ideas on how to handle data from 2D-gel experiments are achieved. The thesis starts with a review of segmentation methodology, and introduces a selected procedure used to identify protein spots throughout. Output from the segmentation is then used to create a multivariate spot-filtering model, which aims to separate protein spots from noise and artefacts often creating problems in 2D-gel analysis. Lately the use of common spot boundaries in multiple gels have been the method of choice when gels are analysed. How such boundaries should be defined is an important subject of discussion, and thus a new method for defining common boundaries based on the individual segmentation of each gel is introduced. Segmentation may be a natural starting point when gels are analysed, but it is not necessarily the most correct. Often the introduction of fixed spot entities introduces restrictions to the data which cause problems at later stages in the analysis. Analysing pixels from multiple gels directly has no such restrictions, and it is shown in this thesis that the output of such an analysis based on multivariate methods can produce very useful results. It can also give insight to the data problematic to achieve with the spot boundary approach. At last in the thesis an improved pixel-based approach is introduced, where a less restricted segmentation is used to reduce and concentrate the amount of data analysed, improving the final output.
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.
Hosseini-Chaleshtari, Jamshid. "Segment Congruence Analysis: An Information Theoretic Approach". PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/797.
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.
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.
Hierarchical image representations are widely used in image processing to model the content of an image in the multi-scale structure. A well-known hierarchical representation is the tree of shapes (ToS) which encodes the inclusion relationship between connected components from different thresholded levels. This kind of tree is self-dual, contrast-change invariant and popular in computer vision community. Typically, in our work, we use this representation to compute the new distance which belongs to the mathematical morphology domain. Distance transforms and the saliency maps they induce are generally used in image processing, computer vision, and pattern recognition. One of the most commonly used distance transforms is the geodesic one. Unfortunately, this distance does not always achieve satisfying results on noisy or blurred images. Recently, a new pseudo-distance, called the minimum barrier distance (MBD), more robust to pixel fluctuation, has been introduced. Some years after, Géraud et al. have proposed a good and fast-to-compute approximation of this distance: the Dahu pseudodistance. Since this distance was initially developed for grayscale images, we propose here an extension of this transform to multivariate images; we call it vectorial Dahu pseudo-distance. This new distance is easily and efficiently computed thanks to the multivariate tree of shapes (MToS). We propose an efficient way to compute this distance and its deduced saliency map in this thesis. We also investigate the properties of this distance in dealing with noise and blur in the image. This distance has been proved to be robust for pixel invariant. To validate this new distance, we provide benchmarks demonstrating how the vectorial Dahu pseudo-distance is more robust and competitive compared to other MB-based distances. This distance is promising for salient object detection, shortest path finding, and object segmentation. Moreover, we apply this distance to detect the document in videos. Our method is a region-based approach which relies on visual saliency deduced from the Dahu pseudo-distance. We show that the performance of our method is competitive with state-of-the-art methods on the ICDAR Smartdoc 2015 Competition dataset
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.
The exponential growth of the Internet requires regular monitoring of network metrics. This thesis focuses on round-trip delays and the possibility of addressing the problems of missing data and multivariate segmentation. The first contribution includes the orchestration of delay measurement campaigns, as well as the development of a simulator that generates end-to-end delay traces. The second contribution of this thesis is the introduction of two missing data completion methods. The first is based on non-negative matrix factorization, while the second uses collaborative neural filtering. Tested on synthetic and real data, these methods demonstrate their efficiency and accuracy. The third contribution of this thesis involves multivariate delay segmentation. This approach is based on hierarchical clustering and is implemented in two stages. Firstly, the delay time series are grouped to obtain, within the same group, series with similar and synchronous variations and trends. Next, the multivariate segmentation step collectively and jointly segments the series within each group. This step uses hierarchical clustering followed by post-processing using the Viterbi algorithm to smooth the segmentation result. This method was tested on real delay traces from two major events affecting two Internet Exchange Points (IXPs). The results show that this method approximates the state-of-the-art in segmentation, while significantly reducing computing speed and costs
Motta, Sergio Luis Stirbolov. "Estudo sobre segmentação de mercado consumidor por atitude e atributos ecológicos de produtos". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-30062009-161308/.
This study intended to verify if the variable attitude in conjunction with the consumer good´s ecologically characteristics may be used as market segmentation´s basis. To satisfy this proposition, we tried, at first, to know all the available theory about the topics that are related to and also the basis to the field research. It was a quantitative and descriptive one, with a field study method. A non-probabilistic sample of students and teachers was used to explain their opinions by self-administration of a strucured and disguised questionnaire. The data analysis ocurred by the application of three multivariate techniques: Factor Analysis, Cluster Analysis and Correspondence Analysis. The first of them was successfull, whereas it was possible to reduce the set of variables to two factors; the fatorial scores performed as inputs to the Cluster Analysis. This technique was successful too, because the majority of simulations combining similarity measures and aglomeration methods engendered clusters, which permitted an answer favorable to the research problem; one of the combinations Euclidean Square Distance and Withinn Groups was considered the most satisfactory and used as basis to the next technique, the Correspondence Analysis. It was applied to profile the clusters and give a relevance to this paper; it was partly successful, as we couldnt use some variables and it was replaced by Cross Tabulation. The final considerations confirmed the researchers expectation as regard to the possibility of obtain clusters using at the same time the variable attitude and good´s ecologically characteristics.
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.
Libri sul tema "Multivariate segmentation":
Kleinbaum, Robert M. Multivariate time series forecasts of market share. Cambridge, Mass: Marketing Science Institute, 1988.
Kleinbaum, Robert M. Multivariate time series forecasts of market share. Cambridge, MA: Marketing Science Institute, 1988.
Capitoli di libri sul tema "Multivariate segmentation":
Hanselmann, Michael, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren e Fred A. Hamprecht. "Multivariate Watershed Segmentation of Compositional Data". In Discrete Geometry for Computer Imagery, 180–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04397-0_16.
Maya, Shigeru, Akihiro Yamaguchi, Kaneharu Nishino e Ken Ueno. "Lag-Aware Multivariate Time-Series Segmentation". In Proceedings of the 2020 SIAM International Conference on Data Mining, 622–30. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.70.
Küppers, Fabian, Anselm Haselhoff, Jan Kronenberger e Jonas Schneider. "Confidence Calibration for Object Detection and Segmentation". In Deep Neural Networks and Data for Automated Driving, 225–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_8.
Raj, Jobin, e Govindan V.K. "Unsupervised Color Image Segmentation by Clustering into Multivariate Gaussians". In Communications in Computer and Information Science, 639–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22786-8_80.
Lim, Meng-Hui, e Andrew Beng Jin Teoh. "Non-user-Specific Multivariate Biometric Discretization with Medoid-Based Segmentation". In Biometric Recognition, 279–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_35.
Gruchalla, Kenny, Mark Rast, Elizabeth Bradley e Pablo Mininni. "Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions". In Advances in Visual Computing, 619–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_57.
Zhuang, Xiahai. "Multivariate Mixture Model for Cardiac Segmentation from Multi-Sequence MRI". In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 581–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46723-8_67.
Harańczyk, Grzegorz. "Change Points Detection in Multivariate Signal Applied to Human Activity Segmentation". In Advanced Analytics and Learning on Temporal Data, 14–24. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_2.
Imani, Shima, e Harsh Shrivastava. "tGLAD: A Sparse Graph Recovery Based Approach for Multivariate Time Series Segmentation". In Advanced Analytics and Learning on Temporal Data, 176–89. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_12.
Ceré, Raphaël, e François Bavaud. "Soft Image Segmentation: On the Clustering of Irregular, Weighted, Multivariate Marked Networks". In Communications in Computer and Information Science, 85–109. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-06010-7_6.
Atti di convegni sul tema "Multivariate segmentation":
Velasco-Forero, Santiago, Maider Marin-McGee e 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.
Frecon, Jordan, Nelly Pustelnik, Herwig Wendt e 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.
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.
Zhang, Hui-Juan, e 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.
Lei, Tianhu, e Jayaram K. Udupa. "Multivariate segmentation of fMRI for human brain mapping". In Medical Imaging 2000, a cura di Chin-Tu Chen e Anne V. Clough. SPIE, 2000. http://dx.doi.org/10.1117/12.383410.
Alexandra Constantin, A., B. Ruzena Bajcsy e 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.
Yang, Zhi, Pengfei Li, Yanxiang Bao e 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.
Li, Zhengxin, Jia Liu e Xiaofeng Zhang. "Similarity Measure of Multivariate Time Series Based on Segmentation". In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383972.3384071.
Derksen, Harm, Yi Ma, Wei Hong e John Wright. "Segmentation of multivariate mixed data via lossy coding and compression". In Electronic Imaging 2007, a cura di Chang Wen Chen, Dan Schonfeld e Jiebo Luo. SPIE, 2007. http://dx.doi.org/10.1117/12.714912.
Lourenco, Bernardo, Vitor Santos, Miguel Oliveira e Tiago Almeida. "Performance Analysis on Deep Learning Semantic Segmentation with multivariate Training Procedures". In 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2020. http://dx.doi.org/10.1109/icarsc49921.2020.9096145.