Literatura académica sobre el tema "Multi-scale pattern clustering"
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Artículos de revistas sobre el tema "Multi-scale pattern clustering"
Araabi, Babak Nadjar y Nasser Kehtarnavaz. "Hough Array Processing via Fast Multi-Scale Clustering". Real-Time Imaging 6, n.º 2 (abril de 2000): 129–41. http://dx.doi.org/10.1006/rtim.1999.0181.
Texto completoNakamura, Eiji y Nasser Kehtarnavaz. "Determining number of clusters and prototype locations via multi-scale clustering". Pattern Recognition Letters 19, n.º 14 (diciembre de 1998): 1265–83. http://dx.doi.org/10.1016/s0167-8655(98)00099-3.
Texto completoWen, Junhao, Erdem Varol, Aristeidis Sotiras, Zhijian Yang, Ganesh B. Chand, Guray Erus, Haochang Shou et al. "Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes". Medical Image Analysis 75 (enero de 2022): 102304. http://dx.doi.org/10.1016/j.media.2021.102304.
Texto completoYadav, Dhirendra Prasad, Kamal Kishore, Ashish Gaur, Ankit Kumar, Kamred Udham Singh, Teekam Singh y Chetan Swarup. "A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection". Sustainability 14, n.º 23 (4 de diciembre de 2022): 16179. http://dx.doi.org/10.3390/su142316179.
Texto completoTan, Jingang, Lili Chen, Kangru Wang, Jiamao Li y Xiaolin Zhang. "SASO: Joint 3D semantic‐instance segmentation via multi‐scale semantic association and salient point clustering optimization". IET Computer Vision 15, n.º 5 (9 de abril de 2021): 366–79. http://dx.doi.org/10.1049/cvi2.12033.
Texto completoGABRIEL, E., D. J. WILSON, A. J. H. LEATHERBARROW, J. CHEESBROUGH, S. GEE, E. BOLTON, A. FOX, P. FEARNHEAD, C. A. HART y P. J. DIGGLE. "Spatio-temporal epidemiology of Campylobacter jejuni enteritis, in an area of Northwest England, 2000–2002". Epidemiology and Infection 138, n.º 10 (5 de marzo de 2010): 1384–90. http://dx.doi.org/10.1017/s0950268810000488.
Texto completoGuo, Yishan y Mandan Liu. "Spatial-temporal trajectory anomaly detection based on an improved spectral clustering algorithm". Intelligent Data Analysis 27, n.º 1 (30 de enero de 2023): 31–58. http://dx.doi.org/10.3233/ida-216185.
Texto completoHe, Yueshun, Wei Zhang, Ping Du y Qiaohe Yang. "A Novel Strategy for Retrieving Large Scale Scene Images Based on Emotional Feature Clustering". International Journal of Pattern Recognition and Artificial Intelligence 34, n.º 08 (14 de noviembre de 2019): 2054019. http://dx.doi.org/10.1142/s0218001420540191.
Texto completoBak, Ji Hyun, Min Hyeok Kim, Lei Liu y Changbong Hyeon. "A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C". PLOS Computational Biology 17, n.º 3 (16 de marzo de 2021): e1008834. http://dx.doi.org/10.1371/journal.pcbi.1008834.
Texto completoBossennec, Claire, Matthis Frey, Lukas Seib, Kristian Bär y Ingo Sass. "Multiscale Characterisation of Fracture Patterns of a Crystalline Reservoir Analogue". Geosciences 11, n.º 9 (3 de septiembre de 2021): 371. http://dx.doi.org/10.3390/geosciences11090371.
Texto completoTesis sobre el tema "Multi-scale pattern clustering"
Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods". University of Sydney, 2008. http://hdl.handle.net/2123/3558.
Texto completoThis thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features of a section of time series. This feature can remove noise from the time series by statistical probability. It is experimentally proven that this feature is successful for pattern repeated time series. Second, a multiscale Gaussian gravity as a pattern relationship measurement which can describe the direction of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that unsupervised pattern classification can be transformed into multiscale supervised pattern classification by multiscale supervisory time series or multiscale filtered time series is presented. The second part of this research focuses on multivariate time series analysis using pattern recognition. A systematic method is proposed to find the independent variables of a group of share prices by time series clustering, principal component analysis, independent component analysis, and object recognition. The number of dependent variables is reduced and the multivariate time series analysis is simplified by time series clustering and principal component analysis. Independent component analysis aims to find the ideal independent variables of the group of shares. Object recognition is expected to recognize those independent variables which are similar to the independent components. This method provides a new clue to understanding the stock market and to modelling a large time series database.
Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods". Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/3558.
Texto completoLegrand, Jonathan. "Toward a multi-scale understanding of flower development - from auxin networks to dynamic cellular patterns". Thesis, Lyon, École normale supérieure, 2014. http://www.theses.fr/2014ENSL0947/document.
Texto completoA striking aspect of flowering plants is that, although they seem to display a great diversity of size and shape, they are made of the same basics constituents, that is the cells. The major challenge is then to understand how multicellular tissues, originally undifferentiated, can give rise to such complex shapes. We first investigated the uncharacterised signalling network of auxin since it is a major phytohormone involved in flower organogenesis.We started by determining the potential binary network, then applied model-based graph clustering methods relying on connectivity profiles. We demonstrated that it could be summarise in three groups, closely related to putative biological groups. The characterisation of the network function was made using ordinary differential equation modelling, which was later confirmed by experimental observations.In a second time, we modelled the influence of the protein dimerisation sequences on the auxin interactome structure using mixture of linear models for random graphs. This model lead us to conclude that these groups behave differently, depending on their dimerisation sequence similarities, and that each dimerisation domains might play different roles.Finally, we changed scale to represent the observed early stages of A. thaliana flower development as a spatio-temporal property graph. Using recent improvements in imaging techniques, we could extract 3D+t cellular features, and demonstrated the possibility of identifying and characterising cellular identity on this basis. In that respect, hierarchical clustering methods and hidden Markov tree have proven successful in grouping cell depending on their feature similarities
Actas de conferencias sobre el tema "Multi-scale pattern clustering"
Dang, Zhiyuan, Cheng Deng, Xu Yang y Heng Huang. "Multi-Scale Fusion Subspace Clustering Using Similarity Constraint". En 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00669.
Texto completoDiédié, Gokou Hervé Fabrice, Koigny Fabrice Kouassi y Tchimou N’Takpé. "Multi-Sink Convergecast Protocol for Large Scale Wireless Sensor Networks". En 8th International Conference on Signal, Image Processing and Embedded Systems (SIGEM 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122016.
Texto completoBerghauser Pont, Meta y Jesper Olsson. "Typology based on three density variables central to Spacematrix using cluster analysis". En 24th ISUF 2017 - City and Territory in the Globalization Age. Valencia: Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/isuf2017.2017.5319.
Texto completoMa, L., L. P. Lu y L. Zhu. "Unsupervised texture segmentation based on multi-scale local binary patterns and FCMs clustering". En IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20061470.
Texto completoGusev, Sergey Igorevich, Elena Sergeyevna Kolbikova, Olga Igorevna Malinovskaya, Azat Fanisovich Garaev y Robert Kamilevich Valiev. "Forecast of Prospective Oil Saturation Zones in the Devonian Carbonate Deposits of the Kharyaginsky Field Based on Geological and Geophysical Information Analysis by Using Machine Learning Methods". En SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206520-ms.
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