Relevant bibliographies by topics / Multi-scale pattern clustering

Academic literature on the topic 'Multi-scale pattern clustering'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Multi-scale pattern clustering"

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Araabi, Babak Nadjar, and Nasser Kehtarnavaz. "Hough Array Processing via Fast Multi-Scale Clustering." Real-Time Imaging 6, no. 2 (April 2000): 129–41. http://dx.doi.org/10.1006/rtim.1999.0181.

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Nakamura, Eiji, and Nasser Kehtarnavaz. "Determining number of clusters and prototype locations via multi-scale clustering." Pattern Recognition Letters 19, no. 14 (December 1998): 1265–83. http://dx.doi.org/10.1016/s0167-8655(98)00099-3.

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Wen, 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 (January 2022): 102304. http://dx.doi.org/10.1016/j.media.2021.102304.

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Yadav, Dhirendra Prasad, Kamal Kishore, Ashish Gaur, Ankit Kumar, Kamred Udham Singh, Teekam Singh, and Chetan Swarup. "A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection." Sustainability 14, no. 23 (December 4, 2022): 16179. http://dx.doi.org/10.3390/su142316179.

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Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LBP can improve the performance of the CNN (Convolution Neural Network). The achieved performance of the model can be used to develop a real-time crack detection system.
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Tan, Jingang, Lili Chen, Kangru Wang, Jiamao Li, and Xiaolin Zhang. "SASO: Joint 3D semantic‐instance segmentation via multi‐scale semantic association and salient point clustering optimization." IET Computer Vision 15, no. 5 (April 9, 2021): 366–79. http://dx.doi.org/10.1049/cvi2.12033.

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GABRIEL, E., D. J. WILSON, A. J. H. LEATHERBARROW, J. CHEESBROUGH, S. GEE, E. BOLTON, A. FOX, P. FEARNHEAD, C. A. HART, and P. J. DIGGLE. "Spatio-temporal epidemiology of Campylobacter jejuni enteritis, in an area of Northwest England, 2000–2002." Epidemiology and Infection 138, no. 10 (March 5, 2010): 1384–90. http://dx.doi.org/10.1017/s0950268810000488.

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SUMMARYA total of 969 isolates of Campylobacter jejuni originating in the Preston, Lancashire postcode district over a 3-year period were characterized using multi-locus sequence typing. Recently developed statistical methods and a genetic model were used to investigate temporal, spatial, spatio-temporal and genetic variation in human C. jejuni infections. The analysis of the data showed statistically significant seasonal variation, spatial clustering, small-scale spatio-temporal clustering and spatio-temporal interaction in the overall pattern of incidence, and spatial segregation in cases classified according to their most likely species-of-origin.
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Guo, Yishan, and Mandan Liu. "Spatial-temporal trajectory anomaly detection based on an improved spectral clustering algorithm." Intelligent Data Analysis 27, no. 1 (January 30, 2023): 31–58. http://dx.doi.org/10.3233/ida-216185.

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With the development of wireless communication technology, when users use wireless networks to meet various needs, wireless networks also record a large number of users’ spatial-temporal trajectory data. In order to better pay attention to the healthy development of students and promote the information construction on campus, a spectral clustering algorithm based on the multi-scale threshold and density combined with shared nearest neighbors (MSTDSNN-SC) is proposed. Firstly, it improves the affinity distance function based on the shortest time dis-tance-shortest time distance sub-sequence (STD-STDSS) by adding location popularity and uses this model to construct the initial adjacency matrix. Then it introduces the covariance scale threshold and spatial scale threshold to perform 0–1 processing on the adjacency matrix to obtain more accurate sample similarity. Next, it constructs an eigenvector space by eigenvalue decom-position of the adjacency matrix. Finally, it uses DBSCAN clustering algorithm with shared nearest neighbors to avoid to manually determine the number of clusters. Taking Internet usage data on campus as an example, multiple clustering algorithms are used for anomaly detection and four evaluation metrics are applied to estimate the clustering results. MSTDSNN-SC algorithm reflects better clustering performance. Furthermore, the abnormal trajectories list is verified to be effective and credible.
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He, Yueshun, Wei Zhang, Ping Du, and 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, no. 08 (November 14, 2019): 2054019. http://dx.doi.org/10.1142/s0218001420540191.

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Due to complicated data structure, image can present rich information, and so images are applied widely at different fields. Although the image can offer a lot of convenience, handling such data consume much time and multi-dimensional space. Especially when users need to retrieve some images from larger-scale image datasets, the disadvantage is more obvious. So, in order to retrieve larger-scale image data effectively, a scene images retrieval strategy based on the MapReduce parallel programming model is proposed. The proposed strategy first, investigates how to effectively store large-scale scene images under a Hadoop cluster parallel processing architecture. Second, a distributed feature clustering algorithm MeanShift is introduced to implement the clustering process of emotional feature of scene images. Finally, several experiments are conducted to verify the effectiveness and efficiency of the proposed strategy in terms of different aspects such as retrieval accuracy, speedup ratio and efficiency and data scalability.
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Bak, Ji Hyun, Min Hyeok Kim, Lei Liu, and Changbong Hyeon. "A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C." PLOS Computational Biology 17, no. 3 (March 16, 2021): e1008834. http://dx.doi.org/10.1371/journal.pcbi.1008834.

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Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.
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Bossennec, Claire, Matthis Frey, Lukas Seib, Kristian Bär, and Ingo Sass. "Multiscale Characterisation of Fracture Patterns of a Crystalline Reservoir Analogue." Geosciences 11, no. 9 (September 3, 2021): 371. http://dx.doi.org/10.3390/geosciences11090371.

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For an accurate multiscale property modelling of fractured crystalline geothermal reservoirs, an enhanced characterisation of the geometrical features and variability of the fracture network properties is an essential prerequisite. Combining regional digital elevation model analysis and local outcrop investigation, the study comprises the characterisation of the fracture pattern of a crystalline reservoir analogue in the Northern Odenwald, with LiDAR and GIS structural interpretation. This approach provides insights into the 3D architecture of the fault and fracture network, its clustering, and its connectivity. Mapped discontinuities show a homogeneous length distribution, which follows a power law with a −2.03 scaling factor. The connectivity of the fracture network is heterogenous, due to a fault control at the hectometric scale. Clustering is marked by long sub-vertical fractures at the outcrop scale, and strongly enhance heterogeneity around weathered fracture and fault corridors. The multi-variable dataset created within this study can be used as input data for accurate discrete fracture networks and fluid-flow modelling of reservoirs of similar type.
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Dissertations / Theses on the topic "Multi-scale pattern clustering"

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Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods." University of Sydney, 2008. http://hdl.handle.net/2123/3558.

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Doctor of Philosophy
This 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.
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Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods." Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/3558.

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This 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.
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Legrand, 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.

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Dans le domaine de la biologie développementale, un des principaux défis est de comprendre comment des tissus multicellulaires, à l'origine indifférenciés, peuvent engendrer des formes aussi complexes que celles d'une fleur. De part son implication dans l'organogenèse florale, l'auxine est une phytohormone majeure. Nous avons donc déterminé son réseau binaire potentiel, puis y avons appliqué des modèles de clustering de graphes s'appuyant sur les profils de connexion présentés par ces 52 facteurs de transcription (FT). Nous avons ainsi pu identifier trois groupes, proches des groupes biologiques putatifs: les facteurs de réponse à l'auxine activateurs (ARF+), répresseurs (ARF-) et les Aux/IAAs. Nous avons détecté l'auto-interaction des ARF+ et des Aux/IAA, ainsi que leur interaction, alors que les ARF- en présentent un nombre restreint. Ainsi, nous proposons un mode de compétition auxine indépendent entre ARF+ et ARF- pour la régulation transcriptionelle. Deuxièmement, nous avons modélisé l'influence des séquences de dimérisation des FT sur la structure de l'interactome en utilisant des modèles de mélange Gaussien pour graphes aléatoires. Les groupes obtenus sont proches des précédents, et les paramètres estimés nous on conduit à conclure que chaque sous-domaine peut jouer un rôle différent en fonction de leur proximité phylogénétique.Enfin, nous sommes passés à l'échelle multi-cellulaire ou, par un graphe spatio-temporel, nous avons modélisé les premiers stades du développement floral d'A. thaliana. Nous avons pu extraire des caractéristiques cellulaires (3D+t) de reconstruction d'imagerie confocale, et avons démontré la possibilité de caractériser l'identité cellulaire en utilisant des méthodes de classification hiérarchique et des arbres de Markov cachés
A 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
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Conference papers on the topic "Multi-scale pattern clustering"

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Dang, Zhiyuan, Cheng Deng, Xu Yang, and Heng Huang. "Multi-Scale Fusion Subspace Clustering Using Similarity Constraint." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00669.

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Diédié, Gokou Hervé Fabrice, Koigny Fabrice Kouassi, and Tchimou N’Takpé. "Multi-Sink Convergecast Protocol for Large Scale Wireless Sensor Networks." In 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.

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Wireless sensor nodes are designed to collect information about their immediate environment. Once gathered, such data are forwarded via a multi-hop communication pattern to a remote gateway, also known as the sink. This process referred to as the convergecast may often require several sinks in order to improve network efficiency and resilience. Provided that load among the latter nodes are well balanced and packet losses are mitigated. This paper aims to design such a protocol by combining clustering, path-vector routing and sinks’ duty cycle scheduling schemes to help balance load and minimize message overhead. Simulation results proved that this solution outperforms DMS-RP (Dynamic Multi-Sink Routing Protocol), a recent state-ofthe-art contribution, in terms of delay minimization, packet delivery and network lifetime enhancement.
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Berghauser Pont, Meta, and Jesper Olsson. "Typology based on three density variables central to Spacematrix using cluster analysis." In 24th ISUF 2017 - City and Territory in the Globalization Age. Valencia: Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/isuf2017.2017.5319.

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Since the publication of the book ‘Spacematrix. Space, density and urban form’ (Berghauser Pont and Haupt, 2010), the Spacematrix method has been linked back to its theoretical foundations by Steadman (2013), is further developed using the measure of accessible density to arrive at a density measure that more closely relates to the environment as experienced by people moving through the city (Berghauser Pont and Marcus, 2014) which then is used to arrive at a multi-scalar density typology (Berghauser Pont et al. 2017). This paper will take yet another step in the development of the Spacematrix method by including the measure of network density in the classification which until now was not used to its full potential. Important for successful classification is the ability to ascertain the fundamental characteristics on which the classification is to be based where the work of Berghauser Pont and Haupt (2010) will be followed addressing three key variables: Floor Space Index (FSI), Ground Space Index (GSI) and Network density (N) where especially the last was not fully included in the earlier work. Besides a typology based on these three variables, this paper will also result in a robust statistical method that can later be used on larger samples for city-scale comparisons. Two statistical methods are tested: hierarchical clustering and centroid-based clustering and besides a general discussion about their strong and weak points, the paper shows that the hierarchical method is more convincing in distinguishing differences in both building type and street pattern that is especially captured with Network density (N). As this method is not useful for large datasets we propose a combination of the two clustering methods as the way forward.
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Ma, L., L. P. Lu, and L. Zhu. "Unsupervised texture segmentation based on multi-scale local binary patterns and FCMs clustering." In IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20061470.

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Gusev, Sergey Igorevich, Elena Sergeyevna Kolbikova, Olga Igorevna Malinovskaya, Azat Fanisovich Garaev, and 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." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206520-ms.

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Abstract The Kharyaginskoye oil field is located on the territory of the Nenets Autonomous District and belongs to the Timan-Pechora Basin oil and gas province. The main object of development is a Devonian age carbonate reservoir. The productive zones of the studied object are mainly confined to thin bed low-porosity reservoirs with a complex structure of void space. The high heterogeneity of deposits laterally and the presence of different levels of oil-water contact (OWC) in the marginal isolated zones necessitate a more accurate assessment of the oil-saturated effective thicknesses. The increase in the reliability of the interpretation was achieved by the joint analysis of borehole and seismic studies using Machine Learning methods. At the stage of configuring the facies model based on well logs and core data, a Multi-Resolution Graph-based Clustering MRGC was used, which provides effective integration of geological and geophysical information. The multi-dimensional dot-pattern recognition method based on k-Nearest neighbors algorithm (k-NN), and by combining various criteria, it allows solving the problem of non-linearity of the relationships between logging responses and the corresponding lithology. The algorithm of the democratic association of neural networks DNNA was used to propagate electrofacies in the inter-well space. The method optimizes the use of seismic data before summation and after summation together with well data through a controlled process that provides a calibrated and scaled distribution of facies. The most probable facies distribution can be used directly as a property in reservoir modeling or as a constraint for modeling. It is known that there is no direct connection between a certain type of wave pattern and the lithological composition of rocks, therefore, the analysis of changing reflection characteristics is performed in conjunction with geophysical data, such as well logging. In addition, a priori geological information about the work area is involved. An important condition for the effective application of facies analysis is the presence of representative core material and the availability of high-quality well information. At the first stage of the work, the lithotyping of carbonate deposits was performed according to the macro description of the core, based on the classification of limestones according to R. H. Dunham. Then, using the multidimensional statistical recognition algorithm MRGC, the relationships between the selected lithotypes and logging responses were obtained. As a result of the tuning, a cluster model was obtained that allows us to distinguish electrofacies characterized by an increased filtration and capacitance potential. At the second stage, the obtained electrofacies, considering the nature of saturation, were used to train cubes of seismic attributes and calculate the cubes of lithofacies and the probability of the existence of each lithofacies. The key point in the distribution was the use of electrofacies obtained in wells belonging to different facies zones. Thus, the joint analysis of all available borehole and seismic information by machine learning methods made it possible to make a forecast lithofacies considering the type of saturation based on geological and geophysical information analysis. The effectiveness of the presented technologies was demonstrated by analyzing the properties of low-permeable carbonate reservoirs, where classical attributes and inversion demonstrate limitations in describing a heterogeneous saturation model. The use of neural network approaches allows to configure complex nonlinear dependencies that are not available to classical methods. The use of a small volume of multi-scale geological and geophysical information using Machine Learning algorithms in the field of field-geophysical and seismic interpretation makes it possible to increase the reliability of interpretation and clarify the location of prospective zones with improved reservoir properties on the studied area, as well as to minimize geological risks during subsequent well placement.
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