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Strahler based graph clustering using convolution

D. Auber, M. Delest, Y. Chiricota
Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004.  
The metric used here is defined from Strahler numbers which measure the "ramification" level of graph vertices.  ...  The density function is then filtered using a convolution, leading to a partition of the graph.  ...  To this end, we used the metric, based on Strahler number calculations.  ... 
doi:10.1109/iv.2004.1320123 dblp:conf/iv/AuberDC04 fatcat:co6fpcdhgrdzjmbrovcgwghc4e

DAG-based visual interfaces for navigation in indexed video content

Maylis Delest, Anthony Don, Jenny Benois-Pineau
2006 Multimedia tools and applications  
This spatio-temporal color signature serves as a basis for graph clustering and graph visualization tools.  ...  The latter, united in a general huge graphs visualization tool, Tulip, supply an exciting graph-based navigation interface for multimedia content.  ...  After pre-filtering of the complete graph and computing Strahler numbers we have used a clustering method [4] based on convolution of Strahler metrics on the graphs.  ... 
doi:10.1007/s11042-006-0032-4 fatcat:xun5a6kkf5a2tmguttgzmg7d34

Structure-function relationships in the feto-placental circulation from in silico interpretation of micro-CT vascular structures

Monika Byrne, Rosalind Aughwane, Joanna James, Ciaran Hutchinson, Owen Arthurs, Neil Sebire, Sebastien Ourselin, Anna David, Andrew Melbourne, Alys Clark
2019 Placenta  
Vascular branching properties For each tree generated, vascular branching properties were calculated based on the Strahler ordering system (Fig. 1b ), including the Strahler branching ratio (the factor  ...  The meso-and micro-vasculature Beyond the identified graph, placental blood vessels were generated using a modified volume filling algorithm 8 .  ... 
doi:10.1016/j.placenta.2019.06.267 fatcat:snuzs6acj5as3naq7vpcm4d5ry

Autocorrelation and regularization in digital images. II. Simple image models

D.L.B. Jupp, A.H. Strahler, C.E. Woodcock
1989 IEEE Transactions on Geoscience and Remote Sensing  
Abstruct-The variogram function used in geostatistical analysis is a useful statistic in the analysis of remotely sensed images.  ...  In Part I1 of this paper, using the results derived in Part I, the basic second-order, or covariance, properties of scenes modeled by simple disks of varying size and spacing after imaging into disk-shaped  ...  This type of variance has been referred to as the "local variance" by Woodcock and Strahler [ 131. Let us now examine two cases.  ... 
doi:10.1109/36.17666 fatcat:5plmzuxkvzexfctrgbbvuzo4be

Automated and accurate segmentation of leaf venation networks via deep learning

Hao Xu, Benjamin Blonder, Miguel Jodra, Yadvinder Malhi, Mark Fricker
2020 New Phytologist  
Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks.  ...  However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi-scale statistics from subsequent network graph  ...  Acknowledgements We thank David Ford for assistance with the GPU cluster.  ... 
doi:10.1111/nph.16923 pmid:32964424 fatcat:j4qvubwhmfhqxgdsqng6xnp75e

Cholestasis-induced adaptive remodeling of interlobular bile ducts

Nachiket Vartak, Amruta Damle-Vartak, Beate Richter, Olaf Dirsch, Uta Dahmen, Seddik Hammad, Jan G. Hengstler
2016 Hepatology  
Bile duct ligation (BDL) is a frequently used model of cholestasis in rodents.  ...  Because curvature and tortuosity of the bile duct are unaltered, this enlargement of the biliary tree is caused by branching and not by convolution.  ...  Liver architecture staining was performed using anti-KRT19 and anti-DPP4 immunoglobulin G (IgG) based on modifications of methods described by Hammad et al.  ... 
doi:10.1002/hep.28373 pmid:26610202 pmcid:PMC5066759 fatcat:3nvh2elvazbcxliltf6p5vqvny

Exploring InfoVis Publication History with Tulip

M. Delest, T. Munzner, D. Auber, J.-P. Domenger
IEEE Symposium on Information Visualization  
Subgraphs of the full dataset can be created interactively or using a wide set of algorithms based on graph theory and combinatorics, including several kinds of clustering.  ...  We found that convolution clustering and small world clustering were particularly effective at showing the structure of the InfoVis publications dataset, as was coloring by the Strahler metric.  ...  In Task 3.2.3, we find the top authors according to Strahler metric using convolution clustering.  ... 
doi:10.1109/infvis.2004.23 dblp:conf/infovis/DelestMAD04 fatcat:7a2lxa6n7ra3vmpfkeahypbub4

Automated and accurate segmentation of leaf venation networks via deep learning [article]

Hao Xu, Benjamin Blonder, Miguel Jodra, Yadvinder Malhi, Mark Fricker
2020 bioRxiv   pre-print
Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks.  ...  However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi-scale statistics from subsequent network graph  ...  We also tested improved 358 Hessian-based enhancement techniques using Multiscale Fractional Anisotropy Tensors (MFAT), 359 in both their eigenvalue-based (MFATλ) and probability-based (MFATp) form (Alhasson  ... 
doi:10.1101/2020.07.19.206631 fatcat:slsa2rizcjdhnjyepfquk6inzq

Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images

Baoxuan Jin, Peng Ye, Xueying Zhang, Weiwei Song, Shihua Li
2019 Journal of the Indian Society of Remote Sensing  
combines object-oriented approach with deep convolutional neural network (COCNN) is presented.  ...  By comparing the COCNN method with the method based solely on CNN, precision and kappa index were selected to evaluate the classification accuracy of the two methods.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creative commons.org/licenses/by/4.0/), which permits unrestricted use, distribution  ... 
doi:10.1007/s12524-019-00945-3 fatcat:rgeken5ifbamhhbkgslg4xuxqe

Automated analysis of whole brain vasculature using machine learning [article]

Mihail Ivilinov Todorov, Johannes C. Paetzold, Oliver Schoppe, Giles Tetteh, Velizar Efremov, Katalin Voelgyi, Marco Duering, Martin Dichgans, Marie Piraud, Bjoern Menze, Ali Erturk
2019 bioRxiv   pre-print
Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation.  ...  Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP).  ...  More advanced metrics to describe the vasculature and networks, for example global Strahler values, network connectivity and local statistics on bifurcation angles and vascular shape can be extracted using  ... 
doi:10.1101/613257 fatcat:233qaac4mbf53h42x2vlrbs74u

New Seasonal Shift in In-Stream Diurnal Nitrate Cycles Identified by Mining High-Frequency Data

Alice H. Aubert, Lutz Breuer, Yiguo Hong
2016 PLoS ONE  
Our study is based on high-frequency data from an agricultural catchment (Studienlandschaft Schwingbachtal, Germany).  ...  We propose a novel approach, i.e. the combination of cluster analysis and Linear Discriminant Analysis, to mine from these data nitrate behavior patterns.  ...  Seven uncorrelated potential drivers (of 12) were used to predict the clusters based on a correlation matrix, and independence was verified (with the Variance Inflation Factor (which provides a measure  ... 
doi:10.1371/journal.pone.0153138 pmid:27073838 pmcid:PMC4830558 fatcat:5rt6s3oy5bgdjcoscwqeiruz7m

Report on Optimal Substructure Techniques for Stellar, Gas and Combined Samples [article]

I. Joncour, A. Buckner, P. Khalaj, E. Moraux, F. Motte
2020 arXiv   pre-print
Clustering of Discrete Distributions 3. Clustering of Continuous Distributions 4. Clustering in Astrophysics 5. StarFormMapper 6. Summary and Conclusions  ...  This document aims at reviewing the different types of clustering algorithms and substructures detection techniques in order to study the spatial and kinematic clustering of stars and detect the gas components  ...  Some other tools based on graph theory and network science have been applied to galaxy point distribution.  ... 
arXiv:2006.07830v1 fatcat:x7nsxwqorjdhzouele7vdspwv4

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Wenzhong Shi, Min Zhang, Rui Zhang, Shanxiong Chen, Zhao Zhan
2020 Remote Sensing  
The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed.  ...  Subsequently, the commonly used networks in AI for change detection are described.  ...  a transfer learning-based framework and convolutional AE models.  ... 
doi:10.3390/rs12101688 fatcat:rgzs4spxarabhjwebkxwppsnfu

Patient-specific modelling, simulation and real time processing for respiratory diseases [article]

Stavros Nousias
2022 arXiv   pre-print
Diameters of algorithm-based airways can be assigned using a Horsfield or Strahler ordering weighted against the ratio of the diameter of a child compared to that of its parent.  ...  Soft & hard clustering Given a number k of clusters, the soft clustering is a Gaussian mixture model that consists in fitting k Gaussian distributions to the distribution of the SDF values of the facets  ...  In other words, C i,i = 1 means calculating the residual at i th node uses the variables in j th node.  ... 
arXiv:2207.01082v3 fatcat:slafyd3k2fdcfopmwtwykr5kxy

Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation

Julian M. L. Budd, Krisztina Kovács, Alex S. Ferecskó, Péter Buzás, Ulf T. Eysel, Zoltán F. Kisvárday, Abigail Morrison
2010 PLoS Computational Biology  
Here, using reconstructions of in vivo labelled excitatory spiny cell and inhibitory basket cell intracortical axons combined with a variety of graph optimization algorithms, we empirically investigated  ...  The Strahler ordering scheme has been widely used to quantify natural tree-like branching hierarchies including dendritic as well as axonal arbors [41, 42] .  ...  Origin of Excess Axonal Wire To investigate potential sources of excess wire, we first used Strahler ordering [40] [41] [42] to characterise the branching structure of each axonal tree (for an example  ... 
doi:10.1371/journal.pcbi.1000711 pmid:20300651 pmcid:PMC2837396 fatcat:z6qm3lb3vjcy7ek24cnhd4czyu
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