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The Random Cluster Model for robust geometric fitting
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
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Random hypothesis generation is central to robust geometric model fitting in computer vision. ...
We show how large clusters of data from genuine instances of the geometric model can be efficiently harvested to produce more accurate hypotheses. ...
Acknowledgements This work is partly supported by the Australian Research Council grant DP0878801. ...
doi:10.1109/cvpr.2012.6247740
dblp:conf/cvpr/PhamCYS12
fatcat:iai2mtv3kzemxevqubky6wolhe
The Random Cluster Model for Robust Geometric Fitting
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Random hypothesis generation is central to robust geometric model fitting in computer vision. ...
We show how large clusters of data from genuine instances of the geometric model can be efficiently harvested to produce more accurate hypotheses. ...
Acknowledgements This work is partly supported by the Australian Research Council grant DP0878801. ...
doi:10.1109/tpami.2013.2296310
pmid:26353345
fatcat:hsdmof2jlzestjlk5rtxbi7lji
Robust clustering of multi-structure data with enhanced sampling
2013
2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)
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The proposed clustering framework is established on a J-linkage framework [4] with guided sampling technique for multi-structures [7] . ...
This paper deals with the improvement of clustering results by enhancing performance of sampling. ...
Hence, finding underlying models is a key for clustering, and it is the model fitting problem that deals with fitting models to data. ...
doi:10.1109/urai.2013.6677397
dblp:conf/urai/KangKC13a
fatcat:zz5vgdfy2zfr5a2qbfm56hpexy
The Node Degree Distribution in Power Grid and Its Topology Robustness under Random and Selective Node Removals
2010
2010 IEEE International Conference on Communications Workshops
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It is found that the node degree in a power grid can be well fitted by a mixture distribution coming from the sum of a truncated Geometric random variable and an irregular Discrete random variable. ...
In this paper we numerically study the topology robustness of power grids under random and selective node breakdowns, and analytically estimate the critical node-removal thresholds to disintegrate a system ...
As we pointed out in Section III, the node degree in power grids fits well a mixture model which includes the sum of a truncated Geometric random variable G(p, k max ) plus an irregular Discrete random ...
doi:10.1109/iccw.2010.5503926
fatcat:gui7qt6zefez7fnqzgaj35zupm
Page 6338 of Mathematical Reviews Vol. , Issue 99i
[page]
1999
Mathematical Reviews
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Summary: “A geometric framework is proposed for nonlinear models with random effects, based on a modified version of the geometric structure presented by D. M. Bates and D. G. Watts [J. Roy. Statist. ...
This geometric framework is used to study some asymptotic inference in terms of curvatures for nonlinear regression models with random effects. ...
Modeling interactome: scale-free or geometric?
2004
Bioinformatics
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We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI ...
We examine the fit of four different network models, including Erdös-Rényi, scalefree, and geometric random network models, to these PPI networks with respect to the measures of local and global network ...
the diameter and clustering coefficient parameters also indicate that PPI networks are closer to the geometric random graph model than to the ER, ER-DD and SF models. ...
doi:10.1093/bioinformatics/bth436
pmid:15284103
fatcat:xhndu5ogejcczg3b67cx4njzr4
An integrative approach to modeling biological networks
2010
Journal of Integrative Bioinformatics
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We confirm that geometric random graphs (GEO) are the best-fitting model for RIGs. ...
Thus, we present an integrative approach that feeds a variety of network properties into five machine learning classifiers to predict the best-fitting network model for PPI networks and RIGs. ...
We use a standard implementation of MNB [9] from WEKA [10], a publicly available collection of machine learning algorithms for data mining. ...
doi:10.1515/jib-2010-120
fatcat:vvza4czztnholc6yzu2iszwac4
Multiple structure recovery via robust preference analysis
2017
Image and Vision Computing
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First points are represented in the preference space, then Robust PCA (Principal Component Analysis) and Symmetric NMF (Non negative Matrix Factorization) are used to break the multi-model fitting problem ...
This paper address the extraction of multiple models from outlier-contaminated data by exploiting preference analysis and low rank approximation. ...
Introduction Geometric multi-model fitting aims at extracting parametric models from unstructured data in order to organize and aggregate visual content in suitable higher-level geometric structures 2 ...
doi:10.1016/j.imavis.2017.09.005
fatcat:ibck4muv2jagvbicz6njlsy76i
Random Field Driven Spatial Complexity at the Mott Transition inVO2
2016
Physical Review Letters
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We show that the geometric properties of the metallic and insulating puddles observed by scanning near-field infrared microscopy are consistent with the system passing near criticality of the random field ...
Ising model as temperature is varied. ...
Using quantitative cluster techniques, we argue that the critical endpoint is in the universality class of the random field Ising model, which is well-known for nonequilibrium effects such as broad hysteresis ...
doi:10.1103/physrevlett.116.036401
pmid:26849604
fatcat:t6ro3fxdlfa63nxrykzjaqka34
Methods for the analysis of incidence rates in cluster randomized trials
2002
International Journal of Epidemiology
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Both the unadjusted analysis and the analysis adjusting for confounders are shown to be robust, even for very small numbers of clusters, in situations that are likely to arise in randomized trials. ...
To control confounding, a Poisson regression model is fitted to the data incorporating all covariates except intervention status, and the analysis is carried out on the residuals from this model. ...
We are extremely grateful to Fred Binka for allowing us to use the data from the trial conducted in Ghana, and to Christian Lengeler for co-ordinating the series of trials and, in particular, a workshop ...
doi:10.1093/ije/31.4.839
pmid:12177032
fatcat:hgvwauctdjcrtmghh2m3omvuce
Parametric Segmentation of Nonlinear Structures in Visual Data: An Accelerated Sampling Approach
[chapter]
2013
Nonlinear Approaches in Engineering Applications 2
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We also propose an accelerated sampling method for robust segmentation of multiple structures. ...
Examples include fitting multiple ellipse patterns to image data, estimation and segmentation of multiple motions in subsequent images in video, and fitting nonlinear patterns to cell images for cancer ...
The inliers to the fit are clustered into groups and the largest group is selected for a second round of random sampling. ...
doi:10.1007/978-1-4614-6877-6_9
fatcat:7xqbn55drvhsnjawzwwnqwod3y
Grouped outlier removal for robust ellipse fitting
2015
2015 14th IAPR International Conference on Machine Vision Applications (MVA)
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To confront the grouped outliers while maintaining the fitting efficiency, we introduce a proximity-based 'split and merge' approach to cluster the edge points into subsets, followed by a breath-first ...
This paper presents a novel outlier removal method which is capable of fitting ellipse in real-time under high outlier rate, based on the phenomenon that outliers generated by ellipse edge point detector ...
In such cases, robust fitting algorithm like random sample consensus (RANSAC) [4] is generally applied to eliminate outliers. ...
doi:10.1109/mva.2015.7153152
dblp:conf/mva/ShaoIH15
fatcat:kwx3xebblzfhbardqeie4iu5kq
Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting
2020
Sensors
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of the state-of-the-art methods in geometric multi-model fitting. ...
Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20113037
pmid:32471177
fatcat:ojtuz6bshjhmvcd36zuqe4tlva
Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments
2009
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
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The objects are segmented out from partial views and a reconstructed model is computed by fitting geometric primitive classes such as planes, spheres, cylinders, and cones. ...
Our proposed approach proposes a robust geometric mapping pipeline for large input datasets that extracts relevant objects useful for a personal robotic assistant to perform manipulation tasks. ...
Acknowledgements This work was supported by the CoTeSys (Cognition for Technical Systems) excellence cluster. ...
doi:10.1109/iros.2009.5354683
dblp:conf/iros/RusuBMB09
fatcat:wpxmeyj5urbijjayrrhgs5hlfu
Connecting Complex Electronic Pattern Formation to Critical Exponents
[article]
2018
arXiv
pre-print
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Whereas thermodynamic critical exponents are derived from the behavior of Fortuin-Kasteleyn (FK) clusters, critical exponents can be similarly defined for geometric clusters. ...
In this paper, we advance the theory of criticality as it pertains to those geometric clusters (defined as connected sets of nearest-neighbor aligned spins) in the context of Ising models. ...
We expect that future studies regarding geometric criticality in random bond and random field Ising models will further facilitate the application of geometric cluster analyses to the interpretation of ...
arXiv:1803.08485v1
fatcat:jzppdl2vnvhf7h7hqmbelvbzpq
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