Results 1  10
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127
Active Contours without Edges
, 2001
"... In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy ..."
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Cited by 1206 (38 self)
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In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "meancurvature flow"like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
ModelBased Clustering, Discriminant Analysis, and Density Estimation
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
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Cited by 573 (29 self)
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Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as \How many clusters are there?", "Which clustering method should be used?" and \How should outliers be handled?". We outline a general methodology for modelbased clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, mineeld detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology, a...
How many clusters? Which clustering method? Answers via modelbased cluster analysis
 THE COMPUTER JOURNAL
, 1998
"... ..."
ModelBased Clustering and Data Transformations for Gene Expression Data
, 2001
"... Motivation: Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particula ..."
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Cited by 200 (9 self)
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Motivation: Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, modelbased clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a 'good' clustering method and determining the 'correct' number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications.
An Active Contour Model without Edges
 Int. Conf. ScaleSpace Theories in Computer Vision
, 1999
"... In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. ..."
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Cited by 103 (11 self)
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In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient.
MCLUST: Software for Modelbased Cluster Analysis
 Journal of Classification
, 1999
"... MCLUST is a software package for cluster analysis written in Fortran and interfaced to the SPLUS commercial software package1. It implements parameterized Gaussian hierarchical clustering algorithms [16, 1, 7] and the EM algorithm for parameterized Gaussian mixture models [5, 13, 3, 14] with the po ..."
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Cited by 95 (16 self)
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MCLUST is a software package for cluster analysis written in Fortran and interfaced to the SPLUS commercial software package1. It implements parameterized Gaussian hierarchical clustering algorithms [16, 1, 7] and the EM algorithm for parameterized Gaussian mixture models [5, 13, 3, 14] with the possible addition of a Poisson noise term. MCLUST also includes functions that combine hierarchical clustering, EM and the Bayesian Information Criterion (BIC) in a comprehensive clustering strategy [4, 8]. Methods of this type have shown promise in a number of practical applications, including character recognition [16], tissue segmentation [1], mine eld and seismic fault detection [4], identi cation of textile aws from images [2], and classi cation of astronomical data [3, 15]. Aweb page with related links can be found at
AE: MCLUST Version 3 for R: Normal Mixture Modeling and ModelBased Clustering
 Department of Statistics, University of Washington
, 2006
"... MCLUST is a contributed R package for normal mixture modeling and modelbased clustering. It provides functions for parameter estimation via the EM algorithm for normal mixture models with a variety of covariance structures, and functions for simulation from these models. Also included are functions ..."
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Cited by 87 (1 self)
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MCLUST is a contributed R package for normal mixture modeling and modelbased clustering. It provides functions for parameter estimation via the EM algorithm for normal mixture models with a variety of covariance structures, and functions for simulation from these models. Also included are functions that combine modelbased hierarchical clustering, EM for mixture estimation and the Bayesian Information Criterion (BIC) in comprehensive strategies for clustering, density estimation and discriminant analysis. There is additional functionality for displaying and visualizing the models along with clustering and classification results. A number of features of the software have been changed in this version, and the functionality has been expanded to include regularization for normal mixture models via a Bayesian prior. MCLUST is licensed by the University of Washington and distributed through
Algorithms for modelbased Gaussian hierarchical clustering
 SIAM Journal on Scientific Computing
, 1998
"... 1 Funded by the O ce of Naval Research under contracts N000149610192 and N00014961 ..."
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Cited by 86 (11 self)
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1 Funded by the O ce of Naval Research under contracts N000149610192 and N00014961
Clustering for sparsely sampled functional data
 Journal of the American Statistical Association
, 2003
"... We develop a flexible modelbased procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse set of time points. In addition to producing final cluster assignments, the procedure generates pre ..."
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Cited by 85 (7 self)
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We develop a flexible modelbased procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse set of time points. In addition to producing final cluster assignments, the procedure generates predictions and confidence intervals for missing portions of curves. Our approach also provides many useful tools for evaluating the resulting models. Clustering can be assessed visually via low dimensional representations of the curves, and the regions of greatest separation between clusters can be determined using a discriminant function. Finally, we extend the model to handle multiple functional and finite dimensional covariates and show how it can be applied to standard finite dimensional clustering problems involving missing data.