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105
Mean shift: A robust approach toward feature space analysis
 In PAMI
, 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
Abstract

Cited by 1461 (34 self)
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A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust Mestimators of location is also established. Algorithms for two lowlevel vision tasks, discontinuity preserving smoothing and image segmentation are described as applications. In these algorithms the only user set parameter is the resolution of the analysis, and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
A Multiscale Random Field Model for Bayesian Image Segmentation
, 1996
"... Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). While this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are com ..."
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Cited by 233 (18 self)
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Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). While this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. In this paper, we propose a new approach to Bayesian image segmentation which directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF), and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm which is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. We also develop a computationally effcient method for unsupervised estimation of m...
Mean Shift Analysis and Applications
, 1999
"... A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatialrange (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The ..."
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Cited by 157 (8 self)
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A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatialrange (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatilityofthe method and compare favorably with results described in the literature for the same images.
Adaptive fuzzy segmentation of magnetic resonance images
 IEEE TRANS. MED. IMAG
, 1999
"... An algorithm is presented for the fuzzy segmentation of twodimensional (2D) and threedimensional (3D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2D adaptive fuzzy Cme ..."
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Cited by 89 (8 self)
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An algorithm is presented for the fuzzy segmentation of twodimensional (2D) and threedimensional (3D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2D adaptive fuzzy Cmeans algorithm (2D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2D AFCM to threedimensional (3D) multispectral images. Because of the potential size of 3D image data, we also describe a new faster multigridbased algorithm for its implementation. We show, using simulated MR data, that 3D AFCM yields lower error rates than both the standard fuzzy Cmeans (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3D scalar and multispectral MR brain images.
Multiscale Representations of Markov Random Fields
 IEEE TRANSACTIONS ON SIGNAL PROCESSING. VOL 41. NO 12. DECEMBER 1993
, 1993
"... Recently, a framework for multiscale stochastic modeling was introduced based on coarsetofine scalerecursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. In this pap ..."
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Cited by 85 (27 self)
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Recently, a framework for multiscale stochastic modeling was introduced based on coarsetofine scalerecursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. In this paper, we show that this model class is also quite rich. In particular, we describe how 1D Markov processes and 2D Markov random fields (MRF’s) can be represented within this framework. The recursive structure of 1D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2D MRF’s are well known to be very difficult to analyze due to their noncausal structure, and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification. In contrast, our multiscale representations are based on scalerecursive models and thus lead naturally to scalerecursive algorithms, which can be substantially more efficient computationally than those associated with MRF models. In 1D, the multiscale representation is a generalization of the midpoint deflection construction of Brownian motion. The representation of 2D MRF’s is based on a further generalization to a “midline ” deflection construction. The exact representations of 2D MRF’s are used to motivate a class of multiscale approximate MRF models based on onedimensional wavelet transforms. We demonstrate the use of these latter models in the context of texture representation and, in particular, we show how they can be used as approximations for or alternatives to wellknown MRF texture models.
A Multiscale Algorithm For Image Segmentation By Variational Method.
, 1994
"... . Most segmentation algorithms are composed of several procedures: split and merge, small region elimination, boundary smoothing, : : : , each depending on several parameters. The introduction of an energy to minimize leads to a drastic reduction of these parameters. We prove that the most simple se ..."
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Cited by 69 (0 self)
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. Most segmentation algorithms are composed of several procedures: split and merge, small region elimination, boundary smoothing, : : : , each depending on several parameters. The introduction of an energy to minimize leads to a drastic reduction of these parameters. We prove that the most simple segmentation tool, the "region merging" algorithm, made according to the simplest energy, is enough to compute a local energy minimum belonging to a compact class and to achieve the job of most of the tools mentioned above. We explain why "merging" in a variational framework leads to a fast multiscale, multichannel algorithm, with a pyramidal structure. The obtained algorithm is O(n ln n), where n is the number of pixels of the picture. We apply this fast algorithm to make grey level and texture segmentation and we show experimental results. Key words. variational methods, nonnumerical algorithm, image processing, texture discrimination AMS(MOS) subject classifications. 68Q20,68U10, 1. Int...
Human ExpertLevel Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks
 Working Notes of the AAAI Workshop on Integrating Multiple Learned Models
, 1996
"... This paper presents the Plannett system, which combines artificial neural networks to achieve expertlevel accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features ..."
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Cited by 57 (0 self)
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This paper presents the Plannett system, which combines artificial neural networks to achieve expertlevel accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a threestage image analysis system called JARtool, the endtoend accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a fourimage test suite. JARtoolPlannett also achieves the best algorithmic accuracy on these images to date. Introduction Between 1991 and 1994, the Magellan space probe collected more than 30,000 synthetic aperture radar (SAR) images of the surface of the planet Venus, a greater amount of data than all previous planetary missions combined (Smyth et al. 1995). To a...
Markov Random Field Segmentation of Brain MR Images
, 1997
"... We describe a fullyautomatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal inhomo ..."
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Cited by 55 (0 self)
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We describe a fullyautomatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular the impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even singleecho MR images are well classified into grey matter, white matter, cerebrospinal fluid, scalpbone, and background. A simulated annealing and an iterated conditional modes implementation are presented. Index Terms Magnetic Resonance Imaging, Segmentation, Markov Random Fields I. INTRODUCTION Excellent softtissue contrast and high spatial resolution make magnetic resonance imaging the method for anatomical imaging in brain research. Segmentation of the MR imag...
Color Image Segmentation: A StateoftheArt Survey
"... Segmentation is the lowlevel operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond to actual objects, or parts of them, within the images. Thus, ..."
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Cited by 49 (0 self)
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Segmentation is the lowlevel operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond to actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higherlevel operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of graylevel images since these have been the only kind of visual information that acquisition devices were able to take and computer resources to handle. Nowadays, color imagery has definitely supplanted monochromatic information and computation power is no longer a limitation in processing large volumes of data. The attention has accordingly been focused in recent years on algorithms for segmentation of color images and various techniques, ofted borrowed from the background of graylevel image segmentation, have been proposed. This paper provides a review of methods advanced in the past few years for segmentation of color images.
An Adaptive Fuzzy CMeans Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities
 Pattern Recognition Letters
, 1998
"... We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy Cmeans algorithm to include a multiplier field, whic ..."
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Cited by 48 (6 self)
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We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy Cmeans algorithm to include a multiplier field, which allows the centroids for each class to vary across the image. First and second order regularization terms ensure that the multiplier field is both slowly varying and smooth. An iterative algorithm that minimizes the objective function is described, and its efficacy is demonstrated on several test images. Key words: image segmentation, fuzzy cmeans, intensity inhomogeneities, magnetic resonance imaging 1 Introduction Image segmentation plays an important role in a variety of applications such as robot vision, object recognition, and medical imaging. There has been considerable interest recently in the use of fuzzy segmentation methods, which retain more information from the original im...