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891,478
Mean shift, mode seeking, and clustering
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeking proce ..."
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Cited by 624 (0 self)
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Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a mode
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 ..."
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Cited by 2395 (37 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
RealTime Tracking of NonRigid Objects using Mean Shift
 IEEE CVPR 2000
, 2000
"... A new method for realtime tracking of nonrigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) an ..."
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Cited by 815 (19 self)
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A new method for realtime tracking of nonrigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution
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 200 (9 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
∗ MeanShift Algorithm
"... – theory & applications – meanshift object tracking ..."
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Cited by 1 (0 self)
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– theory & applications – meanshift object tracking
Mean shift blob tracking through scale space
 in Proc. CVPR
"... The meanshift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the meanshift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this paper, ..."
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Cited by 207 (3 self)
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The meanshift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the meanshift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this pa
Boosted Mean Shift Clustering
"... Abstract. Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture the cor ..."
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Abstract. Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture
Mean shift is a bound optimization
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—We build on the current understanding of mean shift as an optimization procedure. We demonstrate that, in the case of piecewise constant kernels, mean shift is equivalent to Newton’s method. Further, we prove that, for all kernels, the mean shift procedure is a quadratic bound maximization. ..."
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Cited by 46 (0 self)
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Abstract—We build on the current understanding of mean shift as an optimization procedure. We demonstrate that, in the case of piecewise constant kernels, mean shift is equivalent to Newton’s method. Further, we prove that, for all kernels, the mean shift procedure is a quadratic bound maximization
Mean Shift Clustering
, 2005
"... Mean shift represents a general nonparametric mode finding/clustering procedure. In contrast to the classic Kmeans clustering approach (Duda, Hart & Stork, 2001), there are no embedded assumptions on the shape of the distribution nor the number of modes/clusters. Mean shift was first proposed ..."
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Mean shift represents a general nonparametric mode finding/clustering procedure. In contrast to the classic Kmeans clustering approach (Duda, Hart & Stork, 2001), there are no embedded assumptions on the shape of the distribution nor the number of modes/clusters. Mean shift was first proposed
Mean shift for graph bundling
"... We present a fast and simple adaption of the wellknown mean shift technique for image segmentation to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply the equivalent of mean shift segmen ..."
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We present a fast and simple adaption of the wellknown mean shift technique for image segmentation to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply the equivalent of mean shift
Results 1  10
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891,478