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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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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
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
Gaussian mean shift is an EM algorithm
 IEEE Trans. on Pattern Analysis and Machine Intelligence
, 2005
"... The meanshift algorithm, based on ideas proposed by Fukunaga and Hostetler (1975), is a hillclimbing algorithm on the density defined by a finite mixture or a kernel density estimate. Meanshift can be used as a nonparametric clustering method and has attracted recent attention in computer vision ..."
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Cited by 42 (4 self)
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The meanshift algorithm, based on ideas proposed by Fukunaga and Hostetler (1975), is a hillclimbing algorithm on the density defined by a finite mixture or a kernel density estimate. Meanshift can be used as a nonparametric clustering method and has attracted recent attention in computer vision
Geodesic Mean Shift
, 2004
"... In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given surface. The method is based on the meanshift operator which was shown to be successful in image and video processing. Its strength stems from the fact that it works in a single space of ..."
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Cited by 7 (1 self)
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In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given surface. The method is based on the meanshift operator which was shown to be successful in image and video processing. Its strength stems from the fact that it works in a single space
Information theoretic mean shift algorithm
 In Proceedings of IEEE Conf. on Machine Learning for Signal Processing
"... In this paper we introduce a new cost function called Information Theoretic Mean Shift algorithm to capture the “predominant structure ” in the data. We formulate this problem with a cost function which minimizes the entropy of the data subject to the constraint that the CauchySchwartz distance bet ..."
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Cited by 2 (1 self)
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In this paper we introduce a new cost function called Information Theoretic Mean Shift algorithm to capture the “predominant structure ” in the data. We formulate this problem with a cost function which minimizes the entropy of the data subject to the constraint that the CauchySchwartz distance
� � � � � � � � � � � � � � Mean Shift � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � [9]. � � � � � � � � � � � � � � � � � � � � � � 2��
"... mu � � ˆρ(p, q) = ˆpu · ˆqu (2) u=1 � � � � � � ˆqu = qu Pmu k=1 qk, ˆpu = pu Pmu k=1 pk. qu q � � p � � � � u � � � �. � � �� pu � � � � � � �� �mu k=1 qk �� �mu k=1 pk �� � � ��� � � � � � � � 2� � � � � � � � � � � � � � � � � � � � � � � � � � �� mu � √ ρ(p, q) = ..."
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mu � � ˆρ(p, q) = ˆpu · ˆqu (2) u=1 � � � � � � ˆqu = qu Pmu k=1 qk, ˆpu = pu Pmu k=1 pk. qu q � � p � � � � u � � � �. � � �� pu � � � � � � �� �mu k=1 qk �� �mu k=1 pk �� � � ��� � � � � � � � 2� � � � � � � � � � � � � � � � � � � � � � � � � � �� mu � √ ρ(p, q) =
Implementation of Mean Shift Based On SOPC
"... Abstract: Mean Shift algorithm based on floating point arithmetic calculation is very difficult to be implemented on realtime systems. Another problem is that the tracking window of Mean Shift is not adaptive. In order to solve the problems above, an improved Mean Shift which can be implemented on ..."
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Abstract: Mean Shift algorithm based on floating point arithmetic calculation is very difficult to be implemented on realtime systems. Another problem is that the tracking window of Mean Shift is not adaptive. In order to solve the problems above, an improved Mean Shift which can be implemented
Image and Video Segmentation by Anisotropic Kernel Mean Shift
 In Proc. ECCV
, 2004
"... Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particula ..."
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Cited by 69 (1 self)
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Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more
(IDETC). Fast Mean Shift by Compact Density Representation
"... clustering, fast algorithm, mean shift, sampling ..."
Convergence of a Mean Shift Algorithm
"... Abstract: Mean shift is an effective iterative algorithm widely used in clustering, tracking, segmentation, discontinuity preserving smoothing, filtering, edge detection, and information fusion etc. However, its convergence, a key property of any iterative method, has not been rigorously proved till ..."
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Abstract: Mean shift is an effective iterative algorithm widely used in clustering, tracking, segmentation, discontinuity preserving smoothing, filtering, edge detection, and information fusion etc. However, its convergence, a key property of any iterative method, has not been rigorously proved
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