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10,674
Normal variation for . . .
, 2007
"... Let Σ be a closed, smooth surface in R 3. For any two sets X, Y ⊂ R 3, let d(X, Y) denote the Euclidean distance between X and Y. The local feature size f(x) at a point x ∈ Σ is defined to be the distance d(x, M) where M is the medial axis of Σ. Let np denote the unit normal (inward) to Σ at point p ..."
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Let Σ be a closed, smooth surface in R 3. For any two sets X, Y ⊂ R 3, let d(X, Y) denote the Euclidean distance between X and Y. The local feature size f(x) at a point x ∈ Σ is defined to be the distance d(x, M) where M is the medial axis of Σ. Let np denote the unit normal (inward) to Σ at point
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation
, 2002
"... There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is ..."
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Cited by 718 (9 self)
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There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment
Nonlinear total variation based noise removal algorithms
, 1992
"... A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the g ..."
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Cited by 2271 (51 self)
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A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using
Nonnormal variation and regression to
"... Nonnormal variation across repeated measurements leads to nonlinear and heteroskedastic regression to the mean unlike the simple linear and homoskedastic regression to the mean found in normal models. This paper investigates the nature of the regression to the mean phenomenon in nonnormal settings ..."
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Nonnormal variation across repeated measurements leads to nonlinear and heteroskedastic regression to the mean unlike the simple linear and homoskedastic regression to the mean found in normal models. This paper investigates the nature of the regression to the mean phenomenon in nonnormal
LucasKanade 20 Years On: A Unifying Framework: Part 3
 International Journal of Computer Vision
, 2002
"... Since the LucasKanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow, tracking, and layered motion, to mosaic construction, medical image registration, and face coding. Numerous algorithms hav ..."
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Cited by 706 (30 self)
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first consider linear appearance variation when the error function is the Euclidean L2 norm. We describe three different algorithms, the simultaneous, project out, and normalization inverse compositional algorithms, and empirically compare them. Afterwards we consider the combination of linear
Histograms of Oriented Gradients for Human Detection
 In CVPR
, 2005
"... We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly out ..."
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Cited by 3735 (9 self)
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outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that finescale gradients, fine orientation binning, relatively coarse spatial binning, and highquality local contrast normalization in overlapping descriptor blocks
Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms
 IEEE Transactions on Information Theory
, 2005
"... Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
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Cited by 585 (13 self)
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the Bethe approximation, and corresponding generalized belief propagation (GBP) algorithms. We emphasize the conditions a free energy approximation must satisfy in order to be a “valid ” or “maxentnormal ” approximation. We describe the relationship between four different methods that can be used
Modeling and Forecasting Realized Volatility
, 2002
"... this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly rightskewed, the distributions of the logarithms of realized volatilities are a ..."
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Cited by 549 (50 self)
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frequency models, we find that our simple Gaussian VAR forecasts generally produce superior forecasts. Furthermore, we show that, given the theoretically motivated and empirically plausible assumption of normally distributed returns conditional on the realized volatilities, the resulting lognormalnormal mixture
Active Appearance Models Revisited
 International Journal of Computer Vision
, 2003
"... Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to ..."
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Cited by 462 (39 self)
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to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show how the appearance variation can be "
The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs
 Journal of Neuroscience
, 1993
"... How random is the discharge pattern of cortical neurons? We examined recordings from primary visual cortex (Vl; Knierim and Van Essen, 1992) and extrastriate cortex (MT; Newsome et al., 1989a) of awake, behaving macaque monkey and compared them to analytical predictions. For nonbursting cells firi ..."
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Cited by 457 (11 self)
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firing at sustained rates up to 300 Hz, we evaluated two indices of firing variability: the ratio of the variance to the mean for the number of action potentials evoked by a constant stimulus, and the ratenormalized coefficient of variation (C,) of the interspike interval distribution. Firing
Results 1  10
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10,674