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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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appearance variation with the robust error functions described in Part 2 of this series. We first derive robust versions of the simultaneous and normalization algorithms. Since both of these algorithms are very inefficient, as in Part 2 we derive efficient approximations based on spatial coherence. We end
An affine invariant interest point detector
 In Proceedings of the 7th European Conference on Computer Vision
, 2002
"... Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of ..."
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Cited by 1467 (55 self)
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by local extrema of normalized derivatives over scale. 3) An affineadapted Harris detector determines the location of interest points. A multiscale version of this detector is used for initialization. An iterative algorithm then modifies location, scale and neighbourhood of each point and converges
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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;belief revision") version, Weiss For the case of networks with multiple loops, Richard son To summarize, what is currently known about loopy propagation is that ( 1) it works very well in an error correcting code setting and (2) there are conditions for a singleloop network for which it can be guaranteed
Equivariant Adaptive Source Separation
 IEEE Trans. on Signal Processing
, 1996
"... Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Eq ..."
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Cited by 449 (9 self)
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Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI
Policy gradient methods for reinforcement learning with function approximation.
 In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
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Cited by 439 (20 self)
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that the gradient can be written in a form suitable for estimation from experience aided by an approximate actionvalue or advantage function. Using this result, we prove for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal
Smodels  an Implementation of the Stable Model and WellFounded Semantics for Normal Logic Programs
, 1997
"... The Smodels system is a C++ implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs. The system includes two modules: (i) smodels which implements the two semantics for ground programs and (ii) parse which computes a grounded version of a range ..."
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Cited by 294 (9 self)
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The Smodels system is a C++ implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs. The system includes two modules: (i) smodels which implements the two semantics for ground programs and (ii) parse which computes a grounded version of a
The bayesian lasso
, 2005
"... The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (doubleexponential) priors. Gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors ..."
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Cited by 284 (0 self)
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The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (doubleexponential) priors. Gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors
Summarizing Text Documents: Sentence Selection and Evaluation Metrics
 In Research and Development in Information Retrieval
, 1999
"... Humanquality text summarization systems are difficult to design, and even more difficult to evaluate, in part because documents can differ along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for incl ..."
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Cited by 236 (7 self)
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. The potential linguistic ones were derived from an analysis of newswire summaries. Toevaluate these features we use a normalized version of precisionrecall curves, with a baseline of random sentence selection, as well as analyze the properties of such a baseline. We illustrate our discussions with empirical
A revised version of the psychoticism scale
 Personality and Individual Differences
, 1985
"... SummaryIn view of certain psychometric deficiencies of the original Psychoticism scale, an attempt was made to improve the scale by adding new items. It was attempted to increase the internal reliability of the scale, improve the shape of the distribution and increase the mean and variance score. T ..."
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Cited by 175 (0 self)
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. Two different studies are discussed. Reliabilities are now somewhat improved, distributions are closer to normal and mean scores are higher than on the old scale. Four new short 12item scales for the measurement of P, E, N and L are also given.
Normal Meshes
, 2000
"... Normal meshes are new fundamental surface descriptions inspired by differential geometry. A normal mesh is a multiresolution mesh where each level can be written as a normal offset from a coarser version. Hence the mesh can be stored with a single float per vertex. We present an algorithm to approxi ..."
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Cited by 144 (8 self)
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Normal meshes are new fundamental surface descriptions inspired by differential geometry. A normal mesh is a multiresolution mesh where each level can be written as a normal offset from a coarser version. Hence the mesh can be stored with a single float per vertex. We present an algorithm
Results 1  10
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