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The role of linear objective functions in barrier methods: Corrigenda
 Mathematical Programming, Series A
, 2000
"... . The published paper contains a number of typographical errors and an incomplete proof. We indicate the corrections here. Our paper [1] contains the following typographical errors. Page 364, statement of Proposition 1. Replace "Assume that (30) is satisfied: : :" by "Assume that the ..."
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Cited by 10 (4 self)
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. The published paper contains a number of typographical errors and an incomplete proof. We indicate the corrections here. Our paper [1] contains the following typographical errors. Page 364, statement of Proposition 1. Replace "Assume that (30) is satisfied: : :" by "Assume that the conditions of Theorem 1 hold and that (30) is satisfied: : :". Equation (31). Replace the exponent "oe \Gamma 1" by "oe". Equation (37), second displayed line. Replace "o( 1+oe=2 )" by "O( 1+oe=2 )". Equation (47). Delete "i = q + 1; : : : ; m". Page 370, line 10. Replace "~oe ! oe" by "1 ~ oe ! oe 2". Similarly, on line 2 of Algorithm NL, replace "0 ! ~ oe ! oe" by "1 ~ oe ! oe 2". The final part of the proof of Theorem 2 is incomplete. We remedy this fault by deleting the material from line 6 on page 368 through the end of the proof, and replacing with the following. For the term in brackets, we have 1 +O( oe\Gamma1 ) (1 \Gamma )(=+ ) + +O( oe\Gamma1 )(=+ ) \Gamma 1 = \Gamma (1 \Gamma ...
Solving Fuzzy Relation Equations with a Linear Objective Function
, 1996
"... An optimization model with a linear objective function subject to a system of fuzzy relation equations is presented. Due to the nonconvexity of its feasible domain defined by fuzzy relation equations, designing an efficient solution procedure for solving such problems is not a trivial job. In this ..."
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Cited by 12 (1 self)
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An optimization model with a linear objective function subject to a system of fuzzy relation equations is presented. Due to the nonconvexity of its feasible domain defined by fuzzy relation equations, designing an efficient solution procedure for solving such problems is not a trivial job
Lambertian Reflectance and Linear Subspaces
, 2000
"... We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wi ..."
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Cited by 526 (20 self)
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We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a
The Extended Linear Complementarity Problem
, 1993
"... We consider an extension of the horizontal linear complementarity problem, which we call the extended linear complementarity problem (XLCP). With the aid of a natural bilinear program, we establish various properties of this extended complementarity problem; these include the convexity of the biline ..."
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Cited by 785 (30 self)
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of the bilinear objective function under a monotonicity assumption, the polyhedrality of the solution set of a monotone XLCP, and an error bound result for a nondegenerate XLCP. We also present a finite, sequential linear programming algorithm for solving the nonmonotone XLCP.
LINEAR OBJECTIVE FUNCTION OPTIMIZATION WITH THE MAXPRODUCT FUZZY RELATION INEQUALITY CONSTRAINTS
"... chi ..."
KernelBased Object Tracking
, 2003
"... A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatiallysmooth similarity fu ..."
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Cited by 907 (4 self)
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information, Kalman tracking using motion models, and face tracking. Keywords: nonrigid object tracking; target localization and representation; spatiallysmooth similarity function; Bhattacharyya coefficient; face tracking. 1
Reconstruction and Representation of 3D Objects with Radial Basis Functions
 Computer Graphics (SIGGRAPH ’01 Conf. Proc.), pages 67–76. ACM SIGGRAPH
, 2001
"... We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from pointcloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs al ..."
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Cited by 503 (1 self)
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We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from pointcloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs
Hierarchical Models of Object Recognition in Cortex
, 1999
"... The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore th ..."
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Cited by 836 (84 self)
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the biological feasibility of this class of models to explain higher level visual processing, such as object recognition. We describe a new hierarchical model that accounts well for this complex visual task, is consistent with several recent physiological experiments in inferotemporal cortex and makes testable
Pointsto Analysis in Almost Linear Time
, 1996
"... We present an interprocedural flowinsensitive pointsto analysis based on type inference methods with an almost linear time cost complexity. To our knowledge, this is the asymptotically fastest nontrivial interprocedural pointsto analysis algorithm yet described. The algorithm is based on a nons ..."
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Cited by 596 (3 self)
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We present an interprocedural flowinsensitive pointsto analysis based on type inference methods with an almost linear time cost complexity. To our knowledge, this is the asymptotically fastest nontrivial interprocedural pointsto analysis algorithm yet described. The algorithm is based on a non
Object Detection with Discriminatively Trained Part Based Models
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves stateoftheart results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
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Cited by 1424 (49 self)
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and optimizing the latent SVM objective function.
Results 1  10
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3,763,908