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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 514 (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 776 (28 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.
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 817 (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
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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ofGaussians model (for multimodal distributions). These probability densities are then used to formulate a maximumlikelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection
Mental rotation of threedimensional objects
 Science
, 1971
"... you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact inform ..."
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Cited by 818 (5 self)
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you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at
Limma: linear models for microarray data
 Bioinformatics and Computational Biology Solutions using R and Bioconductor
, 2005
"... This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
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Cited by 759 (13 self)
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This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents
LINEAR OBJECTIVE FUNCTION OPTIMIZATION WITH THE MAXPRODUCT FUZZY RELATION INEQUALITY CONSTRAINTS
"... chi ..."
The 2005 pascal visual object classes challenge
, 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
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Cited by 633 (24 self)
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Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars
Results 1  10
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