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Fast Global Minimization of the Active Contour/Snake Model
"... The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. ..."
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Cited by 161 (10 self)
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on the unification of image segmentation and image denoising tasks into a global minimization framework. More precisely, we propose to unify three wellknown image variational models, namely the snake model, the RudinOsherFatemi denoising model and the MumfordShah segmentation model. We will establish theorems
Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
 SIAM JOURNAL ON APPLIED MATHEMATICS
, 2006
"... We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes. ..."
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Cited by 153 (6 self)
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We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes.
Global Minimization Via . . .
 JOURNAL OF GLOBAL OPTIMIZATION
, 2005
"... Given a function on R^n with many multiple local minima we approximate it from below, via concave minimization, with a piecewiselinear convex function by using sample points from the given function. The piecewiselinear function is then minimized using a single linear program to obtain an approxima ..."
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Given a function on R^n with many multiple local minima we approximate it from below, via concave minimization, with a piecewiselinear convex function by using sample points from the given function. The piecewiselinear function is then minimized using a single linear program to obtain
Globally minimal surfaces by continuous Maximal Flows
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Globally minimal surfaces by continuous maximal flows In this paper we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction. We present a solution, simulating a continuous maximal flow by a novel system of partial differential equations. Ex ..."
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Cited by 52 (5 self)
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Globally minimal surfaces by continuous maximal flows In this paper we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction. We present a solution, simulating a continuous maximal flow by a novel system of partial differential equations
Global minimizers for axisymmetric multiphase membranes.
, 2012
"... We consider a CanhamHelfrichtype variational problem defined over closed surfaces enclosing a fixed volume and having fixed surface area. The problem models the shape of multiphase biomembranes. It consists of minimizing the sum of the CanhamHelfrich energy, in which the bending rigidities and sp ..."
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and spontaneous curvatures are now phasedependent, and a line tension penalization for the phase interfaces. By restricting attention to axisymmetric surfaces and phase distributions, we extend our previous results for a single phase [7] and prove existence of a global minimizer.
Global minimization of rational functions and the nearest GCDs
 J. of Global Optimization
"... This paper discusses the global minimization of rational functions with or without constraints. The sum of squares (SOS) relaxations are proposed to find the global minimum and minimizers. Some special features of the SOS relaxations are studied. As an application, we show how to find the nearest co ..."
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Cited by 12 (0 self)
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This paper discusses the global minimization of rational functions with or without constraints. The sum of squares (SOS) relaxations are proposed to find the global minimum and minimizers. Some special features of the SOS relaxations are studied. As an application, we show how to find the nearest
Gradientbased learning applied to document recognition
 Proceedings of the IEEE
, 1998
"... Multilayer neural networks trained with the backpropagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradientbased learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
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Cited by 1533 (84 self)
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transformer networks (GTN’s), allows such multimodule systems to be trained globally using gradientbased methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility
Storage management and caching in PAST, a largescale, persistent peertopeer storage utility
, 2001
"... This paper presents and evaluates the storage management and caching in PAST, a largescale peertopeer persistent storage utility. PAST is based on a selforganizing, Internetbased overlay network of storage nodes that cooperatively route file queries, store multiple replicas of files, and cache a ..."
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Cited by 803 (23 self)
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balances the number of files stored on each node. However, nonuniform storage node capacities and file sizes require more explicit storage load balancing to permit graceful behavior under high global storage utilization; likewise, nonuniform popularity of files requires caching to minimize fetch distance
A tutorial on support vector machines for pattern recognition
 Data Mining and Knowledge Discovery
, 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
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Cited by 3393 (12 self)
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The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when
Results 1  10
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8,889