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115
An EM Algorithm for WaveletBased Image Restoration
, 2002
"... This paper introduces an expectationmaximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with lowcomplexity, expressed in terms of the wavelet coecients, taking a ..."
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Cited by 351 (23 self)
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This paper introduces an expectationmaximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with lowcomplexity, expressed in terms of the wavelet coecients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated waveletbased restoration but, except for certain special cases, the resulting criteria are solved approximately or require very demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation oered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. The algorithm alternates between an Estep based on the fast Fourier transform (FFT) and a DWTbased Mstep, resulting in an ecient iterative process requiring O(N log N) operations per iteration. Thus, it is the rst image restoration algorithm that optimizes a waveletbased penalized likelihood criterion and has computational complexity comparable to that of standard wavelet denoising or frequency domain deconvolution methods. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach outperforms several of the best existing methods in benchmark tests, and in some cases is also much less computationally demanding.
Variable Kernel Density Estimation
 Annals of Statistics
, 1992
"... In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner product between a mapped test point and the centroid of mapped training points in kernel feature space. Our robust KDE replaces the centroid with a robust estimate based on M ..."
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Cited by 109 (4 self)
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In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner product between a mapped test point and the centroid of mapped training points in kernel feature space. Our robust KDE replaces the centroid with a robust estimate based on Mestimation [1]. The iteratively reweighted least squares (IRWLS) algorithm for Mestimation depends only on inner products, and can therefore be implemented using the kernel trick. We prove the IRWLS method monotonically decreases its objective value at every iteration for a broad class of robust loss functions. Our proposed method is applied to synthetic data and network traffic volumes, and the results compare favorably to the standard KDE. Index Terms — kernel density estimation, Mestimator, outlier, kernel feature space, kernel trick 1.
Facility location models for distribution system design
, 2004
"... The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamenta ..."
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Cited by 70 (0 self)
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The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current stateoftheart. In particular, continuous location models, network location models, mixedinteger programming models, and applications are summarized.
On The Convergence Of The Lagged Diffusivity Fixed Point Method In Total Variation Image Restoration
, 1997
"... . In this paper we show that the lagged diffusivity fixed point algorithm introduced by Vogel and Oman in [10] to solve the problem of Total Variation denoising, proposed by Rudin, Osher and Fatemi in [9], is a particular instance of a class of algorithms introduced by Eckhardt and Voss in [11], who ..."
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Cited by 61 (4 self)
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. In this paper we show that the lagged diffusivity fixed point algorithm introduced by Vogel and Oman in [10] to solve the problem of Total Variation denoising, proposed by Rudin, Osher and Fatemi in [9], is a particular instance of a class of algorithms introduced by Eckhardt and Voss in [11], whose origins can be traced back to Weiszfeld's original work for minimizing a sum of Euclidean lengths [12]. There have recently appeared several proofs for the convergence of this algorithm [2], [3], [6]. Here we present a proof of the global and linear convergence using the framework introduced in [11] and give a bound for the convergence rate of the fixed point iteration that agrees with our experimental results. These results are also valid for suitable generalizations of the fixed point algorithm. 1. Introduction. Recently, a new class of nonlinear PDE based techniques has emerged for image restoration problems, primarily because they preserve sharp edges better. A particularly popular te...
Agglomeration and economic geography
 In J. V. Henderson and J.F. Thisse (Eds.), Handbook of Urban and Regional Economics
, 2003
"... Peaks and troughs in the spatial distributions of population, employment and wealth are a universal phenomenon in search of a general theory. Such spatial imbalances have two possible explanations. In the first one, uneven economic development can be seen as the result of the uneven distribution of ..."
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Cited by 54 (8 self)
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Peaks and troughs in the spatial distributions of population, employment and wealth are a universal phenomenon in search of a general theory. Such spatial imbalances have two possible explanations. In the first one, uneven economic development can be seen as the result of the uneven distribution of natural resources. This is sometimes called ‘first nature ’ and refers to exogenously given characteristics of different sites. However, it falls short of providing a reasonable explanation of many other clusters of activities, which are much less dependent on natural advantage. The aim of geographical economics is precisely to understand what are the economic forces that, after controlling for first nature, account for ‘second nature’, which emerges as the outcome of human beings ’ actions to improve upon the first one. Specifically, geographical economics asks what are the economic forces that can sustain a large permanent imbalance in the distributions of economic activities. In this paper, we focus on the socalled ‘new economic geography’ approach. After having described some of the main results developed in standard location theory, we use a unified framework to survey the home market effect as well as coreperiphery models. These models have been criticized by geographers because they accounts for some spatial costs while putting others aside without saying why. Furthermore, coreperiphery models also exhibit some extreme features that are reflected in their bangbang outcomes. We thus move on by investigating what the outcomes of coreperiphery models become when we account for a more complete and richer description of the spatial aspects that these models aim at describing. We conclude by suggesting new lines of research.
Solving the robots gathering problem
 Proc. 30th International Colloquium on Automata, Languages and Programming (ICALP 2003), LNCS 2719
, 2003
"... Abstract. Consider a set of n> 2 simple autonomous mobile robots (decentralized, asynchronous, no common coordinate system, no identities, no central coordination, no direct communication, no memory of the past, deterministic) moving freely in the plane and able to sense the positions of the othe ..."
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Cited by 53 (6 self)
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Abstract. Consider a set of n> 2 simple autonomous mobile robots (decentralized, asynchronous, no common coordinate system, no identities, no central coordination, no direct communication, no memory of the past, deterministic) moving freely in the plane and able to sense the positions of the other robots. We study the primitive task of gathering them at a point not fixed in advance (Gathering Problem). In the literature, most contributions are simulationvalidated heuristics. The existing algorithmic contributions for such robots are limited to solutions for n ≤ 4 or for restricted sets of initial configurations of the robots. In this paper, we present the first algorithm that solves the Gathering Problem for any initial configuration of the robots. 1
Gathering Autonomous Mobile Robots
 In Proc. SIROCCO
, 2002
"... We study the problem of coordinating a set of autonomous mobile robots that can freely move in a twodimensional plane; in particular, we want them to gather at a point not fixed in advance (GATHERING PROBLEM). We introduce a model of weak robots (decentralized, asynchronous, no common knowledge, no ..."
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Cited by 42 (6 self)
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We study the problem of coordinating a set of autonomous mobile robots that can freely move in a twodimensional plane; in particular, we want them to gather at a point not fixed in advance (GATHERING PROBLEM). We introduce a model of weak robots (decentralized, asynchronous, no common knowledge, no identities, no central coordination, no direct communication, oblivious) which can observe the set of all points in the plane which are occupied by other robots. Based on this observation, a robot uses a deterministic algorithm to compute a destination, and moves there. We prove that these robots are too weak to gather at a point in finite time. Therefore, we strengthen them with the ability to detect whether more than one robot is at a point (multiplicity). We analyze the GATHERING PROBLEM for these stronger robots. We show that the problem is still unsolvable if there are only two robots in the system. For 3 and 4 robots, we give algorithms that solve the GATHERING PROBLEM. For more than 4 robots, we present an algorithm that gathers the robots in finite time if they are not in a specific symmetric configuration at the beginning (biangular configuration). We show how to solve such initial configurations separately. However, the general solution of the GATHERING PROBLEM remains an open problem.
Parameterizationfree Projection for Geometry Reconstruction
"... We introduce a Locally Optimal Projection operator (LOP) for surface approximation from pointset data. The operator is parameterization free, in the sense that it does not rely on estimating a local normal, fitting a local plane, or using any other local parametric representation. Therefore, it can ..."
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Cited by 39 (7 self)
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We introduce a Locally Optimal Projection operator (LOP) for surface approximation from pointset data. The operator is parameterization free, in the sense that it does not rely on estimating a local normal, fitting a local plane, or using any other local parametric representation. Therefore, it can deal with noisy data which clutters the orientation of the points. The method performs well in cases of ambiguous orientation, e.g., if two folds of a surface lie near each other, and other cases of complex geometry in which methods based upon local plane fitting may fail. Although defined by a global minimization problem, the method is effectively local, and it provides a second order approximation to smooth surfaces. Hence allowing good surface approximation without using any explicit or implicit approximation space. Furthermore, we show that LOP is highly robust to noise and outliers and demonstrate its effectiveness by applying it to raw scanned data of complex shapes.
The geometric median on Riemannian manifolds with application to robust atlas estimation
 NEUROIMAGE 45 (2009) S143–S152
, 2009
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Faster Minimization of Linear Wirelength for Global Placement
 IEEE Transactions on ComputerAided Design
, 1997
"... A linear wirelength objective more e#ectively captures timing, congestion, and other global placement considerations than a squared wirelength objective. The GORDIANL cell placement tool #16# minimizes linear wirelength by #rst approximating the linear wirelength objectiveby a modi#ed squared wirel ..."
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Cited by 35 (9 self)
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A linear wirelength objective more e#ectively captures timing, congestion, and other global placement considerations than a squared wirelength objective. The GORDIANL cell placement tool #16# minimizes linear wirelength by #rst approximating the linear wirelength objectiveby a modi#ed squared wirelength objective, then executing the following loop # #1# minimize the current objective to yield some approximate solution, and #2# use the resulting solution to construct a more accurate objective#until the solution converges. In this paper, we #rst show that the GORDIANL loop can be viewed as a special case of a new algorithm that generalizes a 1937 iteration due to Weiszfeld #19#. Speci# cally,we formulate the Weiszfeld iteration using a regularization parameter to control the tradeo# between convergence and solution accuracy; the GORDIANL iteration is equivalent to setting this regularization parameter to zero. Other novel numerical methods described in the paper, the Primal Newton it...