the image foresting transform theory algorithms and applications the image foresting transform ift is a graph based approach to the design of image processing operators based on connectivity it naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other we give here a precise definition of the ift and a procedure to compute it a generalization of dijkstra s algorithm with a proof of correctness we also discuss implementation issues and illustrate the use of the ift in a few applications

estimating the parameters of 3 p weibull distribution using particle swarm optimization the weibull distribution is used lifetime distributions in reliability engineering the estimation of the parameters of this distribution is essential maximum likelihood ml estimation is a common method for parameter estimation the working principle of ml estimation based on maximizing the likelihood function we used particle swarm optimization as parameter estimation tool in 3 p weibull weibull distribution plays an important role in failure distribution modeling in reliability studies and the estimation of its parameters is essential in the most real applications maximum likelihood ml estimation is a common method which is usually used to elaborate on the parameter estimation the maximizing likelihood function formed for the parameter estimation of a three parameter 3 p weibull distribution is a quite difficult problem hence the heuristic approaches must be used to discover good solutions particle swarm optimization pso is a population based heuristic optimization technique developed from swarm intelligence the performance of pso greatly depends on its control parameters such as inertia weight and acceleration coefficients slightly different parameter settings may direct to very different performance this paper gives a comprehensive investigation of different pso variants according to inertia weight procedures acceleration coefficients particle size and search space in the parameter estimation problem of 3 p weibull distribution three explanatory numerical examples are given to show that pso approach variants exhibit a rapid convergence to the maximum value of the likelihood function in less iteration provides accurate estimates and pso method is satisfactory for the parameter estimation of the 3 p weibull distribution

on the existence of strong nash equilibria this paper investigates the existence of strong nash equilibria sne in continuous and convex games we show that the concavity and an additional condition on payoff functions together with the compactness of strategy space permit the existence of strong nash equilibria these conditions are satisfied in many economic games and are quite simple to check we also characterize the existence of sne by providing a necessary and sufficient condition moreover we suggest a procedure that can be used to efficiently compute strong nash equilibrium the result is illustrated with an application to an economy with multilateral environmental externalities and to the simple oligopoly static model

application of gradient descent method to the sedimentary grain size distribution fitting existence of a least squares solution for a sum of several weighted normal functions is proved the gradient descent gd method is used to fit the measured data i e the laser grain size distribution of the sediments with a sum of three weighted lognormal functions the numerical results indicate that the gd method is not only easy to operate but also could effectively optimize the parameters of the fitting function with the error decreasing steadily meanwhile the overall fitting results are satisfactory as a new way of data fitting the gd method could also be used to solve other optimization problems

unsupervised hierarchical image segmentation through fuzzy entropy maximization we present an unsupervised multilevel segmentation scheme for automatically segmenting grayscale and color images fuzzy 2 partition entropy is combined with graph cut to form a bi level segmentation operator that splits a given region into 2 parts based on both global optimal threshold and local spatial coherence a multilevel segmentation scheme iteratively performs on selected regions and color channels producing a coarse to fine hierarchy of segments the presented algorithm is evaluated using the berkeley segmentation database and achieves competitive results compared with the state of the art methods the fuzzy c partition entropy has been widely adopted as a global optimization technique for finding the optimal thresholds when performing multilevel gray image segmentation nevertheless existing fuzzy c partition entropy approaches generally have two limitations i e partition number cneeds to be manually tuned for different input and the methods can process grayscale images only to address these two limitations an unsupervised multilevel segmentation algorithm is presented in this paper the core step of our algorithm is a bi level segmentation operator which uses binary graph cuts to maximize both fuzzy 2 partition entropy and segmentation smoothness by iteratively performing this bi level segmentation operator multilevel image segmentation is achieved in a hierarchical manner starting from the input color image our algorithm first picks the color channel that can best segment the image into two labels and then iteratively selects channels to further split each labels until convergence the experimental results demonstrate the presented hierarchical segmentation scheme can efficiently segment both grayscale and color images quantitative evaluations over classic gray images and the berkeley segmentation database show that our method is comparable to the state of the art multi scale segmentation methods yet has the advantage of being unsupervised efficient and easy to implement

an efficient bi convex fuzzy variational image segmentation method abstract image segmentation is an important and well known ill posed inverse problem in computer vision it is a process of assigning a label to each pixel in a digital image so that pixels with the same label have similar characteristics chan vese model which belongs to partial differential equation approaches has been widely used in image segmentation tasks chan vese model has to optimize a non convex problem it usually converges to local minima furthermore the length penalty item which is critical to the final results of chan vese model makes the model be sensitive to parameter settings and costly in computation in order to overcome these drawbacks a novel bi convex fuzzy variational image segmentation method is proposed it is unique in two aspects 1 introducing fuzzy logic to construct a bi convex object function in order to simplify the procedure of finding global optima and 2 efficiently combining the length penalty item and the numerical remedy method to get better results and to bring robustness to parameter settings and greatly reduce computation costs experiments on synthetic natural medical and radar images have visually or quantitatively validated the superiorities of the proposed method compared with five state of the art algorithms

virtual implementation in nash equilibrium the two person case needs to be considered separately we provide a complete characterization of virtually implementable two person social choice functions while not all two person social choice functions are virtually implementable our necessary and sufficient condition is simple this contrasts with the rather complex necessary and sufficient conditions for exact implementation we show how our results can be extended to implementation in strict nash equilibrium and coalition proof nash equilibrium to social choice correspondences which map from cardinal preference profiles to lotteries and to environments with a continuum of pure alternatives

snakes active contour models a snake is an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges snakes are active contour models they lock onto nearby edges localizing them accurately scale space continuation can be used to enlarge the capture region surrounding a feature snakes provide a unified account of a number of visual problems including detection of edges lines and subjective contours motion tracking and stereo matching we have used snakes successfully for interactive interpretation in which user imposed constraint forces guide the snake near features of interest

virtual bayesian implementation allowing for incomplete information this paper characterizes the social choice functions that can be approximated by the equilibrium outcomes of a mechanism incentive compatibility is necessary and almost sufficient for virtual bayesian implementability in conjunction with a second condition bayesian incentive consistency incentive compatibility is also sufficient this new condition is weak under standard topological and informational assumptions it is satisfied by every social choice function the type sets of the agents are taken to be arbitrary possibly infinite measurable spaces an example shows that there are virtually in fact exactly bayesian implementable social choice functions that are not virtually implementable in iteratively undominated strategies

existence results for special nonlinear total least squares problem in this paper we prove an existence theorem for a special nonlinear total least squares problem we show that the optimal parameters of the generalized logistic function exist in the sense of total least squares provided the data satisfy the chebyshev s inequality

virtual implementation in iteratively undominated strategies complete information we investigate the implementation of social choice functions in complete information environments we consider social choice functions scf s which map from a finite set of preference profiles to lotteries over alternatives and require virtual implementation in iteratively undominated strategies an scf x is virtually implementable in iteratively undominated strategies if for all e 0 there exists an scf y which is e close to x that is for all preference profiles x and y map to lotteries which are within e of one another and which is exactly implementable in iteratively undominated strategies under very weak domain restrictions we show that if there are three or more players any scf is virtually implementable in iteratively undominated strategies a noteworthy feature of our constructions is that we only employ finite mechanisms as a corollary we obtain virtual implementation in pure and mixed strategy nash equilibrium using well behaved in particular finite mechanisms the literature on implementation in nash equilibrium and its refinements is compromised by its reliance on game forms with unnatural features for example integer games or modulo constructions with mixed strategies arbitrarily excluded in contrast our results allow for mixed strategies and do not rely on mechanisms with obviously suspicious features

improved bounds for matroid partition and intersection algorithms we give bounds on total lengths of augmenting paths in standard implementations of the matroid partition and intersection algorithms and indicate how these observations can be used to improve the running times in certain applications for example for the matroid intersection algorithm on two r by n matrices the running time is shown to be o nr 2 log r we also give improved versions of the two algorithms when running times are measured in terms of calls to an independence oracle for example there is a matroid partition algorithm on o n n element matroids using o n 2 5 independence tests

generating epsilon efficient solutions in multiobjective programming scalarization approaches to purposely generating e efficient solutions of multiobjective programs are investigated and a generic procedure for computing these solutions is proposed and illustrated with an example real life decision making situations in which the solutions are of significance are described

atomic routing games on maximum congestion we study atomic routing games on networks in which players choose a path with the objective of minimizing the maximum congestion along the edges of their path the social cost is the global maximum congestion over all edges in the network we show that the price of stability is 1 the price of anarchy poa is determined by topological properties of the network in particular poa o logn where is the length of the longest path in the player strategy sets and n is the size of the network further 1 poa c 2 log2n where is the length of the longest cycle in the network and c is a constant

active contours driven by local image fitting energy a new region based active contour model that embeds the image local information is proposed in this paper by introducing the local image fitting lif energy to extract the local image information our model is able to segment images with intensity inhomogeneities moreover a novel method based on gaussian filtering for variational level set is proposed to regularize the level set function it can not only ensure the smoothness of the level set function but also eliminate the requirement of re initialization which is very computationally expensive experiments show that the proposed method achieves similar results to the lbf local binary fitting energy model but it is much more computationally efficient in addition our approach maintains the sub pixel accuracy and boundary regularization properties

estimating the parameters of weibull distribution using simulated annealing algorithm weibull distribution plays an important role in failure distribution modeling in reliability studies it is a hard work to estimate the parameters of weibull distribution this distribution has three parameters but for simplicity a parameter is omitted and as a result the estimation of the others will be easily done when the three parameter distribution is of interest the estimation procedure will be quite boring maximum likelihood estimation is a good method which is usually used to elaborate on the parameter estimation the likelihood function formed for the parameter estimation of a three parameter weibull distribution is very hard to maximize many researchers have studied this maximization problem in this paper we have briefly discussed this problem and proposed a new approach based on the simulated algorithm to solve that

a successive approach to compute the bounded pareto front of practical multiobjective optimization problems this work focuses on the generation of the pareto front for practical applications such as analog circuit sizing the pareto front is the solution to a multiobjective optimization problem it shows all possible optimal compromises between conflicting design objectives performances in technical applications the pareto front is usually bounded it is shown in this work that the boundary of the pareto front consists of so called trade off limits if certain criteria are fulfilled these trade off limits can be computed by solving a multiobjective optimization problem for a subset of the performances a novel approach to pareto optimization is presented which is based on the generation of the trade off limits the pareto front for any number of performances is built up systematically in a successive manner pareto optimal solutions on the boundary of a pareto front are generated first in the next step the inner part of the pareto front is populated with pareto optimal solutions this is the first approach which generates a discretized approximation that shows the total extent of a bounded pareto front without requiring to calculate suboptimal solutions it requires that the pareto front exists is bounded and connected with no gaps and that the set of weakly pareto optimal solutions and pareto optimal solutions are equal

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

necessary and sufficient karush kuhn tucker conditions for multiobjective markov chains optimality the solution concepts proposed in this paper follow the karush kuhn tucker kkt conditions for a pareto optimal solution in finite time ergodic and controllable markov chains multi objective programming problems in order to solve the problem we introduce the tikhonov s regularizator for ensuring the objective function is strict convex then we consider the c variable method for introducing equality constraints that guarantee the result belongs to the simplex and satisfies ergodicity constraints lastly we restrict the cost functions allowing points in the pareto front to have a small distance from one another the computed image points give a continuous approximation of the whole pareto surface the constraints imposed by the c variable method make the problem computationally tractable and the restriction imposed by the small distance change ensures the continuation of the pareto front we transform the multi objective nonlinear problem into an equivalent nonlinear programming problem by introducing the lagrange function multipliers as a result we obtain that the objective function is strict convex the inequality constraints are continuously differentiable and the equality constraint is an affine function under these settings the kkt optimality necessary and sufficient conditions are elicited naturally a numerical example is solved for providing the basic techniques to compute the pareto optimal solutions by resorting to kkt conditions

inefficiency of nash equilibria it is shown that nash equilibria of smooth games generally tend to be inefficient in the pareto sense

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

computing the strong nash equilibrium for markov chains games present a novel method for finding the strong nash equilibriumintroduce tikhonov s regularization method to guarantee convergence to a single equilibriumprove the existence of a unique strong nash equilibriumpropose the euler method with penalty function for converging to the strong nash equilibriumpresent the convergence conditions of the step size parameter and tikhonov s regularizator in this paper we present a novel method for finding the strong nash equilibrium the approach consists on determining a scalar and the corresponding strategies d fixing specific bounds min and max that belong to the pareto front bounds correspond to restrictions imposed by the player over the pareto front that establish a specific decision area where the strategies can be selected we first exemplify the pareto front of the game in terms of a nonlinear programming problem adding a set of linear constraints for the markov chain game based on the c variable method for solving the strong nash equilibrium problem we propose to employ the euler method and a penalty function with regularization the tikhonov s regularization method is used to guarantee the convergence to a single strong equilibrium point then we established a nonlinear programming method to solve the successive single objective constrained problems that arise from taking the regularized functional of the game to achieve the goal we implement the gradient method to solve the first order optimality conditions starting from an utopia point pareto optimal point given an initial of the individual objectives the method solves an optimization problem adding linear constraints required to find the optimal strong strategy d we show that in the regularized problem the functional of the game decrease and finally converges proving the existence and uniqueness of strong nash equilibrium pareto optimal nash equilibrium in addition we present the convergence conditions and compute the estimated rate of convergence of variables and corresponding to the step size parameter of the gradient method and the tikhonov s regularization respectively moreover we provide all the details needed to implement the method in an efficient and numerically stable way the usefulness of the method is successfully demonstrated by a numerical example

on the statistical analysis of dirty pictures may 7th 1986 professor a f m smith in the chair summary a continuous two dimensional region is partitioned into a fine rectangular array of sites or pixels each pixel having a particular colour belonging to a prescribed finite set the true colouring of the region is unknown but associated with each pixel there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model the aim is to reconstruct the true scene with the additional knowledge that pixels close together tend to have the same or similar colours in this paper it is assumed that the local characteristics of the true scene can be represented by a nondegenerate markov random field such information can be combined with the records by bayes theorem and the true scene can be estimated according to standard criteria however the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field thus a simple iterative method of reconstruction is proposed which does not depend on these large scale characteristics the method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field some complications including parameter estimation are discussed potential applications are mentioned briefly

estimating the parameters of 3 p weibull distribution through differential evolution the weibull distribution is used lifetime distributions in reliability engineering the estimation of the parameters of this distribution is essential maximum likelihood ml estimation is a common method for parameter estimation the working principle of ml estimation based on maximizing the likelihood function we used differential evolution algorithm as parameter estimation tool in 3 p weibull the weibull distribution is one of the most widely used lifetime distributions in reliability engineering and the estimation of the parameters of this distribution is essential in the most real applications maximum likelihood ml estimation is a common method which is usually used to elaborate on the parameter estimation the working principle of ml estimation method based on maximizing the established likelihood function and maximizing this function formed for the parameter estimation of a three parameter 3 p weibull distribution is a quite challenging problem in this paper this problem have been briefly discussed and an effective approach based on the differential evolution de algorithm operators is proposed in order to enhance the estimation accuracy with less system resources three explanatory numerical examples are given to show that de approach which requires significantly less cpu time and exhibits a rapid convergence to the maximum value of the likelihood function in less iterations provides accurate estimates and is satisfactory for the parameter estimation of the 3 p weibull distribution

computation and efficiency of potential function minimizers of combinatorial congestion games we study the computation and efficiency of pure nash equilibria in combinatorial congestion games where the strategies of each player i are given by the binary vectors of a polytope p i our main goal is to understand which structural properties of such polytopal congestion games enable us to derive an efficient equilibrium selection procedure to compute pure nash equilibria with attractive social cost approximation guarantees to this aim we identify two general properties of the underlying aggregation polytope p n sum i p i which are sufficient for our results to go through namely the integer decomposition property idp and the box totally dual integrality property box tdi our main results for polytopal congestion games satisfying idp and box tdi are as follows i we show that pure nash equilibria can be computed in polynomial time in fact we obtain this result through a general framework for separable convex function minimization which might be of independent interest ii we bound the inefficiency of these equilibria and show that this provides a tight bound on the price of stability iii we also prove that these results extend to strong equilibria for the bottleneck variant of polytopal congestion games examples of polytopal congestion games satisfying idp and box tdi include common source network congestion games symmetric totally unimodular congestion games non symmetric matroid congestion games and symmetric matroid intersection congestion games in particular r arborescences and strongly base orderable matroids

least squares fitting gompertz curve in this paper we consider the least squares ls fitting of the gompertz curve to the given nonconstant data pi ti yi i 1 m m 3 we give necessary and sufficient conditions which guarantee the existence of the ls estimate suggest a choice of a good initial approximation and give some numerical examples

an optimal graph theoretic approach to data clustering theory and its application to image segmentation a novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated the data to be clustered are represented by an undirected adjacency graph g with arc capacities assigned to reflect the similarity between the linked vertices clustering is achieved by removing arcs of g to form mutually exclusive subgraphs such that the largest inter subgraph maximum flow is minimized for graphs of moderate size approximately 2000 vertices the optimal solution is obtained through partitioning a flow and cut equivalent tree of g which can be efficiently constructed using the gomory hu algorithm 1961 however for larger graphs this approach is impractical new theorems for subgraph condensation are derived and are then used to develop a fast algorithm which hierarchically constructs and partitions a partially equivalent tree of much reduced size this algorithm results in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices the new clustering algorithm is applied to the image segmentation problem the segmentation is achieved by effectively searching for closed contours of edge elements equivalent to minimum cuts in g which consist mostly of strong edges while rejecting contours containing isolated strong edges this method is able to accurately locate region boundaries and at the same time guarantees the formation of closed edge contours

synthetic aperture imaging using pixel labeling via energy minimization synthetic aperture imaging using an array of cameras which has become popular recently can easily handle the occlusion problem by seeing through occluders unfortunately the resulting image is still blurry because it combines information not only from the region of interest but also from the occluding regions removing the blurriness of synthetic aperture images has become a challenging task for many computer vision applications in this paper we propose a novel method to improve the image quality of synthetic aperture imaging using energy minimization unlike the conventional synthetic aperture imaging method which averages images from all the camera views we reformulate the problem as a labeling problem in particular we use the energy minimization method to label each pixel in each camera view to decide whether or not it belongs to an occluder after that the focusing at the desired depth is by averaging pixels that are not labeled as occluder the experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method as well as its improved versions which are simply dim and blur occluders in the resulting image to the best of our knowledge our proposed method is the first one for improving the results of synthetic aperture image without using a training set from the input sequence as well it is the first method that makes no assumptions on whether or not the objects in the scenes are static

maximum likelihood discriminant analysis on the plane using a markovian model of spatial context abstract a method is presented for utilising spatial information in performing discriminant analysis on multivariate data at each point on a regular lattice as for example with landsat the spatial information is modelled as a particularly simple markov random field the discriminant analysis is based on the maximum likelihood method the results are compared with alternatives by monte carlo simulation and seem to be encouraging

image segmentation with ratio cut this paper proposes a new cost function cut ratio for segmenting images using graph based methods the cut ratio is defined as the ratio of the corresponding sums of two different weights of edges along the cut boundary and models the mean affinity between the segments separated by the boundary per unit boundary length this new cost function allows the image perimeter to be segmented guarantees that the segments produced by bipartitioning are connected and does not introduce a size shape smoothness or boundary length bias the latter allows it to produce segmentations where boundaries are aligned with image edges furthermore the cut ratio cost function allows efficient iterated region based segmentation as well as pixel based segmentation these properties may be useful for some image segmentation applications while the problem of finding a minimum ratio cut in an arbitrary graph is np hard one can find a minimum ratio cut in the connected planar graphs that arise during image segmentation in polynomial time while the cut ratio alone is not sufficient as a baseline method for image segmentation it forms a good basis for an extended method of image segmentation when combined with a small number of standard techniques we present an implemented algorithm for finding a minimum ratio cut prove its correctness discuss its application to image segmentation and present the results of segmenting a number of medical and natural images using our techniques

variational and pca based natural image segmentation this paper introduces a novel variational segmentation method within the fuzzy framework which solves the problem of segmenting multi region color scale images of natural scenes we call this kind of images as natural images the advantages of our segmentation method are 1 by introducing the pca descriptors our segmentation model can partition color texture images better than classical variational based segmentation models 2 to preserve geometrical structure of each fuzzy membership function we propose a nonconvex regularization term in our model 3 to solve our segmentation model more efficiently we design a fast iteration algorithm in which we integrate the augmented lagrange multiplier method and the iterative reweighting we conduct comprehensive experiments to measure the segmentation performance of our model in terms of visual evaluation and we also demonstrate the efficiency of the corresponding algorithm in terms of a variety of quantitative indices the proposed model achieves better segmentation results compared with some other well known models such as the level set model and the fuzzy region competition model while the proposed algorithm is much more efficient than the algorithm of the state of the art natural image segmentation model

towards 3 query locally decodable codes of subexponential length a q query locally decodable code ldc encodes an n bit message x as an n bit codeword c x such that one can probabilistically recover any bit xi of the message by querying only q bits of the codeword c x even after some constant fraction of codeword bits has been corrupted we give new constructions of three query ldcs of vastly shorter length than that of previous constructions specifically given any mersenne prime p e 2t 1 we design three query ldcs of length n e exp o n1 t for every n based on the largest known mersenne prime this translates to a length of less than exp o n10 7 compared to exp o n1 2 in the previous constructions it has often been conjectured that there are infinitely many mersenne primes under this conjecture our constructions yield three query locally decodable codes of length n e exp no 1 log log n for infinitely many n we also obtain analogous improvements for private information retrieval pir schemes we give 3 server pir schemes with communication complexity of o n10 7 to access an n bit database compared to the previous best scheme with complexity o n1 5 25 assuming again that there are infinitely many mersenne primes we get 3 server pir schemes of communication complexity no 1 log logn for infinitely many n previous families of ldcs and pir schemes were based on the properties of low degree multivariate polynomials over finite fields our constructions are completely different and are obtained by constructing a large number of vectors in a small dimensional vector space whose inner products are restricted to lie in an algebraically nice set

efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization the fuzzy c partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation however it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced in this paper an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation the efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique finally instead of performing thresholding for each pixel independently the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region the final segmentation results is computed using graph cut which produces smooth segmentation results the experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches

analysis of field experiments by least squares smoothing summary assuming a smooth trend plus independent error model for the environmental effects in the yields of a field plot experiment least squares smoothing methods are developed to estimate both the treatment effects and the unknown trend treatment estimates are closely related to those resulting from a generalized least squares analysis in which the covariance structure for the environmental effects has a particular form however the main emphases are on the accuracy of treatment estimates under a fixed smooth trend plus error model and the exploratory power of the basic method to isolate trend effects of unknown form although the detailed development is for the one dimensional case generalizations of the smoothness concept and extensions to two dimensions are also discussed application of the basic method is illustrated on three data sets and the results compared with other analyses

image segmentation by iterated region merging with localized graph cuts this paper presents an iterated region merging based graph cuts algorithm which is a novel extension of the standard graph cuts algorithm graph cuts addresses segmentation in an optimization framework and finds a globally optimal solution to a wide class of energy functions however the extraction of objects in a complex background often requires a lot of user interaction the proposed algorithm starts from the user labeled sub graph and works iteratively to label the surrounding un segmented regions in each iteration only the local neighboring regions to the labeled regions are involved in the optimization so that much interference from the far unknown regions can be significantly reduced meanwhile the data models of the object and background are updated iteratively based on high confident labeled regions the sub graph requires less user guidance for segmentation and thus better results can be obtained under the same amount of user interaction experiments on benchmark datasets validated that our method yields much better segmentation results than the standard graph cuts and the grabcut methods in either qualitative or quantitative evaluation

on the value of coordination in network design we study network design games where n self interested agents have to form a network by purchasing links from a given set of edges we consider shapley cost sharing mechanisms that split the cost of an edge in a fair manner among the agents using the edge it is well known that the price of anarchy of these games is as high as n another line of research has focused on evaluating the price of stability i e the cost of the best nash equilibrium relative to the social optimum in this paper we investigate to which extent coordination among agents can improve the quality of solutions we resort to the concept of strong nash equilibria which were introduced by aumann and are resilient to deviations by coalitions of agents we analyze the price of anarchy of strong nash equilibria and develop lower and upper bounds for unweighted and weighted games in both directed and undirected graphs these bounds are tight or nearly tight for many scenarios it shows that by using coordination the price of anarchy drops from linear to logarithmic bounds we complement these results by also proving the first superconstant lower bound on the price of stability of standard equilibria without coordination in undirected graphs more specifically we show a lower bound of omega log w log log w for weighted games where w is the total weight of all the agents this almost matches the known upper bound of o log w our results imply that for most settings the worst case performance ratios of strong coordinated equilibria are essentially always as good as the performance ratios of the best equilibria achievable without coordination these settings include unweighted games in directed graphs as well as weighted games in both directed and undirected graphs

on the impact of combinatorial structure on congestion games we study the impact of combinatorial structure in congestion games on the complexity of computing pure nash equilibria and the convergence time of best response sequences in particular we investigate which properties of the strategy spaces of individual players ensure a polynomial convergence time we show that if the strategy space of each player consists of the bases of a matroid over the set of resources then the lengths of all best response sequences are polynomially bounded in the number of players and resources we also prove that this result is tight that is the matroid property is a necessary and sufficient condition on the players strategy spaces for guaranteeing polynomial time convergence to a nash equilibrium in addition we present an approach that enables us to devise hardness proofs for various kinds of combinatorial games including first results about the hardness of market sharing games and congestion games for overlay network design our approach also yields a short proof for the pls completeness of network congestion games in particular we show that network congestion games are pls complete for directed and undirected networks even in case of linear latency functions

a coalitional extension of generalized fuzzy games abstract in this paper we give a coalitional extension of generalized fuzzy games and introduce the notion of coalitional fuzzy equilibria we first prove the existence of coalitional fuzzy equilibria in our model as an application we further show the existence theorem of strong fuzzy equilibria for fuzzy games

graph cuts and efficient n d image segmentation combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics this paper focusses on possibly the simplest application of graph cuts segmentation of objects in image data despite its simplicity this application epitomizes the best features of combinatorial graph cuts methods in vision global optima practical efficiency numerical robustness ability to fuse a wide range of visual cues and constraints unrestricted topological properties of segments and applicability to n d problems graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes geodesic active contours and level sets the segmentation energies optimized by graph cuts combine boundary regularization with region based properties in the same fashion as mumford shah style functionals we present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s t graph cuts after the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in boykov and jolly 2001 this idea was widely studied in computer vision and graphics communities we provide links to a large number of known extensions based on iterative parameter re estimation and learning multi scale or hierarchical approaches narrow bands and other techniques for demanding photo video and medical applications

computing pure nash and strong equilibria in bottleneck congestion games bottleneck congestion games properly model the properties of many real world network routing applications they are known to possess strong equilibria a strengthening of nash equilibrium to resilience against coalitional deviations in this paper we study the computational complexity of pure nash and strong equilibria in these games we provide a generic centralized algorithm to compute strong equilibria which has polynomial running time for many interesting classes of games such as e g matroid or single commodity bottleneck congestion games in addition we examine the more demanding goal to reach equilibria in polynomial time using natural improvement dynamics using unilateral improvement dynamics in matroid games pure nash equilibria can be reached efficiently in contrast computing even a single coalitional improvement move in matroid and single commodity games is strongly np hard in addition we establish a variety of hardness results and lower bounds regarding the duration of unilateral and coalitional improvement dynamics they continue to hold even for convergence to approximate equilibria

estimation of parameters of the shifted gompertz distribution using least squares maximum likelihood and moments methods nonlinear least squares procedures for estimating the parameters of the shifted gompertz distribution are proposed simulation studies are carried out to compare weighted and unweighted least squares methods the maximum likelihood method and method of moments this work concludes that least squares methods via weighting factors to estimate the parameters of this probability distribution give a better performance than unweighted least squares methods showing the importance of weighting factors besides results of this simulation study show that a good performance is obtained using the maximum likelihood method and the estimators obtained with more bias are those of the method of moments

a multiscale image segmentation method this paper presents a novel image segmentation framework that combines image segmentation and feature extraction into a unified model the proposed model consists of two parts the segmentation part and the multiscale decomposition part in the model the segmentation part relies on the image intensities in the regions of interest while the multiscale decomposition part depends on the features in different scales the multiscale decomposition facilitates the process of segmentation since the region of interest can be easily detected from a proper scale the total variation projection regularization tvpr is used to preserve geometric shape of the segmented regions according to the geometric significance of tvpr parameters an adaptive tvpr parameters selection method is presented and edges of different region can be well preserved the proposed method is able to deal with intensity inhomogeneities and mixed noises often occurred in real world images which present challenges in image segmentation numerical examples on synthetic and real images are given to demonstrate the effectiveness of the proposed method this paper proposes a novel image segmentation framework a multiscale image segmentation method is presented within our framework total variation projection regularization tvpr is used to the proposed model we present an adaptive tvpr parameters selection method for image segmentation the experimental results show the effectiveness of the proposed method

a statistical model for positron emission tomography abstract positron emission tomography pet still in its research stages is a technique that promises to open new medical frontiers by enabling physicians to study the metabolic activity of the body in a pictorial manner much as in x ray transmission tomography and other modes of computerized tomography the quality of the reconstructed image in pet is very sensitive to the mathematical algorithm to be used for reconstruction in this article we tailor a mathematical model to the physics of positron emissions and we use the model to describe the basic image reconstruction problem of pet as a standard problem in statistical estimation from incomplete data we describe various estimation procedures such as the maximum likelihood ml method using the em algorithm the method of moments and the least squares method a computer simulation of a pet experiment is then used to demonstrate the ml and the least squares reconstructions the main purposes of this article are to report on what we believe is an

nonlinear weighted least squares estimation of a three parameter weibull density with a nonparametric start this paper is concerned with the parameter estimation problem for the three parameter weibull density which is widely employed as a model in reliability and lifetime studies our approach is a combination of nonparametric and parametric methods the basic idea is to start with an initial nonparametric density estimate which needs to be as good as possible and then apply the nonlinear least squares method to estimate the unknown parameters as a main result a theorem on the existence of the least squares estimate is obtained some simulations are given to show that our approach is satisfactory if the initial density is of good enough quality

on nonlinear weighted least squares estimation of bass diffusion model the bass model is one of the most well known and widely used models of first purchase demand estimation of its parameters has been approached in the literature by various techniques the focus of this paper is on a nonlinear weighted least squares fitting approach as a main result two theorems on the existence of the least squares estimate are obtained one of them gives necessary and sufficient conditions which guarantee the existence of the least squares estimate some numerical experiments are given to illustrate the efficiency of our approach

a note on maximum likelihood estimation for regression models using grouped data summary the estimation of parameters for a class of regression models using grouped or censored data is considered it is shown that with a simple reparameterization some commonly used distributions such as the normal and extreme value result in a log likelihood which is concave with respect to the transformed parameters apart from its theoretical implications for the existence and uniqueness of maximum likelihood estimates this result suggests minor changes to some commonly used algorithms for maximum likelihood estimation from grouped data two simple examples are given

existence of strong equilibrium in discontinuous games abstract in this paper we give necessary and sufficient conditions for the existence of strong equilibria in games with non ordered preferences our result does not require continuous preferences and in the case of normal form games quasi concavity of payoff functions is not needed the result has been obtained by using the ky fan minimax inequality examples illustrate our conditions in some economic situations and compare the result with the previous literature