texture discrimination based upon an assumed stochastic texture model a new approach to texture discrimination is described this approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier the construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided the efficacy of this approach is demonstrated through experimental results obtained with simulated texture data a comparison is provided with more conventional texture discriminants under identical conditions the implications to texture discrimination in realworld imagery are discussed

some aspects of the spline smoothing approach to non parametric regression curve fitting non parametric regression using cubic splines is an attractive flexible and widely applicable approach to curve estimation although the basic idea was formulated many years ago the method is not as widely known or adopted as perhaps it should be the topics and examples discussed in this paper are intended to promote the understanding and extend the practicability of the spline smoothing methodology particular subjects covered include the basic principles of the method the relation with moving average and other smoothing methods the automatic choice of the amount of smoothing and the use of residuals for diagnostic checking and model adaptation the question of providing inference regions for curves and for relevant properties of curves is approached via a finite dimensional bayesian formulation

shortest path and minimum delay algorithms in networks with time dependent edge length in this paper the shortest path problem in networks in which the delay or weight of the edges changes with time according to arbitrary functions is considered algorithms for finding the shortest path and minimum delay under various waiting constraints are presented and the properties of the derived path are investigated it is shown that if departure time from the source node is unrestricted then a shortest path can be found that is simple and achieves a delay as short as the most unrestricted path in the case of restricted transit it is shown that there exist cases in which the minimum delay is finite but the path that achieves it is infinite

a study of causality structure and dynamics in industrial electricity consumption based on granger network abstract based on industrial electricity consumption we model industrial networks by granger causality method and mst minimum spanning tree and then further stick onto an industrial coupling mechanism from energy consumption perspective first we construct granger causality networks of five provinces in south china of gd gx gz hn and yn based on their industrial electricity consumption data and we demonstrate from a network topology perspective the distribution of weight of links of all industrial electricity consumption granger causality networks approximately follows power law distribution revealing a phenomenon that few industries may bring a tremendous influence on the rest moreover correlation analysis between weight and degree of a node shows that in most granger causality networks both span and strength of influence of a given industry will significantly increase further we analyze the relationship between the thresholds of granger causality significance and density of corresponding networks results show gd and hn could be classified into a group with relatively greater global differentiation in industries and unbalanced industrial development however gx gz and yn are grouped as second cluster with relatively balanced industrial development furthermore using chu liu edmonds mst algorithm we extract graphs of msts or maximal cliques from industrial electricity consumption granger causality networks and research on energy transmission structure feedback loop and bootstrap reliability by analyzing msts we find that only gd gx and yn can be extracted with mst graphs and capture the probable transmission routes of key nodes besides we illustrate all three mst graphs are involved with feedback loops structures with various characteristics gx has complete feed forward section feed back section and feedback loop section yn has only feed forward section and feedback loop section gd has multiple feedback loops section finally we conduct bootstrap reliability tests on msts results show branches of reliability of gd gx and yn are decentralized while those feedback loops sections are only concentrated on highly reliable branches as neither hn nor gz can be extracted with mst graphs we characterize them by maximal cliques it is demonstrated that both industrial systems of hn and gz are significantly grouped into two independent branches and those internal industries inside independent branches are highly correlated however from perspectives of whole south china the causal relationship is only converged on a few of industries for regions without mst graphs like hn and gz the reliability of links is also greater than that of gd gx and yn

nonparametric detection of correlated errors summary in regression problems it is hard to detect correlated errors since the errors are not observed in this paper a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced it turns out that the first order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information

formal language constrained path problems given an alphabet sigma a directed graph g whose edges are weighted and sigma labeled and a formal language l subseteq sigma the formal language constrained shortest simple path problem consists of finding a shortest simple path p in g complying with the additional constraint that l p in l here l p denotes the unique word obtained by concatenating the sigma labels of the edges along the path p the main contributions of this paper include the following we show that the formal language constrained shortest path problem is solvable efficiently in polynomial time when l is restricted to be a context free language cfl when l is specified as a regular language we provide algorithms with improved space and time bounds in contrast we show that the problem of finding a simple path between a source and a given destination is np hard even when l is restricted to fixed simple regular languages and to very simple classes of graphs e g complete grids for the class of treewidth bounded graphs we show that i the problem of finding a regular language constrained simple path between source and destination is solvable in polynomial time and ii the extension to finding cfl constrained simple paths is np complete our results extend the previous results in siam j comput 24 1995 pp 1235 1258 proceedings of the 76th annual meeting of the transportation research board 1997 and proceedings of the 9th acm sigact sigmod sigart symposium on database systems 1990 pp 230 242 several additional extensions and applications of our results in the context of transportation problems are presented for instance as a corollary of our results we obtain a polynomial time algorithm for the best k similar path problem studied in proceedings of the 76th annual meeting of the transportation reasearch board 1997 the previous best algorithm was given by proceedings of the 76th annual meeting of the transportation research board 1997 and takes exponential time in the worst case

a jump model for credit default swaps with hierarchical clustering abstract the return time series of credit default swaps cds display possibly the highest excess kurtosis and skewness of any asset class in capital markets capturing this requires a departure from classical modeling techniques here the premise is that cds prices jump and those jumps cluster initially a model is proposed that is driven by a diffusion and a hawkes process to reproduce the clustering of shocks the new element of the model is the use of hierarchical clustering to calibrate the distribution of the jumps partitioning the shocks identifies shifts in the volatility regime of the cds through time which are shown to consistently correspond to both systemic and idiosyncratic market events a piecewise definition of a double exponential distribution for the jumps corresponding to the identified clusters is employed to replicate the regime switching subsequently this demonstrably improves the accuracy of the calibration the model is fitted to all cds with more than 2000 closing prices from 2001 to 2014 a total of 1652 individual time series for each credit a peak over threshold approach is applied to the return series to separate the data into those returns that can broadly be classified as log normal and those that can be characterized as jumps by explicitly linking the results to market traded volumes the results indicate a clear split of the cds into liquid and illiquid names a key finding is that for the illiquid cds it is difficult to justify the continuous component of the model for the low liquidity names the hawkes process is shown to capture the price action without a diffusion a markov chain is then introduced into the simulation to replicate the clustering and regime switching

multi component image segmentation in homogeneous regions based on description length minimization application to speckle poisson and bernoulli noise in this article a minimum description length mdl criterion adapted to independent multi component image segmentation into homogeneous regions is proposed this approach based on a deformable polygonal grid allows us to segment noisy multi component images perturbed with spatially independent speckle poisson or bernoulli noise the advantages of using such a multi component approach rather than a mono component one is demonstrated on synthetic and real images this segmentation method is also applicable to multi component images whose components do not follow the same noise statistics or have not been previously registered

segmentation through variable order surface fitting the solution of the segmentation problem requires a mechanism for partitioning the image array into low level entities based on a model of the underlying image structure a piecewise smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete noiseless image reconstruction based on the extracted functions and regions surface curvature sign labeling provides an initial coarse image segmentation which is refined by an iterative region growing method based on variable order surface fitting experimental results show the algorithm s performance on six range images and three intensity images

a region based segmentation method for multichannel image data abstract segmentation has become a widely used tool in modern image analysis typical methods are highly nonlinear and often ad hoc and none so far have proved accessible to detailed statistical analysis even by asymptotic approximations can such methods be any good this article describes a segmentation procedure for multichannel image data and attempts to develop an understanding of its statistical performance characteristics in terms of nonparametric regression the procedure is based on a recursive merging algorithm defined via a nested sequence of discretizations of the image domain a cross validation rule with a near neighbor block replacement strategy is proposed for selecting the final segmentation model idealized numerical simulation experiments are used to evaluate the rate of convergence of the mean square error of estimation and also to study the efficiency of the cross validation technique interestingly the results show that the rate of convergence tends to decrease as the degree of smo

complex network based time series analysis abstract recent works show that complex network theory may be a powerful tool in time series analysis we propose in this paper a reliable procedure for constructing complex networks from the correlation matrix of a time series an original stock time series the corresponding return series and its amplitude series are considered the degree distribution of the original series can be well fitted with a power law while that of the return series can be well fitted with a gaussian function the degree distribution of the amplitude series contains two asymmetric gaussian branches reconstruction of networks from time series is a common problem in diverse research the proposed strategy may be a reasonable solution to this problem

a maximum likelihood approach to texture classification a new approach to texture classification is described which is based on measurements of the spatial gray level co occurrence probability matrix this approach can make use of assumed stochastic models for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier the efficacy of the approach is demonstrated through experimental results obtained with real world texture data

minimum norm quadratic estimation of spatial variograms abstract the estimation of spatial variograms a measure of spatial correlation is a critical problem in the implementation of kriging a method for interpolating random fields we consider the use of minimum norm quadratic estimators of the variogram when it is specified up to a finite number of linear parameters we investigate the asymptotic behavior of such estimators for gaussian processes as the number of observations within some bounded region increases the basic conclusion is that we can estimate consistently those functions of the parameters and only those functions that have a nonnegligible impact asymptotically on the kriging procedure in general the behavior of the variogram over relatively short distances is the only aspect of the variogram that is asymptotically important as an example consider a gaussian process z on the real line with unknown constant mean and where is known as the semivariogram of the process and is a finite vector of unknown parameters suppose for 0

characterization and estimation of two dimensional arma models a class of finite order two dimensional autoregressive moving average arma is introduced that can represent any process with rational spectral density in this model the driving noise is correlated and need not be gaussian currently known classes of arma models or ar models are shown to be subsets of the above class the three definitions of markov property are discussed and the class of arma models are precisely stated which have the noncausal and semicausal markov property without imposing any specific boundary conditions next two approaches are considered to estimate the parameters of a model to fit a given image the first method uses only the empirical correlations and involves the solution of linear equations the second method is the likelihood approach since the exact likelihood function is difficult to compute we resort to approximations suggested by the toroidal models numerical experiments compare the quality of the two estimation schemes finally the problem of synthesizing a texture obeying an arma model is considered

asymptotic confidence regions for kernel smoothing of a varying coefficient model with longitudinal data abstract we consider the estimation of the k 1 dimensional nonparametric component t of the varying coefficient model y t x t t t e t based on longitudinal observations yij x i tij tij i 1 n j 1 ni where tij is the jth observed design time point t of the ith subject and yij and x i tij are the real valued outcome and r k 1 valued covariate vectors of the ith subject at tij the subjects are independently selected but the repeated measurements within subject are possibly correlated asymptotic distributions are established for a kernel estimate of t that minimizes a local least squares criterion these asymptotic distributions are used to construct a class of approximate pointwise and simultaneous confidence regions for t applying these methods to an epidemiological study we show that our procedures are useful for predicting cd4 t helper lymphocytes cell changes among hiv human immunodeficiency virus infected persons the finite sample properties of o

residual variance and residual pattern in nonlinear regression summary a nonparametric estimator of residual variance in nonlinear regression is proposed it is based on local linear fitting asymptotically the estimator has a small bias but a larger variance compared with the parametric estimator in linear regression finite sample properties are investigated in a simulation study including a comparison with other nonparametric estimators the method is also useful for spotting heteroscedasticity and outliers in the residuals at an early stage of the data analysis a further application is checking the fit of parametric models this is illustrated for longitudinal growth data

a model for segmentation and analysis of noisy images abstract this article proposes a statistical model for image generation that provides automatic segmentation of images into intensity differentiated regions and facilitates the quantitative assessment of uncertainty associated with identified image features the model is specified hierarchically within the bayesian paradigm at the lowest level in the hierarchy a gibbs distribution is used to specify a probability distribution on the space of all possible partitions of the discretized image scene an important feature of this distribution is that the number of partitioning elements or image regions is not specified a priori at higher levels in the hierarchical specification random variables representing emission intensities are associated with regions and pixels observations are assumed to be generated from exponential family models centered about these values

dynamic asset trees and black monday abstract the minimum spanning tree based on the concept of ultrametricity is constructed from the correlation matrix of stock returns the dynamics of this asset tree can be characterised by its normalised length and the mean occupation layer as measured from an appropriately chosen centre called the central node we show how the tree length shrinks during a stock market crisis black monday in this case and how a strong reconfiguration takes place resulting in topological shrinking of the tree

a characteristic point based fuzzy inference classifier by a closeness matrix in this paper a characteristic point based fuzzy inference classifier cpfic is proposed to perform two class classification through fuzzy interpolation a subset of classified samples can be taken as representatives of all samples they are called characteristic points cps a closeness matrix representing the closeness of two samples in a same class is proposed in selecting cps by solving a number of constrained minimizations the cpfic is systematically built experiments were conducted on four classification problems with known bayes errors two benchmark classification problems and a real world application used in our research the cpfic performs well in accuracy evaluations in all the seven experiments the summarizing abilities from the cps into the linguistic descriptions of the fuzzy rule bases were also demonstrated in these examples

a bayesian segmentation methodology for parametric image models region based image segmentation methods require some criterion for determining when to merge regions this paper presents a novel approach by introducing a bayesian probability of homogeneity in a general statistical context the authors approach does not require parameter estimation and is therefore particularly beneficial for cases in which estimation based methods are most prone to error when little information is contained in some of the regions and therefore parameter estimates are unreliable the authors apply this formulation to three distinct parametric model families that have been used in past segmentation schemes implicit polynomial surfaces parametric polynomial surfaces and gaussian markov random fields the authors present results on a variety of real range and intensity images

bandwidth choice for nonparametric regression this paper is concerned with the problem of choosing a bandwidth parameter for nonparametric regression we analyze a tapered fourier series estimate and discuss the relationship of this estimate to a kernel estimate we first consider a method based on an unbiased estimate of mean square error and show that the bandwidth thus chosen is asymptotically optimal other methods are examined as well and are shown to be asymptotically equivalent a small simulation shows however that for small or moderate sample size the methods perform quite differently

estimation of heteroscedasticity in regression analysis consider the regression model y i g t i varepsilon i 1 leq i leq n with nonrandom design variables t i and measurements y i for the unknown regression function g cdot we assume that the data are heteroscedastic i e e varepsilon 2 i sigma 2 i not equiv operatorname const and investigate how to estimate sigma 2 i if sigma 2 i sigma 2 t i with a smooth function sigma 2 cdot initial estimators tilde sigma 2 i can be improved by kernel smoothers and the resulting class of estimators is shown to be uniformly consistent these estimates can be used to improve the estimation of the regression function g itself in parametric and nonparametric models further applications are suggested

a directed hypergraph model for random time dependent shortest paths abstract we consider routing problems in dynamic networks where arc travel times are both random and time dependent the problem of finding the best route to a fixed destination is formulated in terms of shortest hyperpaths on a suitable time expanded directed hypergraph the latter problem can be solved in linear time with respect to the size of the hypergraph for several definitions of hyperpath length different criteria for ranking routes can be modeled by suitable definitions of hyperpath length we also show that the problem becomes intractable if a constraint on the route structure is imposed

segmenting images corrupted by correlated noise image segmentation is fundamental to many image analysis problems it aims to partition a digital image into a set of nonoverlapping homogeneous regions the main contribution of this paper is the development of a new segmentation procedure which is designed to segment images corrupted by correlated noise this new segmentation procedure is based on rissanen s minimum description length mdl principle and consists of two components 1 an mdl based criterion in which the best segmentation is defined as its minimizer and 2 a merging algorithm which attempts to locate this minimizer the performance of this procedure is illustrated via a simulation study with promising results

region competition unifying snakes region growing and bayes mdl for multiband image segmentation we present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition this algorithm is derived by minimizing a generalized bayes minimum description length mdl criterion using the variational principle the algorithm is guaranteed to converge to a local minimum and combines aspects of snakes balloons and region growing the classic snakes balloons and region growing algorithms can be directly derived from our approach we provide theoretical analysis of region competition including accuracy of boundary location criteria for initial conditions and the relationship to edge detection using filters it is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images color images and texture images the novel color model allows us to eliminate intensity gradients and shadows thereby obtaining segmentation based on the albedos of objects it also helps detect highlight regions

improving the accuracy of volumetric segmentation using pre processing boundary detection and image reconstruction the concentration edge detection and gegenbauer image reconstruction methods were previously shown to improve the quality of segmentation in magnetic resonance imaging in this study these methods are utilized as a pre processing step to the weibull e sd field segmentation it is demonstrated that the combination of the concentration edge detection and gegenbauer reconstruction method improves the accuracy of segmentation for the simulated test data and real magnetic resonance images used in this study

shortest paths in a network with time dependent flow speeds abstract the model and solution of the shortest path problem on time dependent networks where the travel time of each link depends on the time interval violate the non passing property of real phenomena calculating the solution of the problem needs much more computation and memory than the general shortest path problem here we suggest a new model for time dependent networks where the flow speed of each link depends on the time interval and a solution algorithm modified from dijkstra s label setting algorithm we present numerical examples and computational experiments showing that the solution of our model satisfies the non passing property and is stable to the variance of the time interval length solving the shortest path of our model needs just a little more computation and memory than the general shortest path problem

kernel regression estimation using repeated measurements data abstract the estimation of growth curves has been studied extensively in parametric situations here we consider the nonparametric estimation of an average growth curve suppose that there are observations from several experimental units each following the regression model y xi f xj e j 1 n where e1 e n are correlated zero mean errors and 0 x1 xn 1 are fixed constants we study some of the properties of a kernel estimator of f x asymptotic and finite sample results concerning the mean squared error of the estimator are obtained in particular the influence of correlation on the bandwidth minimizing mean squared error is discussed a data based method for selecting the bandwidth is illustrated in a data analysis most previous research on kernel regression estimators has involved uncorrelated errors we investigate how dependence of the errors changes the behavior of a kernel estimator our theorems concerning the asymptotic mean squared error show that the estimator cannot be consistent un

texture synthesis using 2 d noncausal autoregressive models the purpose of this paper is to illustrate the usefulness of two dimensional noncausal autoregressive ncar models for the synthesis of textures these models characterize the gray level at a pixel as a linear combination of gray levels at nearby locations in all directions and an additive white noise variable we first show that the class of ncar models is capable of generating a wide variety of image patterns posessing the local replication attribute an essential ingredient of many natural textures it is also shown that the theoretical variograms of many ncar models possess an oscillatory behavior a characteristic of the variograms of many natural textures next we give experimental results of synthesis of 64 64 textures resembling several real textures in the brodatz album the synthetic textures generated by 16 parameter ncar models retain most the visual characteristics of the original textures the variograms of the original and synthetic textures are also similar

testing for trends in high dimensional time series the article considers statistical inference for trends of high dimensional time series based on a modified l2 distance between parametric and nonparametric trend estimators we propose a de diagon

strong consistency of least squares estimators in regression with correlated disturbances this note considers under minimal assumptions the strong consistency of least squares estimates in regression with correlated errors

a minimum description length based image segmentation procedure and its comparison with a cross validation based segmentation procedure abstract image segmentation is a very important problem in image analysis as quite often it is a key component of a good practical solution to a real life imaging problem it aims to partition a digital image into a set of nonoverlapping homogeneous regions one approach to segmenting an image is to fit a piecewise constant function to the image and define the segmentation by the discontinuity points of the fitted function the article s first contribution is to present a new and automatic segmentation procedure which follows this piecewise constant function fitting approach this procedure is based on rissanen s minimum description length mdl principle and consists of two components a an mdl based criterion in which the best segmentation i e the best fitted piecewise constant function is defined as its minimizer and b a fast merging algorithm that attempts to locate this minimizer as a second contribution the new mdl based procedure is compared with a cross validation based segmentation

constructing simple stable descriptions for image partitioning a new formulation of the image partitioning problem is presented construct a complete and stable description of an image in terms of a specified descriptive language that is simplest in the sense of being shortest we show that a descriptive language limited to a low order polynomial description of the intensity variation within each region and a chain code like description of the region boundaries yields intuitively satisfying partitions for a wide class of images the advantage of this formulation is that it can be extended to deal with subsequent steps of the image understanding problem or to deal with other attributes such as texture in a natural way by augmenting the descriptive language experiments performed on a variety of both real and synthetic images demonstrate the superior performance of this approach over partitioning techniques based on clustering vectors of local image attributes and standard edge detection techniques

a study of hierarchical structure on south china industrial electricity consumption correlation abstract based on industrial electricity consumption data of five southern provinces of china from 2005 to 2013 we study the industrial correlation mechanism with mst minimal spanning tree and ht hierarchical tree models first we comparatively analyze the industrial electricity consumption correlation structure in pre crisis and after crisis period using mst model and bootstrap technique of statistical reliability test of links results exhibit that all industrial electricity consumption trees of five southern provinces of china in pre crisis and after crisis time are in formation of chain and the center periphery structure of those chain like trees is consistent with industrial specialization in classical industrial chain theory additionally the industrial structure of some provinces is reorganized and transferred in pre crisis and after crisis time further the comparative analysis with hierarchical tree and bootstrap technique demonstrates that as for both observations of gd and overall nf the industrial electricity consumption correlation is non significant clustered in pre crisis period whereas it turns significant clustered in after crisis time therefore we propose that in perspective of electricity consumption their industrial structures are directed to optimized organization and global correlation finally the analysis of distance of hts verifies that industrial reorganization and development may strengthen market integration coordination and correlation of industrial production except gz other four provinces have a shorter distance of industrial electricity consumption correlation in after crisis period revealing a better performance of regional specialization and integration

detecting dependencies in smooth regression models summary a class of simple estimators for the correlation of the errors in nonparametric regression models is proposed for the fixed design case these estimators are based on squared differences of various spans of the data and are consistent so long as the regression function is lipschitz continuous the limiting distribution is established and applied to derive asymptotic global and local tests for different null hypotheses of uncorrelatedness the finite sample behaviour of estimators and tests is investigated in a monte carlo study a more general class of estimators using generalized difference schemes is also introduced

modeling of two dimensional random fields by parametric cepstrum a method is presented for parametric modeling of stationary random fields the class of parametric models considered allows the most general elliptic field and by linear constraints can include such special cases as isotropic quarter plane and separable fields the technique based on the cepstrum has the great advantage of requiring only the use of fast fourier transforms in the fitting process thus unlike the fitting of two dimensional autoregressions no iteration is necessary other advantages are that any wiener filters constructed from the fitted spectrum are guaranteed to be stable and that the spectrum is guaranteed to be positive statistical tests for determining various special types of field from data are developed the choice of model order is discussed as well

flows over time in time varying networks optimality conditions and strong duality abstract there has been much research on network flows over time due to their important role in real world applications this has led to many results but the more challenging continuous time model still lacks some of the key concepts and techniques that are the cornerstones of static network flows the aim of this paper is to advance the state of the art for dynamic network flows by developing the continuous time analogues of the theory for static network flows specifically we make use of ideas from the static case to establish a reduced cost optimality condition a negative cycle optimality condition and a strong duality result for a very general class of network flows over time

dynamics of market correlations taxonomy and portfolio analysis the time dependence of the recently introduced minimum spanning tree description of correlations between stocks called the asset tree has been studied in order to reflect the financial market taxonomy the nodes of the tree are identified with stocks and the distance between them is a unique function of the corresponding element of the correlation matrix by using the concept of a central vertex chosen as the most strongly connected node of the tree an important characteristic is defined by the mean occupation layer during crashes due to the strong global correlation in the market the tree shrinks topologically and this is shown by a low value of the mean occupation layer the tree seems to have a scale free structure where the scaling exponent of the degree distribution is different for business as usual and crash periods the basic structure of the tree topology is very robust with respect to time we also point out that the diversification aspect of portfolio optimization results in the fact that the assets of the classic markowitz portfolio are always located on the outer leaves of the tree technical aspects such as the window size dependence of the investigated quantities are also discussed

estimation of planar curves surfaces and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation the author addresses the problem of parametric representation and estimation of complex planar curves in 2 d surfaces in 3 d and nonplanar space curves in 3 d curves and surfaces can be defined either parametrically or implicitly with the latter representation used here a planar curve is the set of zeros of a smooth function of two variables x y a surface is the set of zeros of a smooth function of three variables x y z and a space curve is the intersection of two surfaces which are the set of zeros of two linearly independent smooth functions of three variables x y z for example the surface of a complex object in 3 d can be represented as a subset of a single implicit surface with similar results for planar and space curves it is shown how this unified representation can be used for object recognition object position estimation and segmentation of objects into meaningful subobjects that is the detection of interest regions that are more complex than high curvature regions and hence more useful as features for object recognition

hierarchy in picture segmentation a stepwise optimization approach a segmentation algorithm based on sequential optimization which produces a hierarchical decomposition of the picture is presented the decomposition is data driven with no restriction on segment shapes it can be viewed as a tree where the nodes correspond to picture segments and where links between nodes indicate set inclusions picture segmentation is first regarded as a problem of piecewise picture approximation which consists of finding the partition with the minimum approximation error then picture segmentation is presented as an hypothesis testing process which merges only segments that belong to the same region a hierarchical decomposition constraint is used in both cases which results in the same stepwise optimization algorithm at each iteration the two most similar segments are merged by optimizing a stepwise criterion the algorithm is used to segment a remote sensing picture and illustrate the hierarchical structure of the picture

a restricted dynamic programming heuristic algorithm for the time dependent traveling salesman problem abstract dynamic programming dp algorithms for the traveling salesman problem tsp can easily incorporate time dependent travel times time windows and precedence relationships which present difficulties for algorithms based on linear or nonlinear programming formulations and for many tsp heuristics however exact dp algorithms for the tsp have exponential storage and computational time requirements and can solve only very small problems we present a restricted dp heuristic a generalization of the nearest neighbor heuristic that can include all the above considerations but solves much larger problems the heuristic cannot guarantee optimality because only a userspecified specified number of partial tours is retained at each stage in this paper the heuristic is implemented for the time dependent traveling salesman problem and is tested on a personal computer on randomly generated problems the quality of the solution improves on average as more computational time is permitted