multiple paths extraction in images using a constrained expanded trellis single shortest path extraction algorithms have been used in a number of areas such as network flow and image analysis in image analysis shortest path techniques can be used for object boundary detection crack detection or stereo disparity estimation sometimes one needs to find multiple paths as opposed to a single path in a network or an image where the paths must satisfy certain constraints in this paper we propose a new algorithm to extract multiple paths simultaneously within an image using a constrained expanded trellis cet for feature extraction and object segmentation we also give a number of application examples for our multiple paths extraction algorithm

finite element methods for active contour models and balloons for 2 d and 3 d images the use of energy minimizing curves known as snakes to extract features of interest in images has been introduced by kass witkin and terzopoulos 1987 a balloon model was introduced by cohen 1991 as a way to generalize and solve some of the problems encountered with the original method a 3 d generalization of the balloon model as a 3 d deformable surface which evolves in 3 d images is presented it is deformed under the action of internal and external forces attracting the surface toward detected edgels by means of an attraction potential we also show properties of energy minimizing surfaces concerning their relationship with 3 d edge points to solve the minimization problem for a surface two simplified approaches are shown first defining a 3 d surface as a series of 2 d planar curves then after comparing finite element method and finite difference method in the 2 d problem we solve the 3 d model using the finite element method yielding greater stability and faster convergence this model is applied for segmenting magnetic resonance images

optimum replacement of a system subject to shocks a mathematical lemma we consider a system that is subject to shocks that occur randomly over time we assume that the system must be replaced after it has functioned for a random length of time i a moment of a major failure that is a stopping time with respect to the process nt t 0 that models the number of shocks occurring in 0 t it may however be economical to replace the system at time mint i prior to its failure for some fixed but optimally chosen t costs are due to shocks maintenance and replacement we introduce an optimality criterion based on cost considerations which we use to find an optimal replacement time t0 in the process we prove a general mathematical lemma that helps in arriving at the optimal replacement time for general classes of processes nt stopping times i and the cost structures involved we also give a discrete time version of the lemma

δ shock model based on polya process and its optimal replacement policy abstract shock models are of great interest in engineering reliability among the others the shock model has been widely studied in the literature in this model the system breaks down due to the arrivals of two successive shocks which are too close to each other that is the system fails when the time between two consecutive shocks falls below a fixed threshold in the literature the shock model has been mostly studied by assuming that shocks arrive according to a renewal process so that the interarrival times between shocks are independent and identically distributed in the current paper we consider the case when the shock arrival process is described by a polya process which has dependent interarrival times in particular we obtain survival function and mean lifetime of the system and study the optimal replacement policy for the shock model based on polya process

adjoint active surfaces for localization and imaging this paper addresses the problem of localizing and segmenting regions embedded within a surrounding medium by characterizing their boundaries as opposed to imaging the entirety of the volume active surfaces are used to directly reconstruct the shape of the region of interest we describe the procedure for finding the optimal surface which is computed iteratively via gradient descent that exploits the sensitivity of an error minimization functional to changes of the active surface in doing so we introduce the adjoint model to compute the sensitivity and in this respect the method shares common ground with several other disciplines such as optimal control finally we illustrate the proposed active surface technique in the framework of wave propagation governed by the scalar helmholtz equation potential applications include electromagnetics acoustics geophysics nondestructive testing and medical imaging

manifold regularization a geometric framework for learning from labeled and unlabeled examples we propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution we focus on a semi supervised framework that incorporates labeled and unlabeled data in a general purpose learner some transductive graph learning algorithms and standard methods including support vector machines and regularized least squares can be obtained as special cases we use properties of reproducing kernel hilbert spaces to prove new representer theorems that provide theoretical basis for the algorithms as a result in contrast to purely graph based approaches we obtain a natural out of sample extension to novel examples and so are able to handle both transductive and truly semi supervised settings we present experimental evidence suggesting that our semi supervised algorithms are able to use unlabeled data effectively finally we have a brief discussion of unsupervised and fully supervised learning within our general framework

active contours without edges we propose a new model for active contours to detect objects in a given image based on techniques of curve evolution mumford shah 1989 functional for segmentation and level sets our model can detect objects whose boundaries are not necessarily defined by the gradient we minimize an energy which can be seen as a particular case of the minimal partition problem in the level set formulation the problem becomes a mean curvature flow like evolving the active contour which will stop on the desired boundary however the stopping term does not depend on the gradient of the image as in the classical active contour models but is instead related to a particular segmentation of the image we give a numerical algorithm using finite differences finally we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable also the initial curve can be anywhere in the image and interior contours are automatically detected

a variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity level set functions based variational image segmentation models provide reliable methods to capture boundaries of objects regions in a given image provided that the underlying intensity has homogeneity the case of images with essentially piecewise constant intensities is satisfactorily dealt with in the well known work of chan vese 2001 and its many variants however for images with intensity inhomogeneity or multiphases within the foreground of objects such models become inadequate because the detected edges and even phases do not represent objects and are hence not meaningful to deal with such problems in this paper we have proposed a new variational model with two fitting terms based on regions and edges enhanced quantities respectively from multiplicative and difference images tests and comparisons will show that our new model outperforms two previous models both synthetic and real life images are used to illustrate the reliability and advantages of our new model graphical abstractdisplay omitted highlightswe model image segmentation problem using product and difference images the convex formulation of the model is also presented the proposed model takes care of the minute details and inhomogeneity in an image this model is compared with lcv and fgm of the active contour model the competing energies are qualitatively tested by using jaccord similarity index

threshold dynamics for the piecewise constant mumford shah functional we propose an efficient algorithm for minimizing the piecewise constant mumford shah functional of image segmentation it is based on the threshold dynamics of merriman bence and osher for evolving an interface by its mean curvature we show that a very fast minimization can be achieved by alternating the solution of a linear parabolic partial differential equation and simple thresholding

an energy based three dimensional segmentation approach for the quantitative interpretation of electron tomograms electron tomography allows for the determination of the three dimensional structures of cells and tissues at resolutions significantly higher than that which is possible with optical microscopy electron tomograms contain in principle vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles the development of reliable quantitative approaches for the analysis of features in tomograms is an important problem and a challenging prospect due to the low signal to noise ratios that are inherent to biological electron microscopic images this is in part a consequence of the tremendous complexity of biological specimens we report on a new method for the automated segmentation of hiv particles and selected cellular compartments in electron tomograms recorded from fixed plastic embedded sections derived from hiv infected human macrophages individual features in the tomogram are segmented using a novel robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features the optimization is carried out in a transformed spherical domain with the center an interior point of the particle of interest providing a proper setting for the fast and accurate minimization of the segmentation energy this method provides tools for the semi automated detection and statistical evaluation of hiv particles at different stages of assembly in the cells and presents opportunities for correlation with biochemical markers of hiv infection the segmentation algorithm developed here forms the basis of the automated analysis of electron tomograms and will be especially useful given the rapid increases in the rate of data acquisition it could also enable studies of much larger data sets such as those which might be obtained from the tomographic analysis of hiv infected cells from studies of large populations

technical efficiency estimation with multiple inputs and multiple outputs using regression analysis regression and linear programming provide the basis for popular techniques for estimating technical efficiency regression based approaches are typically parametric and can be both deterministic or stochastic where the later allows for measurement error in contrast linear programming models are nonparametric and allow multiple inputs and outputs the purported disadvantage of the regression based models is the inability to allow multiple outputs without additional data on input prices in this paper deterministic cross sectional and stochastic panel data regression models that allow multiple inputs and outputs are developed notably technical efficiency can be estimated using regression models characterized by multiple input multiple output environments without input price data we provide multiple examples including a monte carlo analysis

random walks on graphs for salient object detection in images we formulate the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph the seed labeled nodes are first detected using markov random walks performed on two different graphs that represent the image while the global properties of the image are computed from the random walk on a complete graph the local properties are computed from a sparse k regular graph the most salient node is selected as the one which is globally most isolated but falls on a locally compact object a few background nodes and salient nodes are further identified based upon the random walk based hitting time to the most salient node the salient nodes and the background nodes will constitute the labeled nodes a new graph representation of the image that represents the saliency between nodes more accurately the pop out graph model is computed further based upon the knowledge of the labeled salient and background nodes a semisupervised learning technique is used to determine the labels of the unlabeled nodes by optimizing a smoothness objective label function on the newly created pop out graph model along with some weighted soft constraints on the labeled nodes

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

globally minimal surfaces by continuous maximal flows in this paper we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction we present a solution simulating a continuous maximal flow by a novel system of partial differential equations existing methods are either grid biased graph based methods or suboptimal active contours and surfaces the solution simulates the flow of an ideal fluid with isotropic velocity constraints velocity constraints are defined by a metric derived from image data an auxiliary potential function is introduced to create a system of partial differential equations it is proven that the algorithm produces a globally maximal continuous flow at convergence and that the globally minimal surface may be obtained trivially from the auxiliary potential the bias of minimal surface methods toward small objects is also addressed an efficient implementation is given for the flow simulation the globally minimal surface algorithm is applied to segmentation in 2d and 3d as well as to stereo matching results in 2d agree with an existing minimal contour algorithm for planar images results in 3d segmentation and stereo matching demonstrate that the new algorithm is robust and free from grid bias

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

interactive image segmentation using probabilistic hypergraphs this paper introduces a novel interactive framework for segmenting images using probabilistic hypergraphs which model the spatial and appearance relations among image pixels the probabilistic hypergraph provides us a means to pose image segmentation as a machine learning problem in particular we assume that a small set of pixels which are referred to as seed pixels are labeled as the object and background the seed pixels are used to estimate the labels of the unlabeled pixels by learning on a hypergraph via minimizing a quadratic smoothness term formed by a hypergraph laplacian matrix subject to the known label constraints we derive a natural probabilistic interpretation of this smoothness term and provide a detailed discussion on the relation of our method to other hypergraph and graph based learning methods we also present a front to end image segmentation system based on the proposed method which is shown to achieve promising quantitative and qualitative results on the commonly used grabcut dataset

three dimensional elastic matching of volumes registering volumes that have been deformed with respect to each other involves recovery of the deformation a 3 d elastic matching algorithm has been developed to use surface information for registering volumes surface extraction is performed in two steps extraction of contours in 2 d image planes using active contours and forming triangular patch surface models from the stack of 2 d contours one volume is modeled as being deformed with respect to another goal volume correspondences between surfaces in the two image volumes are used to warp the deformed volume towards its goal this process of contour extraction surface formation and matching and warping is repeated a number of times with decreasing image volume stiffness as the iterations continue the stretched volume is refined towards its goal volume registration examples of deformed volumes are presented

group specific stochastic production frontier models with parametric specifications this paper develops a stochastic frontier model that not only focuses more on group specific temporal variations in technical efficiency rather than individual temporal variations but also allows for a parametric function of the time varying coefficient of the efficiency factor we derived the concentrated least squares estimator and its asymptotic properties when applied to the penn world data set the group specific models yield much more variation in the temporal patterns of efficiency across countries this application demonstrates the feasibility of applying a group specific stochastic frontier model with a parametric function of temporal pattern to a real empirical analysis

random walks for image segmentation a novel method is proposed for performing multilabel interactive image segmentation given a small number of pixels with user defined or predefined labels one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels by assigning each pixel to the label for which the greatest probability is calculated a high quality image segmentation may be obtained theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits this algorithm is formulated in discrete space i e on a graph using combinatorial analogues of standard operators and principles from continuous potential theory allowing it to be applied in arbitrary dimension on arbitrary graphs

minimal surfaces based object segmentation a geometric approach for 3d object segmentation and representation is presented the segmentation is obtained by deformable surfaces moving towards the objects to be detected in the 3d image the model is based on curvature motion and the computation of surfaces with minimal areas better known as minimal surfaces the space where the surfaces are computed is induced from the 3d image volumetric data in which the objects are to be detected the model links between classical deformable surfaces obtained via energy minimization and intrinsic ones derived from curvature based flows the new approach is stable robust and automatically handles changes in the surface topology during the deformation

signal matching through scale space given a collection of similar signals that have been deformed with respect to each other the general signal matching problem is to recover the deformation we formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term the minimization reduces to a dynamic system governed by a set of coupled first order differential equations the dynamic system finds an optimal solution at a coarse scale and then tracks it continuously to a fine scale among the major themes in recent work on visual signal matching have been the notions of matching as constrained optimization of variational surface reconstruction and of coarse to fine matching our solution captures these in a precise succinct and unified form results are presented for one dimensional signals a motion sequence and a stereo pair

differential and numerically invariant signature curves applied to object recognition we introduce a new paradigm the differential invariant signature curve or manifold for the invariant recognition of visual objects a general theorem of e cartan implies that two curves are related by a group transformation if and only if their signature curves are identical the important examples of the euclidean and equi affine groups are discussed in detail secondly we show how a new approach to the numerical approximation of differential invariants based on suitable combination of joint invariants of the underlying group action allows one to numerically compute differential invariant signatures in a fully group invariant manner applications to a variety of fundamental issues in vision including detection of symmetries visual tracking and reconstruction of occlusions are discussed

a generalized model for determining optimal number of minimal repairs before replacement abstract a generalized model for determining optimal number of minimal repairs before replacement is introduced which incorporates minimal repair replacement and general random repair cost the expected cost rate is obtained it is shown that the optimal number n which minimizes the cost rate is given by a unique solution of the equation under certain conditions various special cases are considered

regularization of inverse visual problems involving discontinuities inverse problems such as the reconstruction problems that arise in early vision tend to be mathematically ill posed through regularization they may be reformulated as well posed variational principles whose solutions are computable standard regularization theory employs quadratic stabilizing functionals that impose global smoothness constraints on possible solutions discontinuities present serious difficulties to standard regularization however since their reconstruction requires a precise spatial control over the smoothing properties of stabilizers this paper proposes a general class of controlled continuity stabilizers which provide the necessary control over smoothness these nonquadratic stabilizing functionals comprise multiple generalized spline kernels combined with noncontinuous continuity control functions in the context of computational vision they may be thought of as controlled continuity constraints these generic constraints are applicable to visual reconstruction problems that involve both continuous regions and discontinuities for which global smoothness constraints fail

circular shortest path in images abstract shortest path algorithms have been used in a number of applications such as crack detection road or linear feature extraction in images there are applications where the starting and ending positions of the shortest path need to be constrained in this paper we present several new algorithms for the extraction of a circular shortest path in an image such that the starting and ending positions coincide the new algorithms we developed include multiple search algorithm image patching algorithm multiple backtracking algorithm the combination of image patching and multiple back tracking algorithm and approximate algorithm the typical running time of our circular shortest path extraction algorithm on a 256 256 image is about 0 3 s on a rather slow 85 mhz sun sparc computer a variety of real images for crack detection in borehole data and object boundary extraction have been tested and good results have been obtained

a binary level set model and some applications to mumford shah image segmentation in this paper we propose a pde based level set method traditionally interfaces are represented by the zero level set of continuous level set functions instead we let the interfaces be represented by discontinuities of piecewise constant level set functions each level set function can at convergence only take two values i e it can only be 1 or 1 thus our method is related to phase field methods some of the properties of standard level set methods are preserved in the proposed method while others are not using this new method for interface problems we need to minimize a smooth convex functional under a quadratic constraint the level set functions are discontinuous at convergence but the minimization functional is smooth we show numerical results using the method for segmentation of digital images

stabilization of parametric active contours using a tangential redistribution term depending on implementation active contours have been classified as geometric or parametric active contours parametric contours irrespective of representation are known to suffer from the problem of irregular bunching and spacing out of curve points during the curve evolution in a spline based implementation of active contours this leads to occasional formation of loops locally and subsequently the curve blows up due to instabilities in this paper we analyze the reason for this problem and propose a solution to alleviate the same we propose an ordinary differential equation ode for controlling the curve parametrization during evolution by including a tangential force we show that the solution of the proposed ode is bounded we demonstrate the effectiveness of the proposed method for segmentation and tracking tasks on closed as well as open contours

color image segmentation advances and prospects abstract image segmentation is very essential and critical to image processing and pattern recognition this survey provides a summary of color image segmentation techniques available now basically color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces therefore we first discuss the major segmentation approaches for segmenting monochrome images histogram thresholding characteristic feature clustering edge detection region based methods fuzzy techniques neural networks etc then review some major color representation methods and their advantages disadvantages finally summarize the color image segmentation techniques using different color representations the usage of color models for image segmentation is also discussed some novel approaches such as fuzzy method and physics based method are investigated as well

symmetry seeking models and 3d object reconstruction we propose models of 3d shape which may be viewed as deformable bodies composed of simulated elastic material in contrast to traditional purely geometric models of shape deformable models are active their shapes change in response to externally applied forces we develop a deformable model for 3d shape which has a preference for axial symmetry symmetry is represented even though the model does not belong to a parametric shape family such as generalized cylinders rather a symmetry seeking property is designed into internal forces that constrain the deformations of the model we develop a framework for 3d object reconstruction based on symmetry seeking models instances of these models are formed from monocular image data through the action of external forces derived from the data the forces proposed in this paper deform the model in space so that the shape of its projection into the image plane is consistent with the 2d silhouette of an object of interest the effectiveness of our approach is demonstrated using natural images

boundary vector field for parametric active contours snakes are widely used in the fields of computer vision and image processing there are a number of shortcomings of the traditional snakes including the capture range problem and the concave object extraction problem gradient vector flow snake enhances the concave object extraction capability however it suffers from high computational requirement in this paper we present a new external force which is generated by a novel interpolation scheme which reduces the computational requirement significantly and at the same time improves the capture range and concave object extraction capability

global regularizing flows with topology preservation for active contours and polygons active contour and active polygon models have been used widely for image segmentation in some applications the topology of the object s to be detected from an image is known a priori despite a complex unknown geometry and it is important that the active contour or polygon maintain the desired topology in this work we construct a novel geometric flow that can be added to image based evolutions of active contours and polygons in order to preserve the topology of the initial contour or polygon we emphasize that unlike other methods for topology preservation the proposed geometric flow continually adjusts the geometry of the original evolution in a gradual and graceful manner so as to prevent a topology change long before the curve or polygon becomes close to topology change the flow also serves as a global regularity term for the evolving contour and has smoothness properties similar to curvature flow these properties of gradually adjusting the original flow and global regularization prevent geometrical inaccuracies common with simple discrete topology preservation schemes the proposed topology preserving geometric flow is the gradient flow arising from an energy that is based on electrostatic principles the evolution of a single point on the contour depends on all other points of the contour which is different from traditional curve evolutions in the computer vision literature

image segmentation by map ml estimations image segmentation plays an important role in computer vision and image analysis in this paper image segmentation is formulated as a labeling problem under a probability maximization framework to estimate the label configuration an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori map estimation and the maximum likelihood ml estimation the map estimation problem is modeled with markov random fields mrfs and a graph cut algorithm is used to find the solution to the map estimation the ml estimation is achieved by computing the means of region features in a gaussian model our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance its results match image edges very well and are consistent with human perception comparing to six state of the art algorithms extensive experiments have shown that our algorithm performs the best

fine structured object segmentation via neighborhood propagation this paper presents a novel method for the challenging task of fine structured fs object segmentation the task is formulated as a label propagation problem on an affinity graph the proposed method has mainly three advantages first to enhance the completeness and connectivity of fs objects we introduce a novel neighborhood system combining both local and nonlocal connections with a robust scheme for edge weight calculation second appearance models are explicitly incorporated into the energy function as a term of region cost this helps to further preserve the connectivity of the fine parts for which the label information is hard to propagate correctly via neighboring pixels alone third the resulting energy minimization problem has a closed form solution with global optimum guaranteed showing an advantage over the fs object segmentation methods that suffer from np hardness to enrich the evaluation of fs object segmentation methods we created a new challenging data set it consists of 100 natural images involving diverse fs objects with accurately hand labeled ground truth extensive experimental results demonstrate that our method is effective in handling fs objects and achieves the state of the art performance highlightswe propose a method for fine structured object segmentation via label propagation a novel affinity graph combining local and nonlocal connections is constructed we incorporate appearance models as labeling preference for the label propagation the energy minimization is achieved with a globally optimal closed form solution extensive experimental results demonstrate the effectiveness of our method

b spline snakes a flexible tool for parametric contour detection we present a novel formulation for b spline snakes that can be used as a tool for fast and intuitive contour outlining we start with a theoretical argument in favor of splines in the traditional formulation by showing that the optimal curvature constrained snake is a cubic spline irrespective of the form of the external energy field unfortunately such regularized snakes suffer from slow convergence speed because of a large number of control points as well as from difficulties in determining the weight factors associated to the internal energies of the curve we therefore propose an alternative formulation in which the intrinsic scale of the spline model is adjusted a priori this leads to a reduction of the number of parameters to be optimized and eliminates the need for internal energies i e the regularization term in other words we are now controlling the elasticity of the spline implicitly and rather intuitively by varying the spacing between the spline knots the theory is embedded into a multiresolution formulation demonstrating improved stability in noisy image environments validation results are presented comparing the traditional snake using internal energies and the proposed approach without internal energies showing the similar performance of the latter several biomedical examples of applications are included to illustrate the versatility of the method

a stochastic production frontier model with group specific temporal variation in technical efficiency abstract the purpose of this study was to develop a stochastic frontier production model that relaxes the assumption of identical temporal patterns of technical inefficiency and the restriction of specific forms of temporal pattern without imposing other restrictions specifically the proposed model allows group specific patterns of temporal change in technical inefficiency this is a direct generalization of the model of lee and schmidt 1993 lee y h schmidt p 1993 a production frontier model with flexible temporal variation in technical inefficiency in fried h lovell c a k schmidt p eds the measurement of productive efficiency techniques and applications oxford university press oxford which imposed an identical temporal pattern of technical inefficiency on all firms when applied to indonesian rice farms the model is shown to be an improvement on the earlier model of lee and schmidt 1993 and the changes in the efficiency rankings of farms over time demonstrate the model s flexibility

geometrically induced force interaction for three dimensional deformable models in this paper we propose a novel 3 d deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions this external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient the evolution of the deformable model is solved using the level set method so that topological changes are handled automatically the relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves the geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries and it gives the deformable model a high invariancy in initialization configurations the voxel interactions across the whole image domain provide a global view of the object boundary representation giving the external force a long attraction range the bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross boundary initializations and facilitates the handling of weak edges and broken boundaries in addition we show that by enhancing the geometrical interaction field with a nonlocal edge preserving algorithm the new deformable model can effectively overcome image noise we provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images and show that the proposed method achieves significant improvements against existing image gradient techniques

anisotropic second and fourth order diffusion models based on convolutional virtual electric field for image denoising this paper proposes novel second order and fourth order anisotropic diffusion models for image denoising these two models are based on convolutional virtual electric field convef the proposed second order anisotropic diffusion model was introduced by introducing the convef into the perona malik p m equation and the fourth order one is coined by combining convef and the you kaveh y k equation the employment of the convef model allows to improve the estimation of high order derivatives and makes the proposed methods more robust to noise in addition the proposed convef based y k model is an anisotropic diffusion equation which can avoid the over smoothness of edges and provide better edge protection capability except for better denoising some experiments are conducted to demonstrate the efficiency of the proposed methods

facial age estimation based on label sensitive learning and age oriented regression this paper provides a new age estimation approach which distinguishes itself with the following three contributions first we combine distance metric learning and dimensionality reduction to better explore the connections between facial features and age labels second to exploit the intrinsic ordinal relationship among human ages and overcome the potential data imbalance problem a label sensitive concept and several imbalance treatments are introduced in the system training phase finally an age oriented local regression is presented to capture the complicated facial aging process for age determination the simulation results show that our approach achieves the lowest estimation error against existing methods

the partial adjustment valuation approach with dynamic and variable speeds of adjustment to evaluating and measuring the business value of information technology abstract in this paper we develop the partial adjustment valuation approach in which the speeds of partial adjustment are assumed to be dynamic and variable rather than fixed or constant to assessing the value of information technology it the speeds of adjustment are a function of a set of macroeconomic and or microeconomic variables observed and unobserved and hence become time varying or dynamic and variable over time the approach is illustrated by a practical application the results imply that the constant speeds of adjustment may overestimate or underestimate the actual speeds of adjustment and accordingly may miscalculate the values of performance metrics thus the partial adjustment valuation approach with dynamic and variable speeds of adjustment is more realistic and more importantly captures the changing patterns and trends of the adjustment speeds and the performance measures as well as such the partial adjustment valuation approach with constant speeds of adjustment fails to adequately explain the dynamic production process of a decision making unit the empirical evidence also conflicts with the lopsided view that the productivity paradox does not exist in developed countries

a wavelet frame based image force model for active contouring algorithms this paper proposes a directional image force dif for active contouring dif is the inner product of the zero crossing strength zcs of wavelet frame coefficients and the normal of a snake by representing strength and orientation of edges at multiple resolution levels dif markedly improves the immunity of snakes to noise and convexity

salient object detection using content sensitive hypergraph representation and partitioning as an important problem in image understanding salient object detection is essential for image classification object recognition as well as image retrieval in this paper we propose a new approach to detect salient objects from an image by using content sensitive hypergraph representation and partitioning firstly a polygonal potential region of interest p roi is extracted through analyzing the edge distribution in an image secondly the image is represented by a content sensitive hypergraph instead of using fixed features and parameters for all the images we propose a new content sensitive method for feature selection and hypergraph construction in this method the most discriminant color channel which maximizes the difference between p roi and the background is selected for each image also the number of neighbors in hyperedges is adjusted automatically according to the image content finally an incremental hypergraph partitioning is utilized to generate the candidate regions for the final salient object detection in which all the candidate regions are evaluated by p roi and the best match one will be the selected as final salient object our approach has been extensively evaluated on a large benchmark image database experimental results show that our approach can not only achieve considerable improvement in terms of commonly adopted performance measures in salient object detection but also provide more precise object boundaries which is desirable for further image processing and understanding

membrane boundary extraction using circular multiple paths membrane proteins represent over 50 of known drug targets accordingly several widely used assays in the high content analysis area rely on quantitative measures of the translocation of proteins between intracellular organelles and the cell surface in order to increase the sensitivity of these assays one needs to measure the signal specifically along the membrane requiring a precise segmentation of this compartment manual tracing of membrane boundary is very time consuming and confronts us with issues of objectivity and reproducibility in this paper we present an approach based on a circular multiple paths technique on transformed images that enables us to segment the membrane compartment accurately and rapidly we have presented three approaches for image transformation the circular property of the multiple paths ensures that we are obtaining closed contours for the membrane boundary the position of the multiple paths provides the edges of the membrane boundary the effectiveness of our algorithm is illustrated using cells expressing epitope tagged membrane proteins

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

interactive segmentation of non star shaped contours by dynamic programming in this paper we present the rack algorithm for the detection of optimal non star shaped contours in images it is based on the combination of a user driven image transformation and dynamic programming the fundamental idea is to interactively specify and edit the general shape of the desired object by using a rack this rack is used to model the image as a directed acyclic weighted graph that contains a path corresponding to the expected contour in this graph the shortest path with respect to an adequate cost function can be calculated efficiently via dynamic programming the experimental results indicate the algorithm s ability of combining an acceptable amount of user interaction with generally good segmentation results

graph laplacians and their convergence on random neighborhood graphs given a sample from a probability measure with support on a submanifold in euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold the graph laplacian of such a graph is used in several machine learning methods like semi supervised learning dimensionality reduction and clustering in this paper we determine the pointwise limit of three different graph laplacians used in the literature as the sample size increases and the neighborhood size approaches zero we show that for a uniform measure on the submanifold all graph laplacians have the same limit up to constants however in the case of a non uniform measure on the submanifold only the so called random walk graph laplacian converges to the weighted laplace beltrami operator

a reductive approach to hypergraph clustering an application to image segmentation in the last few years hypergraph based methods have gained considerable attention in the resolution of real world clustering problems since such a mode of representation can handle higher order relationships between elements compared to the standard graph theory the most popular and promising approach to hypergraph clustering arises from concepts in spectral hypergraph theory 53 and clustering is configured as a hypergraph cut problem where an appropriate objective function has to be optimized the spectral relaxation of this optimization problem allows to get a clustering that is close to the optimum but this approach generally suffers from its high computational demands especially in real world problems where the size of the data involved in their resolution becomes too large a natural way to overcome this limitation is to operate a reduction of the hypergraph where spectral clustering should be applied over a hypergraph of smaller size in this paper we introduce two novel hypergraph reduction algorithms that are able to maintain the hypergraph structure as accurate as possible these algorithms allowed us to design a new approach devoted to hypergraph clustering based on the multilevel paradigm that operates in three steps i hypergraph reduction ii initial spectral clustering of the reduced hypergraph and iii clustering refinement the accuracy of our hypergraph clustering framework has been demonstrated by extensive experiments with comparison to other hypergraph clustering algorithms and have been successfully applied to image segmentation for which an appropriate hypergraph based model have been designed the low running times displayed by our algorithm also demonstrates that the latter unlike the standard spectral clustering approach can handle datasets of considerable size

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

circular shortest paths by branch and bound shortest path algorithms are used for a large variety of optimisation problems in network and transportation analysis they are also used in image analysis for object segmentation disparity estimation path finding and crack detection sometimes the topology of the problem demands that the path be circular such circular path constraints occur in polar object segmentation disparity estimation for panoramic stereo images and in shortest paths around a cylinder in this paper we present a new efficient algorithm for circular shortest path determination on a u by v trellis in o u 1 6 v average time we impose a binary search tree on the set of path endpoints and use a best first branch and bound search technique to efficiently obtain the global minimum circular path the typical running time of our circular shortest path algorithm on a 256 times 256 image is in the order of 0 1 seconds on a 1ghz dell p3 workstation under the linux operating system applications to crack detection and object segmentation are presented

automatic ultrasound image segmentation based on local entropy and active contour model abstract it is still a challenging task to segment ultrasound medical images automatically and accurately because of the poor quality of images to address these problems a two stage automatic segmentation scheme based on local entropy and a proposed active contour model for ultrasound images are put forward in this paper first a new region based active contour model in the level set formulation driven by global and local intensity information is established for the segmentation furthermore for automatically segmenting the ultrasound images a coarse segmentation is performed utilizing local entropy information of the ultrasound images then the coarse segmentation result is used as the initial value of the explicit segmentation based on the proposed model several experiments on real ultrasound images demonstrate that the proposed method outperforms other methods on both visual perception and objective evaluation metrics

elastic model based segmentation of 3 d neuroradiological data sets this paper presents a new technique for the automatic model based segmentation of three dimensional 3 d objects from volumetric image data the development closely follows the seminal work of taylor and cootes on active shape models but is based on a hierarchical parametric object description rather than a point distribution model the segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of shape models geometric models are derived from a sample set of image data which have been segmented by experts the surfaces of these binary objects are converted into parametric surface representations which are normalized to get an invariant object centered coordinate system surface representations are expanded into series of spherical harmonics which provide parametric descriptions of object shapes it is shown that invariant object surface parametrization provides a good approximation to automatically determine object homology in terms of sets of corresponding sets of surface points gray level information near object boundaries is represented by 1 d intensity profiles normal to the surface considering automatic segmentation of brain structures as their driving application the authors choice of coordinates for object alignment was the well accepted stereotactic coordinate system major variation of object shapes around the mean shape also referred to as shape eigenmodes are calculated in shape parameter space rather than the feature space of point coordinates segmentation makes use of the object shape statistics by restricting possible elastic deformations into the range of the training shapes the mean shapes are initialized in a new data set by specifying the landmarks of the stereotactic coordinate system the model elastically deforms driven by the displacement forces across the object s surface which are generated by matching local intensity profiles elastical deformations are limited by setting bounds for the maximum variations in eigenmode space the technique has been applied to automatically segment left and right hippocampus thalamus putamen and globus pallidus from volumetric magnetic resonance scans taken from schizophrenia studies the results have been validated by comparison of automatic segmentation with the results obtained by interactive expert segmentation

global minimum for active contour models a minimal path approach a new boundary detection approach for shape modeling is presented it detects the global minimum of an active contour model s energy between two end points initialization is made easier and the curve is not trapped at a local minimum by spurious edges we modify the snake energy by including the internal regularization term in the external potential term our method is based on finding a path of minimal length in a riemannian metric we then make use of a new efficient numerical method to find this shortest path it is shown that the proposed energy though based only on a potential integrated along the curve imposes a regularization effect like snakes we explore the relation between the maximum curvature along the resulting contour and the potential generated from the image the method is capable to close contours given only one point on the objects boundary by using a topology based saddle search routine we show examples of our method applied to real aerial and medical images

an extended optimal replacement model of systems subject to shocks abstract a system is subject to shocks that arrive according to a non homogeneous poisson process as shocks occur a system has two types of failures type i failure minor failure is rectified by a minimal repair whereas type ii failure catastrophic failure is removed by replacement the probability of a type ii failure is permitted to depend on the number of shocks since the last replacement this paper proposes a generalized replacement policy where a system is replaced at the nth type i failure or first type ii failure or at age t whichever occurs first the cost of the minimal repair of the system at age t depends on the random part c t and deterministic paper c t the expected cost rate is obtained the optimal n and optimal t which would minimize the cost rate are derived and discussed various special cases are considered and detailed

learning to track the visual motion of contours abstract a development of a method for tracking visual contours is described given an untrained tracker a training motion of an object can be observed over some extended time and stored as an image sequence the image sequence is used to learn parameters in a stochastic differential equation model these are used in turn to build a tracker whose predictor imitates the motion in the training set tests show that the resulting trackers can be markedly tuned to desired curve shapes and classes of motions

detection and segmentation of generic shapes based on affine modeling of energy in eigenspace this paper presents a novel approach for detection and segmentation of man made generic shapes in cluttered images the set of shapes to be detected are members of affine transformed versions of basic geometric shapes such as rectangles circles etc the shape set is represented by its vectorial edge map transformed over a wide range of affine parameters we use vectorial boundary instead of regular boundary to improve the robustness to noise background clutter and partial occlusion our approach consists of a detection stage and a verification stage in the detection stage we first derive the energy from the principal eigenvectors of the set next an a posteriori probability map of energy distribution is computed from the projection of the edge map representation in a vectorial eigen space local peaks of the posterior probability map are located and indicate candidate detections we use energy probability based detection since we find that the underlying distribution is not gaussian and resembles a hypertoroid in the verification stage each candidate is verified using a fast search algorithm based on a novel representation in angle space and the corresponding pose information of the detected shape is obtained the angular representation used in the verification stage yields better results than a euclidean distance representation experiments are performed in various interfering distortions and robust detection and segmentation are achieved

a variational approach for the segmentation of the left ventricle in cardiac image analysis in this paper we propose a level set method to segment mr cardiac images our approach is based on a coupled propagation of two cardiac contours and integrates visual information with anatomical constraints the visual information is expressed through a gradient vector flow based boundary component and a region term that aims at best separating the cardiac contours regions according to their global intensity properties in order to deal with misleading visual support an anatomical constraint is considered that couples the propagation of the cardiac contours according to their relative distance the resulting motion equations are implemented using a level set approach and a fast and stable numerical approximation scheme the additive operator splitting encouraging experimental results are provided using real data

information theoretic active polygons for unsupervised texture segmentation curve evolution models used in image segmentation and based on image region information usually utilize simple statistics such as means and variances hence can not account for higher order nature of the textural characteristics of image regions in addition the object delineation by active contour methods results in a contour representation which still requires a substantial amount of data to be stored for subsequent multimedia applications such as visual information retrieval from databases polygonal approximations of the extracted continuous curves are required to reduce the amount of data since polygons are powerful approximators of shapes for use in later recognition stages such as shape matching and coding the key contribution of this paper is the development of a new active contour model which nicely ties the desirable polygonal representation of an object directly to the image segmentation process this model can robustly capture texture boundaries by way of higher order statistics of the data and using an information theoretic measure and with its nature of the ordinary differential equations this new variational texture segmentation model is unsupervised since no prior knowledge on the textural properties of image regions is used another contribution in this sequel is a new polygon regularizer algorithm which uses electrostatics principles this is a global regularizer and is more consistent than a local polygon regularization in preserving local features such as corners