model based bayesian feature matching with application to synthetic aperture radar target recognition abstract we present a bayesian approach for model based classification from unordered attributed feature sets a set of features is estimated from measured data and is matched with a set predicted for each candidate hypothesis using a feature model both extracted and predicted feature sets have uncertainty and some features may not be present in one set or the other computation of the match likelihoods requires a correspondence between estimated and predicted features and two bayesian correspondence methods are discussed the proposed procedure is used to predict classification performance as a function of sensor parameters for a 10 vehicle target recognition problem using x band synthetic aperture radar imagery

image sequence analysis using relational structures abstract time varying images are usually analyzed to obtain a description of the observed objects and of their actions an abstraction process is necessary which converts the input images into symbolic descriptions the attributes of image symbols and the relationship between symbols are computed from the sampled intensity or colour measurements relational structures are a formal tool to describe not only the symbolic image representation but furthermore stored models as prototypes of objects and objects as instantiations of such prototypes following this paradigm an essential task of image sequence analysis is the generation of mappings between images to establish the correspondence relationship and between images and prototypes to identify objects different types of morphisms between relational structures rs morphisms are proposed together with an approach for computing them by searching for cliques in a compatibility graph of hypothetical assignments of elements from both structures the common formalism presented here aims at a unification of methods for those steps necessary to instantiate objects and follow them through the sequence one practical reason for this attempt is to make relational database systems available to store and retrieve the large amount of data whidch might be generated during the exploration of a real world image sequence

a graduated assignment algorithm for graph matching a graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise by combining graduated nonconvexity two way assignment constraints and sparsity large improvements in accuracy and speed are achieved its low order computational complexity o lm where l and m are the number of links in the two graphs and robustness in the presence of noise offer advantages over traditional combinatorial approaches the algorithm not restricted to any special class of graph is applied to subgraph isomorphism weighted graph matching and attributed relational graph matching to illustrate the performance of the algorithm attributed relational graphs derived from objects are matched then results from twenty five thousand experiments conducted on 100 mode random graphs of varying types graphs with only zero one links weighted graphs and graphs with node attributes and multiple link types are reported no comparable results have been reported by any other graph matching algorithm before in the research literature twenty five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated

analysis of a zig zag array of circular holes in an infinite solid under uniaxial tension abstract this paper is concerned with a theoretical analysis of an infinite zig zag array of circular holes in an infinite solid under uniaxial tension we use properly defined unit regions assume complex stress potentials in the form of laurent series expansions and determine the unknown coefficients from the boundary conditions for the unit regions used numerical results for the maximum stress the stress concentration factor and the effect of perforation on the tensile stiffness of the solid are given for various hole sizes and hole spacings in both directions the results are fitted to reliable polynomial formulae for convenience in engineering applications

relaxation by hopfield network in stereo image matching abstract this paper outlines a relaxation approach using the hopfield neural network for solving the global stereovision matching problem the primitives used are edge segments the similarity smoothness and uniqueness constraints are transformed into the form of an energy function whose minimum value corresponds to the best solution of the problem we combine two methods a optimization relaxation 1 and b relaxation merit 2 with the above three constraints mapped in an energy function the main contribution is made 1 by applying a learning strategy in the similarity constraint and 2 by introducing specific conditions to overcome the violation of the smoothness constraint and to avoid the serious problem arising from the required fixation of a disparity limit so we improve the stereovision matching process a better performance of the proposed method is illustrated with a comparative analysis against a classical relaxation method

a riemannian approach to graph embedding in this paper we make use of the relationship between the laplace beltrami operator and the graph laplacian for the purposes of embedding a graph onto a riemannian manifold to embark on this study we review some of the basics of riemannian geometry and explain the relationship between the laplace beltrami operator and the graph laplacian using the properties of jacobi fields we show how to compute an edge weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths on the manifold between nodes for the particular case of a constant sectional curvature surface we use the kruskal coordinates to compute edge weights that are proportional to the geodesic distance between points we use the resulting edge weight matrix to embed the nodes of the graph onto a riemannian manifold to do this we develop a method that can be used to perform double centring on the laplacian matrix computed from the edge weights the embedding coordinates are given by the eigenvectors of the centred laplacian with the set of embedding coordinates at hand a number of graph manipulation tasks can be performed in this paper we are primarily interested in graph matching we recast the graph matching problem as that of aligning pairs of manifolds subject to a geometric transformation we show that this transformation is pro crustean in nature we illustrate the utility of the method on image matching using the coil database

local registration and deformation of a road cartographic database on a spot satellite image abstract herein we propose a new method to locally register cartographic road networks on spot satellite images this approach is based on markov random fields mrf on graphs since the cartographic and image data are obtained from different sources the noises degrading these data are of different nature cartographers also introduce in the generalization process distortions in the road map in order to emphasize some details of the road this can create important differences between the map data and the ground truth the proposed algorithm aims at correcting the error due to noise and generalization hence increasing the accuracy of the road map the first step of the method is to translate the road network into a graph where the nodes are characteristic points of the roads e g crossroads the random variable or descriptors are defined by the nodes position the edges are defined by the roads joining these points then local registration is performed by defining a model in a bayesian framework the solution is obtained by computing the maximum a posteriori map the posterior probability is assumed to be a product of two probabilities the prior of the network and the likelihood of the map each depending on the image data both are markov random field probabilities the likelihood of the registered map is the probability of a network configuration given the map data it is a measure of a global resemblance between the two we use geometrical measures euclidean distances and angles to build this probability the prior consists of two terms both depending on the image data the dependance exists through the fact that between two connected nodes we compute a best path thanks to a dynamic programing algorithm minimizing a cost function based on image gray levels curvature and gradient information the first term of the prior penalizes configurations for which different roads overlap each other and the second term depends on gray level statistics along these paths we run a simulated annealing algorithm to optimize the proposed model the tests are done on one real image data extracted from spot satellite images and artificially noisy cartographic data translated rotated or randomly deformed network we present some results showing a good global registration but also accurate correction of local distortions

contextual inference in contour based stereo correspondence standard approaches to stereo correspondence have difficulty when scene structure does not lie in or near the frontal parallel plane in part because an orientation disparity as well as a positional disparity is introduced we propose a correspondence algorithm based on differential geometry that takes explicit advantage of both disparities the algorithm relates the 2d differential structure position tangent and curvature of curves in the left and right images to the frenet approximation of the 3d space curve a compatibility function is defined via transport of the frenet frames and they are matched by relaxing this compatibility function on overlapping neighborhoods along the curve the remaining false matches are concurrently eliminated by a model of near and far neurons derived from neurobiology examples on scenes with complex 3d structures are provided

error correcting graph matching on the influence of the underlying cost function investigates the influence of the cost function on the optimal match between two graphs it is shown that for a given cost function there are an infinite number of other cost functions that lead for any given pair of graphs to the same optimal error correcting matching furthermore it is shown that well known concepts from graph theory such as graph isomorphism subgraph isomorphism and maximum common subgraph are special cases of optimal error correcting graph matching under particular cost functions

shape matching of two dimensional objects in this paper we present results in the areas of shape matching of nonoccluded and occluded two dimensional objects shape matching is viewed as a segment matching problem unlike the previous work the technique is based on a stochastic labeling procedure which explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification to reduce the computation time the technique is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels this basic technique has been extended to the situation where various objects partially occlude each other to form an apparent object and our interest is to find all the objects participating in the occlusion in such a case several hierarchical processes are executed in parallel for every object participating in the occlusion and are coordinated in such a way that the same segment of the apparent object is not matched to the segments of different actual objects these techniques have been applied to two dimensional simple closed curves represented by polygons and the power of the techniques is demonstrated by the examples taken from synthetic aerial industrial and biological images where the matching is done after using the actual segmentation methods

fuzzy cognitive maps for stereovision matching this paper outlines a method for solving the stereovision matching problem using edge segments as the primitives in stereovision matching the following constraints are commonly used epipolar similarity smoothness ordering and uniqueness we propose a new matching strategy under a fuzzy context in which such constraints are mapped the fuzzy context integrates both fuzzy clustering and fuzzy cognitive maps with such purpose a network of concepts nodes is designed each concept represents a pair of primitives to be matched each concept has associated a fuzzy value which determines the degree of the correspondence the goal is to achieve high performance in terms of correct matches the main findings of this paper are reflected in the use of the fuzzy context that allows building the network of concepts where the matching constraints are mapped initially each concept value is loaded via the fuzzy clustering and then updated by the fuzzy cognitive maps framework this updating is achieved through the influence of the remainder neighboring concepts until a good global matching solution is achieved under this fuzzy approach we gain quantitative and qualitative matching correspondences this method works as a relaxation matching approach and its performance is illustrated by comparative analysis against some existing global matching methods

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

representation and shape matching of 3 d objects a three dimensional scene analysis system for the shape matching of real world 3 d objects is presented various issues related to representation and modeling of 3 d objects are addressed a new method for the approximation of 3 d objects by a set of planar faces is discussed the major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3 d scene analysis the method is a sequential region growing algorithm it is not applied to range images but rather to a set of 3 d points the 3 d model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object applying the necessary transformations and then approximating the surface by polygons a stochastic labeling technique is used to do the shape matching of 3 d objects the technique matches the faces of an unknown view against the faces of the model it explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification it is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels the objective here is to match the individual views of the object taken from any vantage point details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting

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

matching invariant to translations rotations and scale changes abstract an optimization approach to invariant matching is proposed in this approach an object or a pattern is invariantly represented by an object centred description called an attributed relational structure ars embedding invariant properties and relations between the primitives of the pattern such as line segments and points noise effect is taken into account such that a scene can consist of noisy sub parts of a model the matching is then to find the optimal mapping between the arss of the scene and the model a gain functional is formulated to measure the goodness of fit and is to be maximized by using the relaxation labelling method experiments are shown to illustrate the matching algorithm and to demonstrate that the approach is truly invariant to arbitrary translations rotations and scale changes under noise

a new algorithm for error tolerant subgraph isomorphism detection we propose a new algorithm for error correcting subgraph isomorphism detection from a set of model graphs to an unknown input graph the algorithm is based on a compact representation of the model graphs this representation is derived from the set of model graphs in an off line preprocessing step the main advantage of the proposed representation is that common subgraphs of different model graphs are represented only once therefore at run time given an unknown input graph the computational effort of matching the common subgraphs for each model graph onto the input graph is done only once consequently the new algorithm is only sublinearly dependent on the number of model graphs furthermore the new algorithm can be combined with a future cost estimation method that greatly improves its run time performance

on the problem of linear elasticity for an infinite region containing a finite number of non intersecting spherical inhomogeneities abstract the problem of linear elasticity for an infinite number of non intersecting spherical and more generally ellipsoidal inhomogeneities is attacked the approach taken does not misrepresent geometry of inhomogeneities although the continuity conditions at the intefaces are only approximately satisfied the principal idea of the approach is to extend the method of kachanov 1985 int j fracture28 r11 r19 1987 int j solids structures23 23 43 for interacting cracks to the realm of the eshelby equivalent inclusion method the application to a test problem for two spherical cavities suggests that the approach can be useful for predictions of the overall response of composite materials and interfacial stress concentrations

line pattern retrieval using relational histograms this paper presents a new compact shape representation for retrieving line patterns from large databases the basic idea is to exploit both geometric attributes and structural information to construct a shape histogram we realize this goal by computing the n nearest neighbor graph for the lines segments for each pattern the edges of the neighborhood graphs are used to gate contributions to a two dimensional pairwise geometric histogram shapes are indexed by searching for the line pattern that maximizes the cross correlation of the normalized histogram bin contents we evaluate the new method on a database containing over 2 500 line patterns each composed of hundreds of lines

a superlinearly convergent method of feasible directions method of feasible directions mfd is one of the most important methods for solving engineering optimization problems there exist two types of mfd one has at most a linear convergence rate and the other has a superlinear convergence rate the former type includes zoutendijk s mfd topkis veinott s mfd pironneau polak s mfd cawood kostreva s norm relaxed mfd and a modified mfd the latter includes panier tits fsqp approaches however the price of the superlinearly convergent mfd for achieving superlinearity is rather high computational cost per single iteration this paper presents an algorithm based on the modified mfd the theoretical analysis shows that this algorithm is locally superlinearly convergent this algorithm has attained the same convergence rate as fsqp while it only needs to solve two subproblems instead of three subproblems in fsqp hence this algorithm should be computationally more efficient than fsqp

on the foundations of relaxation labeling processes a large class of problems can be formulated in terms of the assignment of labels to objects frequently processes are needed which reduce ambiguity and noise and select the best label among several possible choices relaxation labeling processes are just such a class of algorithms they are based on the parallel use of local constraints between labels this paper develops a theory to characterize the goal of relaxation labeling the theory is founded on a definition of con sistency in labelings extending the notion of constraint satisfaction in certain restricted circumstances an explicit functional exists that can be maximized to guide the search for consistent labelings this functional is used to derive a new relaxation labeling operator when the restrictions are not satisfied the theory relies on variational cal culus it is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality a procedure nearly identical to the relaxation operator derived under restricted circum stances serves in the more general setting further a local convergence result is established for this operator the standard relaxation labeling formulas are shown to approximate our new operator which leads us to conjecture that successful applications of the standard methods are explainable by the theory developed here observations about con vergence and generalizations to higher order compatibility relations are described

graph matching using the interference of continuous time quantum walks we consider how continuous time quantum walks can be used for graph matching we focus in detail on both exact and inexact graph matching and consider in depth the problem of measuring graph similarity we commence by constructing an auxiliary graph in which the two graphs to be matched are co joined by a layer of indicator vertices one for each potential correspondence between a pair of vertices we simulate a continuous time quantum walk in parallel on the two graphs the layer of connecting indicator vertices in the auxiliary graph allow quantum interference to take place between the two walks the interference amplitudes on the indicator vertices are determined by differences in the two walks and can be used to calculate probabilities for matches between pairs of vertices from the graphs by applying the hungarian kuhn munkres algorithm to these probabilities we recover a correspondence mapping between the graphs to calculate graph similarity we combine these probabilities with edge consistency information to give a consistency measure based on the consistency measure we define two graph similarity measures one of which requires correspondence matches while the second does not we analyse our approach experimentally using synthetic and real world graphs this reveals that our method gives results that are intermediate between the most sophisticated iterative techniques available and simpler less complex ones

elastic fields of interacting elliptic inhomogeneities abstract this paper presents a method for the calculation of two dimensional elastic fields in a solid containing any number of inhomogeneities under arbitrary far field loadings the method called pseudo dislocations method is illustrated for the solution of interacting elliptic inhomogeneities it reduces the interacting inhomogeneities problem to a set of linear algebraic equations numerical results are presented for a variety of elliptic inhomogeneity arrangements including the special cases of elliptic holes cracks and circular inhomogeneities all these complicated problems can be solved with high accuracy and efficiency

symbol recognition by error tolerant subgraph matching between region adjacency graphs we propose an error tolerant subgraph isomorphism algorithm formulated in terms of region adjacency graphs rag a set of edit operations to transform one rag into another one are defined as regions are represented by polylines and string matching techniques are used to measure their similarity the algorithm follows a branch and bound approach driven by the rag edit operations this formulation allows matching computing under distorted inputs and also reaching a solution in a near polynomial time the algorithm has been used for recognizing symbols in hand drawn diagrams

probabilistic relaxation and the hough transform abstract we discuss the recent developments in probabilistic relaxation which is used as a tool for contextual sensory data interpretation the relationship of this technique with the hough transform is then established focusing on the generalised hough transform ght we show that the label probability updating formula of the probabilistic relaxation process exploiting binary relations between object primitives under the assumption that the primitives convey weak context exhibits very close similarity to the voting function employed by a computationally efficient ght we argue that the relationship could be exploited by importing the positive features of the respective techniques to benefit one another specific suggestions for enhancing the respective techniques are mentioned they include the adoption of the representational efficiency of the hough transform to reduce the computational complexity of probabilistic relaxation vice versa in the case of the generalised hough transform it is pointed out that the effect of an unknown object transformation could be dealt with by means of a parallel cooperative interpretation rather than by means of an exhaustive search through the parameter space of a transformation group this has implications in terms of both the storage and computational requirements of the ght it also opens the possibility of using the ght for detecting objects subject to more complex transformations such as affine or perspective the relationship also suggests the possibility of using alternative voting functions which may speed up the object detection process

localizing overlapping parts by searching the interpretation tree this paper discusses how local measurements of positions and surface normals may be used to identify and locate overlapping objects the objects are modeled as polyhedra or polygons having up to six degrees of positional freedom relative to the sensors the approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on distances between faces angles between face normals and angles relative to the surface normals of vectors between sensed points the method described here is an extension of a method for recognition and localization of nonoverlapping parts previously described in 18 and 15

polynomial time metrics for attributed trees we address the problem of comparing attributed trees and propose four novel distance measures centered around the notion of a maximal similarity common subtree the proposed measures are general and defined on trees endowed with either symbolic or continuous valued attributes and can be applied to rooted as well as unrooted trees we prove that our measures satisfy the metric constraints and provide a polynomial time algorithm to compute them this is a remarkable and attractive property since the computation of traditional edit distance based metrics is in general np complete at least in the unordered case we experimentally validate the usefulness of our metrics on shape matching tasks and compare them with an approximation of edit distance

edge labeling using dictionary based relaxation an improved application of probabilistic relaxation to edge labeling is presented the improvement derives from the use of a representation of the edge process that is internally consistent and which utilizes a more complex description of edge structure the application uses a dictionary to represent permitted labelings of the entire context conveying neighborhood of each pixel details are given of the dictionary approach and the related representation of the edge process a comparison with other edge postprocessing strategies is provided

a special boundary element formulation for multiple circular hole problems in an infinite plate abstract a special boundary element formulation is proposed to solve the multiple circular hole problems in an infinite plate the main ideas of the proposed formulation are followed from two directions the first formulation in which all the unknowns are taken from the boundary tractions is like the boundary element method in the second all the derivations of the solution are derived from the complex variable method of plane elasticity using the principle of superposition a system of linear algebraic equations for the unknowns is established finally several numerical examples are given

indexing hierarchical structures using graph spectra hierarchical image structures are abundant in computer vision and have been used to encode part structure scale spaces and a variety of multiresolution features in this paper we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph dag into a low dimensional vector space based on a novel spectral characterization of a dag this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest neighbor search we establish the insensitivity of the signature to minor perturbation of graph structure due to noise occlusion or node split merge to accommodate large scale occlusion the dag rooted at each nonleaf node of the query votes for model objects that share that part effectively accumulating local evidence in a model dag s topological subspaces we demonstrate the approach with a series of indexing experiments in the domain of view based 3d object recognition using shock graphs

on deformable models for visual pattern recognition this paper reviews model based methods for non rigid shape recognition these methods model match and classify non rigid shapes which are generally problematic for conventational algorithms using rigid models issues including model representation optimization criteria formulation model matching and classification are examined in detail with the objective to provide interested researchers a roadmap for exploring the field this paper emphasizes on 2d deformable models their potential applications and future research directions particularly on deformable pattern classification are discussed

automatic learning of cost functions for graph edit distance abstract graph matching and graph edit distance have become important tools in structural pattern recognition the graph edit distance concept allows us to measure the structural similarity of attributed graphs in an error tolerant way the key idea is to model graph variations by structural distortion operations as one of its main constraints however the edit distance requires the adequate definition of edit cost functions which eventually determine which graphs are considered similar in the past these cost functions were usually defined in a manual fashion which is highly prone to errors the present paper proposes a method to automatically learn cost functions from a labeled sample set of graphs to this end we formulate the graph edit process in a stochastic context and perform a maximum likelihood parameter estimation of the distribution of edit operations the underlying distortion model is learned using an expectation maximization algorithm from this model we finally derive the desired cost functions in a series of experiments we demonstrate the learning effect of the proposed method and provide a performance comparison to other models

approximating the problem not the solution an alternative view of point set matching this work discusses the issue of approximation in point set matching in general one may have two classes of approximations when tackling a matching problem 1 an algorithmic approximation which consists in using suboptimal procedures to infer the assignment and 2 a representational approximation which involves a simplified and suboptimal model for the original data matching techniques have typically relied on the first approach by retaining the complete model and using suboptimal techniques to solve it in this paper we show how a technique based on using exact inference in simple graphical models an instance of the second class can significantly outperform instances of techniques from the first class we experimentally compare this method with well known spectral and relaxation methods which are exemplars of the first class we have performed experiments with synthetic and real world data sets which reveal significant performance improvement in a wide operating range

object recognition and articulated object learning by accumulative hopfield matching in this paper a novel object recognition method based on attributed relational graph matching is proposed which is called accumulative hopfield matching we first divide the scene graph into many sub graphs and a modified hopfield network is then constructed to obtain the sub graph isomorphism between each sub scene graph and model graph the final result is deduced by accumulating the solutions of all small sub networks comparing to the traditional hopfield network the proposed system has the advantage of finding homomorphic mappings between two graphs furthermore the system can be applied for articulated object recognition and visual model learning which is considered as a difficult topic till now the proposed method has been evaluated with real images

multiple void crack interaction abstract this paper presents a method for fracture analysis of a general two dimensional system containing multiple holes or voids and cracks the superposition technique is used to break the problem into a number of single hole and single crack problems each hole problem is modeled using the method of pseudo tractions and each crack problem is modeled by a distribution of dislocations an integral equation approach is developed based on two types of fundamental solutions one due to point loads in a solid with a hole and the other due to point dislocations in an infinite elastic body the resulting integral equations present cauchy type singularities only on the crack part of the multiple hole crack problem the results in terms of stress intensity factors sifs are presented for a variety of hole and crack arrangements relative sizes of cracks and holes spacings and crack orientations the amplification and retardation effects on sifs are investigated it is found that the hole crack arrangements have significant effects on the nature of the amplification or retardation in the fractured porous elastic medium modeled as a crack surrounded by holes amplification or retardation can occur depending on the relative size of the holes and cracks and the spacing between them very strong retardation exists as the spacing becomes small some optimal retardations void toughening are achieved by adjusting the geometry parameters an array of periodical crack hole structure is examined as a numerical example

structural matching in computer vision using probabilistic relaxation in this paper we develop the theory of probabilistic relaxation for matching features extracted from 2d images derive as limiting cases the various heuristic formulae used by researchers in matching problems and state the conditions under which they apply we successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair for this purpose each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations

analysis of stress concentrations in plates with rectangular openings by a combined conformal mapping finite element approach plates with rectangular openings develop stress concentrations under bending while these stresses can be determined using finite elements in many problems this would be difficult because a high density mesh would be needed in the neighborhood of every opening corner in this paper it is shown how a complex variable conformal mapping approach can be numerically coupled with the finite element method to analyze these corner stresses this approach can be used even with relatively coarse meshes where the finite element results by themselves do not resolve the stress concentrations in essence the method relies on finite element analysis to obtain information on the stress field in a region surrounding the plate opening this information is subsequently used to set up the parameters of the conformal mapping approach to obtain the near field stresses at the opening corners

improving consistency and reducing ambiguity in stochastic labeling an optimization approach we approach the problem of labeling a set of objects from a quantitative standpoint we define a world model in terms of transition probabilities and propose a definition of a class of global criteria that combine both ambiguity and consistency a projected gradient algorithm is developed to minimize the criterion we show that the minimization procedure can be implemented in a highly parallel manner results are shown on several examples and comparisons are made with relaxation labeling techniques

parallel and deterministic algorithms from mrfs surface reconstruction deterministic approximations to markov random field mrf models are derived one of the models is shown to give in a natural way the graduated nonconvexity gnc algorithm proposed by a blake and a zisserman 1987 this model can be applied to smooth a field preserving its discontinuities a class of more complex models is then proposed in order to deal with a variety of vision problems all the theoretical results are obtained in the framework of statistical mechanics and mean field techniques a parallel iterative algorithm to solve the deterministic equations of the two models is presented together with some experiments on synthetic and real images

geometric graph comparison from an alignment viewpoint in this paper we propose a new approach for the comparison and retrieval of geometric graphs formulated from an alignment perspective the algorithm presented here is quite general in nature and applies to geometric graphs of any dimension the method involves two major steps firstly graph alignment is effected making use of an optimisation approach whose target function arises from a diffusion process over the graphs under study this provides from the theoretical viewpoint a link between stochastic processes on graphs and the heat kernel the second step involves using a probabilistic approach to recover the transformation parameters that map the graph vertices to one another so as to permit the computation of a similarity measure based on the goodness of fit between the two graphs under study here we view the transformation parameters as random variables and aim at minimising the kullback liebler divergence between the two graphical structures under study we provide a sensitivity analysis on synthetic data and illustrate the utility of the method for purposes of comparison and retrieval of cad objects and binary shape categorisation we also compare our results to those yielded by alternatives elsewhere in the literature

bayesian grid matching a method for locating distorted grid structures in images is presented the method is based on the theories of template matching and bayesian image restoration the grid is modeled as a deformable template prior knowledge of the grid is described through a markov random field mrf model which represents the spatial coordinates of the grid nodes knowledge of how grid nodes are depicted in the observed image is described through the observation model the prior consists of a node prior and an arc edge prior both modeled as gaussian mrfs the node prior models variations in the positions of grid nodes and the arc prior models variations in row and column spacing across the grid grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid the method can be applied to noisy images with missing grid nodes and grid node artifacts and the method accommodates a wide range of grid distortions including large scale warping varying row column spacing as well as nonrigid random fluctuations of the grid nodes the methodology is demonstrated in two case studies concerning 1 localization of dna signals in hybridization filters and 2 localization of knit units in textile samples

graph matching based on spectral embedding with missing value this paper proposes an efficient algorithm for inexact graph matching based on spectral embedding with missing value we commence by building an association graph model based on initial matching algorithm then by dot product representation of graph with missing value a new embedding method co embedding where the correspondences between unmatched nodes are treated as missing data in an association graph is presented at last a new graph matching algorithm which alternates between the co embedding and point pattern matching is proposed convictive experimental results on both synthetic and real world data demonstrate the effectiveness of the proposed graph matching algorithm

on convex relaxation of graph isomorphism graphs are a ubiquitous mathematical abstraction used in numerous problems in science and engineering of particular importance is the need to find the best structure preserving matching of graphs since graph matching gm is a computationally intractable problem numerous heuristics exist to approximate its solution an important class of gm heuristics is relaxation techniques based on replacing the original problem by a continuous convex program conditions for applicability or inapplicability of such convex relaxations are poorly understood in this study we propose easy to check spectral properties to characterize a wide family of graphs for which we prove equivalence of convex relaxation to the exact gm

structural indexing efficient 3 d object recognition the authors present an approach for the recognition of multiple 3 d object models from three 3 d scene data the approach uses two different types of primitives for matching small surface patches where differential properties can be reliably computed and lines corresponding to depth or orientation discontinuities these are represented by splashes and 3 d curves respectively it is shown how both of these primitives can be encoded by a set of super segments consisting of connected linear segments these super segments are entered into a table and provide the essential mechanism for fast retrieval and matching the issues of robustness and stability of the features are addressed in detail the acquisition of the 3 d models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3 d curves the authors present results with the current system 3 d object recognition based on super segments and discuss further extensions

new measurements and corner guidance for curve matching with probabilistic relaxation reliable curve matching is a difficult yet important problem in many vision based applications including image based modeling we describe in this paper two aspects of our research in this area a new algorithm for curve matching including lines within a probabilistic relaxation framework and an approach of incorporating previously matched points corners to guide curve matching we propose similarity invariant unary and binary measurements suitable for curves and introduce an additional measurement to model the uncertainty of the binary measurements the uncertainty measure is proven to be very important in computing the matching support from neighboring matches we also show how to use a set of previously matched points corners to guide the curve matching the role of the corner guidance is explicitly modeled by a set of unary measurements and a similarity function under the same relaxation framework preprocessing techniques contributing to the success of our curve matching techniques are also developed and discussed experiments with complex real scenes show that the rate of correct matching is higher than 98

a hybrid micro macro bem formulation for micro crack clusters in elastic components local analysis schemes capable of detailed representations of the micro features of a problem are integrated with a macro scale bem technique capable of handling complex finite geometries and realistic boundary conditions the micro scale effects are introduced into the macro scale bem analysis through an augmented fundamental solution obtained from an integral equation representation of the micro scale features the proposed hybrid micro macro bem formulation allows decomposition of the complete problem into two sub problems one residing entirely at the micro level and the other at the macro level this allows for investigations of the effects of the micro structural attributes while retaining the macro scale geometric features and actual boundary conditions for the component or structure under consideration as a first attempt elastic fracture mechanics problems with interacting cracks at close spacings are considered the numerical results obtained from the hybrid bem analysis establish the accuracy and effectiveness of the proposed micro macro computational scheme for this class of problems the proposed micro macro bem formulation can easily be extended to investigate the effects of other micro features e g interfaces short or continuous fibre reinforcements voids and inclusions in the context of linear elasticity on macroscopic failure modes observed in structural components

object matching using a locally affine invariant and linear programming techniques in this paper we introduce a new matching method based on a novel locally affine invariant geometric constraint and linear programming techniques to model and solve the matching problem in a linear programming formulation all geometric constraints should be able to be exactly or approximately reformulated into a linear form this is a major difficulty for this kind of matching algorithm we propose a novel locally affine invariant constraint which can be exactly linearized and requires a lot fewer auxiliary variables than other linear programming based methods do the key idea behind it is that each point in the template point set can be exactly represented by an affine combination of its neighboring points whose weights can be solved easily by least squares errors of reconstructing each matched point using such weights are used to penalize the disagreement of geometric relationships between the template points and the matched points the resulting overall objective function can be solved efficiently by linear programming techniques our experimental results on both rigid and nonrigid object matching show the effectiveness of the proposed algorithm

spectral embedding of graphs in this paper we explore how to embed symbolic relational graphs with unweighted edges in a pattern space we adopt a graph spectral approach we use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix for each eigenmode we compute vectors of spectral properties these include the eigenmode perimeter eigenmode volume cheeger number inter mode adjacency matrices and intermode edge distance we embed these vectors in a pattern space using two contrasting approaches the first of these involves performing principal or independent components analysis on the covariance matrix for the spectral pattern vectors the second approach involves performing multidimensional scaling on the l2 norm for pairs of pattern vectors we illustrate the utility of the embedding methods on neighbourhood graphs representing the arrangement of corner features in 2d images of 3d polyhedral objects two problems are investigated the first of these is the clustering of graphs representing distinct objects viewed from different directions the second is the identification of characteristic views of single objects these two studies reveal that both embedding methods result in well structured view spaces for graph data extracted from 2d views of 3d objects

edit distance based kernel functions for structural pattern classification a common approach in structural pattern classification is to define a dissimilarity measure on patterns and apply a distance based nearest neighbor classifier in this paper we introduce an alternative method for classification using kernel functions based on edit distance the proposed approach is applicable to both string and graph representations of patterns by means of the kernel functions introduced in this paper string and graph classification can be performed in an implicit vector space using powerful statistical algorithms the validity of the kernel method cannot be established for edit distance in general however by evaluating theoretical criteria we show that the kernel functions are nevertheless suitable for classification and experiments on various string and graph datasets clearly demonstrate that nearest neighbor classifiers can be outperformed by support vector machines using the proposed kernel functions