pattern recognition by graph matching using the potts mft neural networks this paper is concerned with programming of the potts mean field theory neural networks for pattern recognition by homomorphic mapping of the attributed relational graphs arg in order to generate the homomorphic mapping from the scene relational graph to the model graph we make use of the recently introduced suganthan technical report nanyang technical university 1994 compatibility functions in relation to the hopfield network an efficient pose clustering algorithm is used to separate and localize different occurrences of any particular object model in the scene the pose clustering algorithm also eliminates spurious hypotheses generated by the network and resolves ambiguities in the final interpretation the performance of the proposed approach to pattern recognition by homomorphism is demonstrated using a number of line patterns silhouette images and circle patterns

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

krawtchouk polynomials and universal bounds for codes and designs in hamming spaces universal bounds for the cardinality of codes in the hamming space f sub r sup n with a given minimum distance d and or dual distance d are stated a self contained proof of optimality of these bounds in the framework of the linear programming method is given the necessary and sufficient conditions for attainability of the bounds are found the parameters of codes satisfying these conditions are presented in a table a new upper bound for the minimum distance of self dual codes and a new lower bound for the crosscorrelation of half linear codes are obtained

recognition of handprinted hebrew characters using features selected in the hough transform space abstract automatic identification of handprinted hebrew characters is described in this paper the recognition model devised constitutes a multi stage system in the first stage a coarse classifier allocates the input patterns into one of 17 categories based on the number and the location of end points within predetermined regions in the characters matrix the second stage uses features extracted in the hough transform space to classify characters assigned to each of 16 categories the remaining one category composed of similar square like rotated l shape classes is recognized by structural analysis and a statistical classifier an additional step of postprocessing is added to compensate for the sensitivity of the hough transform to the existence of similar classes within some of the categories experiments were conducted with a multi author 40 writers data base an average recognition rate of 86 9 was observed for the system this compared favorably with the results of two other recognition methods

linear programming bounds for doubly even self dual codes using a variant of the linear programming method we derive a new upper bound on the minimum distance d of doubly even self dual codes of length n asymptotically for n growing it gives d n spl les 0 166315 spl middot spl middot spl middot o 1 thus improving on the mallows odlyzko sloane bound of 1 6 to establish this we prove that in any doubly even self dual code the distance distribution is asymptotically upper bounded by the corresponding normalized binomial distribution in a certain interval

pattern recognition by affine moment invariants abstract the paper deals with moment invariants which are invariant under general affine transformation and may be used for recognition of affine deformed objects our approach is based on the theory of algebraic invariants the invariants from second and third order moments are derived and shown to be complete the paper is a significant extension and generalization of recent works several numerical experiments dealing with pattern recognition by means of the affine moment invariants as the features are described

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

a bound for divisible codes a divisible code is a linear code whose word weights have a common divisor larger than one if the divisor is a power of the field characteristic there is a simple bound on the dimension of the code in terms of its weight range when this bound is applied to the subcode of words with weight divisible by four in a type i binary self dual code it yields an asymptotic improvement of the conway sloane bound for self dual codes

review of shape representation and description techniques more and more images have been generated in digital form around the world there is a growing interest in 1nding images in large collections or from remote databases in order to 1nd an image the image has to be described or represented by certain features shape is an important visual feature of an image searching for images using shape features has attracted much attention there are many shape representation anddescription techniques in the literature in this paper we classify and review these important techniques we examine implementation procedures for each technique and discuss its advantages and disadvantages some recent research results are also included and discussed in this paper finally we identify some promising techniques for image retrieval according to standard principles

new asymptotic bounds for self dual codes and lattices we give an independent proof of the krasikov litsyn bound d n spl lsim 1 5 sup 1 4 2 on doubly even self dual binary codes the technique used a refinement of the mallows odlyzko sloane approach extends easily to other families of self dual codes modular lattices and quantum codes in particular we show that the krasikov litsyn bound applies to singly even binary codes and obtain an analogous bound for unimodular lattices we also show that in each case our bound differs from the true optimum by an amount growing faster than o spl radic n

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

simulated annealing with an optimal fixed temperature contrary to conventional belief it turns out that in some problem instances of moderate size fixed temperature simulated annealing algorithms based on a heuristic formula for determining the optimal temperature can be superior to algorithms based on cooling such a heuristic formula however often seems elusive in practical cases considered we include instances of traveling salesman quadratic assignment and graph partitioning problems where we obtain results that compare favorably to the ones known in the literature

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 branch and cut algorithm for the resolution of large scale symmetric traveling salesman problems an algorithm is described for solving large scale instances of the symmetric traveling salesman problem stsp to optimality the core of the algorithm is a polyhedral cutting plane procedure that exploits a subset of the system of linear inequalities defining the convex hull of the incidence vectors of the hamiltonian cycles of a complete graph the cuts are generated by several identification procedures that have been described in a companion paper whenever the cutting plane procedure does not terminate with an optimal solution the algorithm uses a tree search strategy that as opposed to branch and bound keeps on producing cuts after branching the algorithm has been implemented in fortran two different linear programming lp packages have been used as the lp solver the implementation of the algorithm and the interface with one of the lp solvers is described in sufficient detail to permit the replication of our experiments computational results are reported with up to 42 stsps with sizes rangin

simulated annealing searching for an optimal temperature schedule a sizable part of the theoretical literature on simulated annealing deals with a property called convergence which asserts that the simulated annealing chain is in the set of global minimum states of the objective function with probability tending to 1 however in practice the convergent algorithms are considered too slow whereas a number of nonconvergent ones are usually preferred we attempt a detailed analysis of various temperature schedules examples will be given of when it is both practically and theoretically justified to use boiling fixed temperature or even fast cooling schedules which have a small probability of reaching global minima applications to traveling salesman problems of various sizes are also given

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

an improved upper bound on the minimum distance of doubly even self dual codes we derive a new upper bound on the minimum distance d of doubly even self dual codes of length n asymptotically for n growing it gives lim sub n spl rarr spl infin sup d n spl les 5 5 sup 3 4 10 0 165630 thus improving on the mallows odlyzko sloane bound of 1 6 and our recent bound of 0 166315

an improved annealing scheme for the qap abstract recently there has been some interest in the use of simulated annealing to obtain good solutions to a number of combinatorial problems this paper reports on the use of this method applied to the quadratic assignment problem i e the assignment of inter communicating objects to locations to minimize the total cost of communication between them the result is a much improved annealing scheme for this problem which performs well on a range of examples finding improved solutions for several of the largest problems available in the literature and requiring only modest amounts of computational effort

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

solution of large scale symmetric travelling salesman problems in this paper we report on a cutting plane procedure with which we solved symmetric travelling salesman problems of up to 1000 cities to optimality our implementation is based on a fast lp solver ibm s mpsx and makes effective use of polyhedral results on the symmetric travelling salesman polytope we describe the important ingredients of our code and give an extensive documentation of its computational performance

two dimensional half rate codes using two dimensional finite field filter banks this correspondence introduces two dimensional 2 d filter bank codes tdfbcs the synthesis bank of a two channel two dimensional filter bank over a finite field is used to design the 2 d code and the corresponding analysis bank is used to generate the syndrome of the code first we study the encoder of half rate tdfbcs and show that these linear codes are lattice cyclic it is proven that any 2 d lattice cyclic code can also be generated by a 2 d filter bank second we introduce a methodology to design tdfbcs over binary erasure channels these codes have a simple and efficient maximum likelihood ml decoding algorithm for burst erasures we show that half rate tdfbcs of dimensions n1 x n2 can recover burst erasures of size up to n1 x n2 2 and n1 2 x n2 using the proposed decoding technique finally we present examples of tdfbcs that satisfy the reiger bound with equality i e they are capable of correcting any burst of size n1 x n2 2

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

on the reconstructive matching of multidimensional objects to determine a known object position and orientation within a scene is a task of fundamental importance a scheme called reconstructive matching rm which is inherently invariant to shift rotation and optionally to scale changes is introduced rm decomposes the matching within the source space into a set of mutually independent processes of dimensionality reduced by one applied to computerized tomography ct the approach allows ct object matching prior to their reconstruction

shadow bounds for self dual codes conway and sloane 1990 have previously given an upper bound on the minimum distance of a singly even self dual binary code using the concept of the shadow of a self dual code we improve their bound finding that the minimum distance of a self dual binary code of length n is at most 4 n 24 4 except when n mod 24 22 when the bound is 4 n 24 6 we also show that a code of length a multiple of 24 meeting the bound cannot be singly even the same technique gives similar results for additive codes over gf 4 relevant to quantum coding theory

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

investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography abstract the simulated annealing sa algorithm is a well established optimization technique which has found applications in many research areas however the sa algorithm is limited in its application due to the high computational cost and the difficulties in determining the annealing schedule this paper demonstrates that the temperature parallel simulated annealing tpsa algorithm a parallel implementation of the sa algorithm shows great promise to overcome these limitations when applied to continuous functions the tpsa algorithm greatly reduces the computational time due to its parallel nature and avoids the determination of the annealing schedule by fixing the temperatures during the annealing process the main contributions of this paper are threefold first this paper explains a simple and effective way to determine the temperatures by applying the concept of critical temperature t c second this paper presents systematic tests of the tpsa algorithm on various continuous functions demonstrating comparable performance as well established sequential sa algorithms third this paper demonstrates the application of the tpsa algorithm on a difficult practical inverse problem namely the hyperspectral tomography problem the results and conclusions presented in this work provide are expected to be useful for the further development and expanded applications of the tpsa algorithm

a quantitative analysis of the simulated annealing algorithm a case study for the traveling salesman problem a quantitative study is presented of the typical behavior of the simulated annealing algorithm based on a cooling schedule presented previously by the authors the study is based on the analysis of numerical results obtained by systematically applying the algorithm to a 100 city traveling salesman problem the expectation and the variance of the cost are analyzed as a function of the control parameter of the cooling schedule a semiempirical average case performance analysis is presented from which estimates are obtained on the expectation of the average final result obtained by the simulated annealing algorithm as a function of the distance parameter which determines the decrement of the control parameter

partial shape classification using contour matching in distance transformation an algorithm is presented to recognize and locate partially distorted 2d shapes without regard to their orientation location and size the algorithm first calculates the curvature function from the digitized image of an object the points of local maxima and minima extracted from the smooth curvature are used as control points to segment the boundary and to guide the boundary matching procedure the boundary matching procedure considers two shapes at a time one shape from the template databank and the other from the object being classified the procedure tries to match the control points in the unknown shape to those of a shape from the template databank and estimates the translation rotation and scaling factors to be used to normalize the boundary of the unknown shape the chamfer 3 4 distance transformation and a partial distance measurement scheme constitute the final step in measuring the similarity between the two shapes the unknown shape is assigned to the class corresponding to the minimum distance the algorithm has been successfully tested on partial shapes using two sets of data one with sharp corners and the other with curve segments this algorithm not only is computationally simple but also works reasonably well in the presence of a moderate amount of noise

estimates for the range of binomiality in codes spectra we derive new estimates for the range of binomiality in a code s spectra where the distance distribution of a code is upperbounded by the corresponding normalized binomial distribution the estimates depend on the code s dual distance

a new upper bound on the minimal distance of self dual codes it is shown that the minimal distance d of a binary self dual code of length n or 74 is at most 2 n 6 10 this bound is a consequence of some new conditions on the weight enumerator of a self dual code obtained by considering a particular translate of the code called its shadow these conditions also enable one to find the highest possible minimal distance of a self dual code for all n or 60 to show that self dual codes with d or 22 with d or 8 exist precisely for n 24 32 and n or 26 and with d or 10 exist precisely for n or 46 and to show that there are exactly eight self dual codes of length 32 with d 8 several of the self dual codes of length 34 have trivial group this appears to be the smallest length where this can happen